Filter by learning principle, evidence level, type and audience. Evidence levels describe how much a source lets you trust a claim about learners, based on study design, who took part and what was measured. They are not endorsements, and they are not official WWC, ESSA or GRADE ratings.
Technical testProvisional (rules only)
2026-10-16 · arXiv new: cs.CY, cs.HC, cs.CL, cs.AI · Study (preprint)
Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of answering student questions. With the rise in enrollment of international students in computing programs, researchers have begun to explore the difficulties that...
Why this label for Multi-sourced AI Tutor for Non-Native English Speaker Students
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
- Tutoring and personalization: matched 'Tutor'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryProvisional (rules only)
2026-10-16 · arXiv new: cs.CY, cs.HC, cs.CL, cs.AI · Study (preprint)
Critique in design education depends on interpreting work in progress, articulating intentions and translating feedback into revisions. This technical report presents Critsly, an artefact-aware AI critique workspace, and StudioCrit, its architecture-studio research mode.
Why this label for Critsly and StudioCrit: An Artefact-Aware AI Critique Workspace and Simulation-B
- Commentary or coverage: provisional rules-only grade, pending grader consensus and review: R3 genre gate (F7): Self-described conceptual, legal, policy or reflective work with no learner data.
- Preprint: not peer reviewed; capped at Level 3
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-16 · Frontiers in Education · Study
Large language models (LLMs) are increasingly used to support evaluative tasks, raising questions about whether their judgements remain independent when users provide prior evaluative information. This study examined the context sensitivity of GPT-5.5 Instant-generated academic peer-review evaluations under positive...
Why this label for Context sensitivity in ChatGPT - generated peer review: evaluative alignment acr
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryProvisional (rules only)
2026-10-15 · Frontiers in Education · Study
Artificial Intelligence (AI) is increasingly part of higher education's institutional infrastructure, extending beyond teaching and learning to governance, administration, and student support. This study examines how AI Governance Readiness (AIGR) influences Administrative Efficiency through AI (AEAI), AI Fluency...
Policy and governance
Why this label for Artificial intelligence as academic infrastructure: governance, administrative i
- Level 1: Descriptive: provisional rules-only grade, pending grader consensus and review: R5 design: descriptive (F3): Descriptive, qualitative, pilot or deployment study with real participants.
- Policy and governance: matched 'governance'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-15 · eSchool News · News
No matter where you turn today, it feels as though people just can’t stop talking about artificial intelligence (AI). Suddenly it’s become a part of our workspace, our entertainment, the tools we use for shopping and paying bills, but perhaps most notably, it’s quickly found its way into our classrooms.
Why this label for Vibe coding: 3 easy steps to classroom magic
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-15 · Higher Ed Dive · News
At the National Association for College Admission Counseling conference, discussions ranged from AI in application essays to international recruitment.
Why this label for How admissions officials are navigating the academic year ahead
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learners
2026-10-15 · arXiv (back catalog) · Study (preprint)
Adaptive assessment systems personalize learning by estimating learner proficiency and recommending suitable questions. However, many existing approaches rely on fixed difficulty labels, static rating updates, or black-box prediction models that provide limited control over rating stability, interpretability, and...
Why this label for MARS: A Momentum-Aware Adaptive Rating System for Personalized Assessment
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: evaluation measures prediction and routing performance on logged and simulated data, not learner outcomes.
- Grader B: R4 real-learner gate: evaluated on a log dataset (ASSISTments) and a simulated exam (MARS-SimExam); the only outcomes are prediction, rating stability and routing scores, so it measures the model
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testProvisional (rules only)
2026-10-14 · arXiv search: AI + education · Study (preprint)
Phonetic error detection (PED) aims to identify phoneme-level substitutions, deletions, and insertions and is essential for educational and clinical speech assessment and intervention. Despite recent advances in speech models for phoneme recognition, their ability to reliably detect fine-grained phonetic errors...
Why this label for PEDBench: Can Speech Models Detect Phonetic Errors?
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-14 · arXiv search: AI + education · Study (preprint)
Law students are examined through open-text answers: case solutions, essays and memos that apply the law to a set of facts. Such answers are expensive to grade, human graders disagree widely on them, and large language models (LLMs) now offer to grade them at scale, both in teaching and in the benchmarks that test...
Assessment and grading
Why this label for SoK: Grading Open-Text Legal Answers, from Law School Exams to Trustworthy Rubri
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
- Assessment and grading: matched 'Grading'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-14 · EdSurge · News
How Learning Commons’ new open platform could help edtech companies and schools implement technology.
Why this label for Who Is Building the AI Foundation for K-12 Classrooms?
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-13 · arXiv search: AI + education · Study (preprint)
Code generation models are actively being used in professional and educational settings. However, the competence of large language models (LLMs) to generate algorithmically diverse code remains largely unknown.
Why this label for DivAlgo: A systematic evaluation of algorithmic diversity for code generation mo
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryProvisional (rules only)
2026-10-13 · arXiv search: AI + education · Study (preprint)
Human-AI Interaction is commonly evaluated by what an artificial agent enables during use, yet interaction can also alter the human who acts next. We introduce Residual Human States, a trajectory-based framework for representing the potentially multidimensional human state carried forward after interaction with AI,...
Why this label for What Human-AI Interaction Leaves Behind: Residual Human States - A Trajectory-Ba
- Level 1: Descriptive: provisional rules-only grade, pending grader consensus and review: R5 design: descriptive (F3): Descriptive, qualitative, pilot or deployment study with real participants.
- Preprint: not peer reviewed; capped at Level 3
- Tutoring and personalization: matched 'tutoring'
- Assessment and grading: matched 'exam'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-13 · arXiv search: AI + education · Study (preprint)
The rapid advancement of image-to-text extraction systems and large language models (LLMs) poses new challenges for educational assessment by making assignment materials easy to transcribe and solve with minimal human effort. To address this problem, we present AIRRI (Artificial Intelligence Resilient Reading...
Why this label for AIRRI: Towards a Human-Readable, Machine-Resistant Reading Interface
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-13 · arXiv search: AI + education · Study (preprint)
Large language models (LLMs) promise to automate one of higher education's most resource-intensive activities: assessing student writing. Existing evaluations are typically limited to a single model, a single prompt and a single institution.
Why this label for Machine-human agreement in marking undergraduate assignments is specific to mode
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
- Reading and writing: matched 'student writing'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-13 · The 74 · News
Updated October 14, 2026 Over the past few weeks, the nation saw something that doesn’t happen very often: Rival tech leaders agreed that artificial intelligence development needs to slow down. They are talking about AI’s potential to harm humankind, but education technology developers need to think this way about...
Why this label for Opinion: AI-Powered Instruction Needs Precision. Right Now, It’s Not Even Close
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-13 · The Hechinger Report · News
Evan Perez could be the face of a new movement. He’s a first-year student attending Babson College on a full scholarship, majoring in business with a concentration in entrepreneurship.
Why this label for COLUMN: As careers, college and the workforce change rapidly, should high school
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-13 · Higher Ed Dive · News
University leaders discussed application essay strategies in the AI era at the National Association for College Admission Counseling's annual conference.
Why this label for 3 admissions experts on why they require - or nixed - supplemental essays
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-10-13 · arXiv (back catalog) · Study (preprint)
Compute cost in automated essay scoring (AES) is usually treated as a technical efficiency issue rather than a measurement design concern. Yet deployment decisions require more than reporting latency, model size, or inference expense.
Assessment and gradingPolicy and governance
Why this label for From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for
- Commentary: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R3 genre gate: proposes a measurement design framework without an empirical evaluation.
- Grader B: R3 genre gate: conceptual cost-measurement framework for automated essay scoring with no empirical study
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testProvisional (rules only)
2026-10-12 · arXiv search: AI + education · Study (preprint)
Large language models can answer the same question in different styles, but users' preferred styles are hidden and vary across users and questions. We study inference-time personalization through online selection from a fixed set of prompts, each representing a persona.
Why this label for Online LLM Personalization from Pairwise Preferences Across Users
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
- Tutoring and personalization: matched 'tutoring'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-11 · arXiv search: AI + education · Study (preprint)
AI-text detectors are already applied to Kazakh writing, yet no study reports how often publicly available detectors flag Kazakh text whose human authorship is dated before ChatGPT. We present AINA-KK: 3,811 human passages from encyclopedic, news, web and educational pages that provably existed before ChatGPT, and...
Academic integrity
Why this label for Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
- Academic integrity: matched 'Text Detectors'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryProvisional (rules only)
2026-10-11 · arXiv search: AI + education · Study (preprint)
Large language models can produce fluent tutoring dialogue, but whether they actually help students learn remains unclear. We present a process-centered synthesis of LLM tutoring that follows the path from learner-state inference to pedagogical action, tutor training, evaluation, and student simulation.
Why this label for Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop
- Commentary or coverage: provisional rules-only grade, pending grader consensus and review: R3 genre gate (F7): Self-described conceptual, legal, policy or reflective work with no learner data.
- Preprint: not peer reviewed; capped at Level 3
- Tutoring and personalization: matched 'Tutors'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryProvisional (rules only)
2026-10-09 · arXiv search: AI + education · Study (preprint)
Generative AI is increasingly studied as a tool for supporting lesson authoring; the creation and adaptation of classroom materials, yet little is known about how it should fit into secondary computing teachers' subject-specific practices. We conducted two complementary qualitative studies with eight secondary...
Why this label for From Instructional Intent to Classroom-Ready Lessons: Investigating Teacher-AI C
- Level 1: Descriptive: provisional rules-only grade, pending grader consensus and review: R5 design: descriptive (F3): Descriptive, qualitative, pilot or deployment study with real participants.
- Preprint: not peer reviewed; capped at Level 3
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
2026-10-09 · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education by Maria Ranieri, Gabriele Biagini. Available online 9 October 2026. Abstract on the publisher page.
Why this label for Artificial Intelligence in Higher Education (2019 - 2026): A Systematic Review o
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-09 · EdTech Digest · News
Bans reward outdated skills. Grade judgment instead.
Policy and governance
Why this label for Your AI Policy Is Slop
- Commentary or coverage: news, commentary or policy with no study
- Policy and governance: matched 'AI Policy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: TeachersOutcome: Teacher practicePreprintSmall or imprecise
2026-10-09 · arXiv (back catalog) · Study (preprint)
Generative AI is increasingly studied as a tool for supporting lesson authoring; the creation and adaptation of classroom materials, yet little is known about how it should fit into secondary computing teachers' subject-specific practices. We conducted two complementary qualitative studies with eight secondary...
Why this label for From Instructional Intent to Classroom-Ready Lessons: Investigating Teacher-AI C
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: focus groups and think-aloud sessions with eight teachers are qualitative exploratory research.
- Grader B: R5 design ceiling: qualitative focus groups and think-aloud with 8 teachers, so descriptive/exploratory Level 1
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testProvisional (rules only)
2026-10-08 · arXiv search: AI + education · Study (preprint)
Pre-trained transformer models are increasingly being used to study scientific and technological progress. Encoders tuned to paper or patent text outperform general-purpose models on downstream classification, regression, and proximity tasks within science and technology.
Why this label for SciTBERT: A family of chronologically consistent language models for scientific
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-08 · arXiv search: AI + education · Study (preprint)
It is assumed that a natural way to improve the pedagogical quality of large language model tutors is to define a metric of instructional performance and fine-tune the model against it. To test this strategy, we designed a metric of pedagogical adaptivity that scores each instructional decision in a learning sequence...
Why this label for The learner who does not learn: when optimizing a pedagogical metric degrades LL
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
- Tutoring and personalization: matched 'tutoring'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-08 · EdTech Digest · News
At EDTECH WEEK NYC, StartEd’s Alan Todd gathers CEOs to rethink learning, leadership, and growth in the age of AI. INTERVIEW | by Victor Rivero As AI reshapes learning and work, edtech leaders face growing pressure to show what their companies deliver.
Why this label for Big Questions in the Big Apple
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Adults not in a learning role (raters)Outcome: Model performancePreprintSimulated learners
2026-10-08 · arXiv (back catalog) · Study (preprint)
It is assumed that a natural way to improve the pedagogical quality of large language model tutors is to define a metric of instructional performance and fine-tune the model against it. To test this strategy, we designed a metric of pedagogical adaptivity that scores each instructional decision in a learning sequence...
Why this label for The learner who does not learn: when optimizing a pedagogical metric degrades LL
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: educators rate tutor outputs across learner scenarios; no real learners are assessed.
- Grader B: R4 real-learner gate: 2,000 simulated learner scenarios, with educators rating model outputs; no learners, so T
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'tutoring'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintOpen data or materials
2026-10-08 · arXiv (back catalog) · Study (preprint)
Knowledge tracing (KT) models need many logged learners, so a new course or platform starts without a usable model. In LLM-based KT the LLM generates the answer, which we call System-Two; it is either fine-tuned on the target data or reasons and votes over ten samples, which is slow and gives coarse probabilities.
Why this label for Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logge
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: logged learner data are used to evaluate knowledge-tracing model accuracy, the only outcome reported.
- Grader B: R4 real-learner gate: knowledge-tracing AUC on existing logged datasets; the only outcome is model performance
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Level 1: Descriptive or exploratoryProvisional (rules only)
2026-10-07 · arXiv search: AI + education · Study (preprint)
Corrective feedback is among the best-evidenced drivers of second-language acquisition, yet corrections delivered during lessons rarely accumulate into an actionable view of grammar mastery. Prompted frontier models can provide such a view from learner--tutor lesson transcripts, but they are costly at scale.
Why this label for Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models O
- Level 1: Descriptive: provisional rules-only grade, pending grader consensus and review: R5 descriptive (F8 vendor self-report): Vendor describes its own internal work without design details; vendor hold applies.
- Preprint: not peer reviewed; capped at Level 3
- Feedback: matched 'Corrective feedback'
- Tutoring and personalization: matched 'tutor'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-07 · arXiv search: AI + education · Study (preprint)
Large language models (LLMs) have already been shown to match or exceed human experts in persuasion. While their persuasive capabilities hold promise for beneficial uses such as education and health communication, they can also be used to manipulate and misinform, making their evaluation a growing priority for...
Why this label for LLM Persuasion Is in the Eye of the Evaluation
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-07 · arXiv search: AI + education · Study (preprint)
Automatic Short Answer Scoring (ASAS) is central to NLP for Education. However, openly available benchmarks remain scarce, and existing datasets largely address how well students answer a question directly rather than how well they master underlying concepts (knowledge elements) such as thermal energy or epistemic...
Assessment and grading
Why this label for Alice: A Large-Scale German Benchmark for Rubric-Based Multi-Dimensional Automat
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
- Assessment and grading: matched 'Rubric'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-07 · arXiv search: AI + education · Study (preprint)
As Large Language Models (LLMs) are increasingly adopted for automated grading and feedback in higher education, their structured outputs, including multi-dimensional rubric scores, detailed feedback comments, and improvement suggestions, create an appearance of thorough analytic evaluation. This study examines...
Assessment and grading
Why this label for Beyond Score Accuracy: Examining the Diagnostic Quality of LLM-Generated Structu
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
- Assessment and grading: matched 'grading'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 2: CorrelationalLearners: Higher-ed studentsOutcome: Behavior or engagementPreprintPreregisteredPerception only
2026-10-07 · arXiv (back catalog) · Study (preprint)
AI learning tools are rapidly entering classrooms, but evidence about whether they help students learn is mixed and rests mostly on test scores. Comparatively less research addresses whether the use of AI changes students' live learning behaviors in class.
Why this label for How assigned AI use before class shapes active student engagement in class
- Level 2: Correlational: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R7 hard cap: randomized assignment is reported, but the measured outcome is classroom engagement rather than learning.
- Grader B: R7 engagement-only cap: a preregistered randomized field experiment (n=759 MBA students) that would otherwise be Level 3, but outcomes are class contributions and self-reported perceptions, with no learning measure, so max 2
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Motivation and engagement: matched 'student engagement'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Higher-ed studentsOutcome: Model performancePreprint
2026-10-07 · arXiv (back catalog) · Study (preprint)
As Large Language Models (LLMs) are increasingly adopted for automated grading and feedback in higher education, their structured outputs, including multi-dimensional rubric scores, detailed feedback comments, and improvement suggestions, create an appearance of thorough analytic evaluation. This study examines...
Assessment and grading
Why this label for Beyond Score Accuracy: Examining the Diagnostic Quality of LLM-Generated Structu
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: student responses provide test data, while the outcomes measure LLM grading and feedback quality.
- Grader B: R4 real-learner gate: compares LLM grades and feedback with instructors on existing student responses; the only outcome is model output quality
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
CommentaryProvisional (rules only)
2026-10-06 · arXiv search: AI + education · Study (preprint)
In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning analytics research increasingly relies on large language models (LLMs) to extract such information from dialogue for a variety of downstream tasks, including knowledge...
Why this label for Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Student Failu
- Commentary or coverage: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
- Preprint: not peer reviewed; capped at Level 3
- Tutoring and personalization: matched 'Tutoring'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryProvisional (rules only)
2026-10-06 · arXiv search: AI + education · Study (preprint)
As large language models increasingly mediate writing, translation, information retrieval, and education, the technological support available to a language may influence its position in contemporary social life. For minority and minoritized languages, this raises a dual problem: inadequate functionality can encourage...
Policy and governance
Why this label for From Algorithmic Marginalization to AI-Mediated Re-Centering: Can Culturally Gro
- Commentary or coverage: provisional rules-only grade, pending grader consensus and review: R3 genre gate (F7): Self-described conceptual, legal, policy or reflective work with no learner data.
- Preprint: not peer reviewed; capped at Level 3
- Policy and governance: matched 'governance'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-06 · eSchool News · News
AI is increasingly part of everyday leadership work, from drafting emails and reports to organizing ideas. Used thoughtfully, it can reduce administrative burden.
Why this label for “My boss probably used AI”: What school leaders need to know about AI, authentic
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 2: CorrelationalLearners: K-12 studentsOutcome: Behavior or engagementPreprint
2026-10-06 · arXiv (back catalog) · Study (preprint)
Algorithmic recommendations can help participants navigate large matching markets. For example, recommendations for school and college choices may reduce information frictions and disparities in access to high-performing programs.
Why this label for Personalized Recommendations Without Inducing Congestion: Mitigating Disparities
- Level 2: Correlational: Change to 2. Randomized deployment in the NYC high school match; the outcome is school-choice behavior, not learning, so held at 2 after final review.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: applicants were assigned in a randomized trial, with ranking and matching behavior measured; Level 4 learning-outcome criteria are unmet.
- Grader B: R7 behavior/engagement-only cap: an RCT in the NYC high school match with real applicants, but outcomes are ranking and matching behavior, not learning, so capped at 2; preliminary conference version, treated as a preprint
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryProvisional (rules only)
2026-10-05 · arXiv search: AI + education · Study (preprint)
Institutional Analytics (IA) dashboards inform decision-making in higher education, yet data limitations, constraints in analytical techniques, and missing contextual information often affect their interpretation. To support more responsible interpretation of IA, we introduce FACTRIA, a framework that organizes...
Why this label for Responsible Institutional Analytics: Interpreting Bias with AI Support
- Commentary or coverage: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
- Preprint: not peer reviewed; capped at Level 3
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testProvisional (rules only)
2026-10-05 · arXiv search: AI + education · Study (preprint)
We present an AI-based web-oriented tool designed to support formative feedback for oral presentation slides in higher education: AISSA. It allows students to upload their slides before presentation and automatically receive rubric-based quantitative scores and qualitative feedback generated by large language models...
Why this label for AISSA Demo: AI-based Student Slides Analysis Tool for Automated Grading and Feed
- Technical test: provisional rules-only grade, pending grader consensus and review: R4 real-learner gate (F1/F2): Outputs of models, simulations or audits; any humans act as raters, references or data sources, not learners.
- Preprint: not peer reviewed; capped at Level 3
- Feedback: matched 'formative feedback'
- Motivation and engagement: matched 'student engagement'
- Assessment and grading: matched 'Grading'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-05 · EdSurge · News
A backlash against classroom AI is sweeping public schools. But as New Mexico shows, blanket bans risk robbing students of early literacy interventions.
Why this label for Will Kids Pay the Price for AI Panic?
- Commentary or coverage: news, commentary or policy with no study
- Reading and writing: matched 'literacy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-05 · eSchool News · News
For many schools, the path to AI literacy remains unclear. The challenge is not simply AI’s rapid evolution, but its ability to generate answers almost instantly, forcing educators to reconsider how learning is guided and measured.
Why this label for Learner agency comes first in AI-driven digital literacy
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-10-05 · EdTech Digest · News
Connected data is only the beginning. Institutions also need clear authority to turn AI insights into action.
Why this label for Before Higher Ed Can Scale AI, It Needs the Right Foundation - and a Reason to B
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
No study cited
2026-10-05 · Product Hunt · Launch / announcement
An AI tutor that teaches at a whiteboard, not a chat box
Why this label for Albie
- No study cited: launch or announcement; no traceable study
- Tutoring and personalization: matched 'tutor'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryProvisional (rules only)
2026-10-04 · arXiv search: AI + education · Study (preprint)
As large language models (LLMs) are increasingly integrated into engineering workflows, students require hands-on experience to learn how to collaborate with them critically. This paper presents a scalable gamified workshop designed for engineering Master's students to practice human-AI collaboration in navigation...
Assessment and grading
Why this label for Educating future engineers about LLMs: A scalable workshop
- Level 1: Descriptive: provisional rules-only grade, pending grader consensus and review: R5 design: descriptive (F3): Descriptive, qualitative, pilot or deployment study with real participants.
- Preprint: not peer reviewed; capped at Level 3
- Assessment and grading: matched 'automated scoring'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
2026-10-03 · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education by Jered Borup, Charles R. Graham, Leanna Archambault et al. Available online 3 October 2026. Abstract on the publisher page.
Why this label for Online learner engagement and academic support in the age of generative AI
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learners
2026-10-02 · arXiv (back catalog) · Study (preprint)
AI can help adults produce plausible workplace artefacts while leaving their independent reasoning difficult to inspect. LearnAdapt Praxis addresses this design problem through a five-stage learning workspace: Frame, Learn, Build, Validate and Transfer.
Why this label for LearnAdapt Praxis: Controlled AI Assistance and Evidence Traces for Adult Workpl
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: synthetic project episodes test engineering properties; no adult learner outcomes are measured.
- Grader B: R4 real-learner gate: system checks run on a synthetic rehearsal with fictional contexts; the abstract says it does not establish learning gains
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
No study cited
2026-10-01 · EdTech Magazine K-12 · Launch / announcement
Google has rolled out a wave of new artificial intelligence features across its ecosystem of products purpose-built for education. At the heart of these features sits Google Classroom, a learning hub for teachers and students globally.
Why this label for Google Launches New AI Tools Purpose-Built for Education
- No study cited: launch or announcement; no traceable study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Behavior or engagementPeer-reviewed (journal reference listed)
2026-10-01 · arXiv (back catalog) · Study
Large language models can generate plausible solutions to programming assignments, making it tempting to detect their use by matching student code against a reference bank of generated solutions. Yet similar code can also arise when an assignment admits only a few natural implementations, which leaves open what a...
Why this label for Correctness, Convergence, and AI-Generated Code Detection: A Longitudinal Study
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: retrospective comparisons of student submissions and generated code are descriptive, with no controlled learner assignment.
- Grader B: R5 design ceiling: descriptive longitudinal analysis of real student code submissions across cohorts compared with LLM code; no comparison design, so Level 1 (accepted at Koli Calling, DOI)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Behavior or engagementPreprintPerception only
2026-10-01 · arXiv (back catalog) · Study (preprint)
Timely and specific feedback is one of the strongest influences on student learning, yet it is difficult to sustain in large electrical engineering classes where the ratio of students to demonstrators is high and a learner who is stuck may wait days to find out why an approach was wrong. Generative Artificial...
Assessment and grading
Why this label for Feedback Without the Wait: Piloting a Generative AI Practice Platform in a Large
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the abstract describes a pilot of student engagement and perceptions without a comparison design.
- Grader B: R5 design ceiling: a single-course pilot reporting engagement and perceptions, so Level 1
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-09-30 · EdSurge · News
In this episode of This Week with EdSurge, two former educators join to discuss EdReport's recent brief and explain why it is so important to verify ...
Why this label for Can Schools Trust AI Features in Edtech Products?
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-30 · EdTech Digest · News
FETC unveils an agenda for human leadership in the age of AI. GUEST COLUMN | by Jennifer Womble These are extraordinary times in education.
Why this label for AI Cannot Lead Your District. Only People Can.
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
2026-09-29 · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education by Jiun-Yu Wu, An-Ching Shih, Yuan-Hsuan Lee et al. Available online 29 September 2026. Abstract on the publisher page.
Why this label for Fifty years of topic development, thematic combination, and AI's evolving reach
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-29 · EdSurge · News
As AI features flood K-12 curricula, educators are left to filter proven quality from marketing hype.
Why this label for Study: Edtech Is Rushing AI Integration Before Proving It Works
- Commentary or coverage: news summary of others' work; mentions 'Study', graded C until the cited study is itself graded
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-29 · EdTech Digest · News
The families with the most resources are buying more human support, not less. GUEST COLUMN | by Alicia Kacharia There is a lot of excitement about how, for the first time, every student can access a personalized tutor regardless of income, location, or level.
Why this label for AI Will Work Best for the Students Who Need It the Least
- Commentary or coverage: news, commentary or policy with no study
- Tutoring and personalization: matched 'tutor'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-29 · EdTech Magazine K-12 · News
Fueled by factors such as chronic absenteeism and pandemic learning challenges, students’ reading scores have been trending downward for years. A biennial National Assessment of Education Progress test found that approximately a third of eighth grade students lack basic reading skills, the largest percentage on record.
Why this label for How K - 12 Districts Are Boosting Student Literacy With Technology
- Commentary or coverage: news, commentary or policy with no study
- Reading and writing: matched 'Literacy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
No study cited
2026-09-29 · Product Hunt · Launch / announcement
An AI tutor on an infinite whiteboard, not a chat thread
Why this label for Gauth Unlimited Digital Canvas
- No study cited: launch or announcement; no traceable study
- Tutoring and personalization: matched 'tutor'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Behavior or engagementPreprintResearcher-made measureSimulated learners
2026-09-29 · arXiv (back catalog) · Study (preprint)
When a student is stuck, a tutor faces the assistance dilemma: help given too early can hinder productive struggle, while help withheld too long leaves the student in a frustrating, persistent impasse (i.e., wheel spinning). Generative AI tutors increasingly use guardrails restricting answer-giving, yet little is...
Why this label for Examining Variation in How Guided AI Tutors Resolve Student Impasses
- Level 1: Descriptive or exploratory: Real tutoring logs analyzed descriptively, then simulated conditions; graded on the real arm as 1.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: associations in authentic student sessions support a correlational level; simulated tutor conditions do not raise it.
- Grader B: R4 mixed study graded on the real-learner arm only: correlational analysis of authentic sessions (Level 2), downgraded under R6 measurement because the impasse/recovery coding has no reported reliability
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'Tutors'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Model performancePreprintSmall or imprecise
2026-09-29 · arXiv (back catalog) · Study (preprint)
AI-assisted cheating has become an important threat to the security of online exams. This study examines whether the risk of AI-assisted cheating in the final exam can be predicted using students' digital traces in the learning management system (LMS) during the first eight weeks of the semester.
Assessment and gradingAcademic integrity
Why this label for Early Prediction of AI-Assisted Cheating Risk in Online Exams Through Learning A
- Level 1: Descriptive or exploratory: Change to 1. Predictive analysis of LMS logs from 52 students; correlational design downgraded once for imprecision (n under 100, no interval).
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: student logs train a cheating-risk predictor, and predictive accuracy is the reported outcome.
- Grader B: R5 predictive/correlational design (Level 2), downgraded under R6 imprecision: n=52 with no interval information
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-09-28 · EdTech Magazine K-12 · News
Digital devices used for K - 12 learning have traditionally provided access to productivity software, research and digital assignments. But now, artificial intelligence technology is enabling devices to generate instructional material, guide students through problems and connect classroom workflows.
Why this label for How AI Is Transforming the Role of the K - 12 Device
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Learning or achievementPreprintResearcher-made measure
2026-09-28 · arXiv (back catalog) · Study (preprint)
The rapid proliferation of large language models (LLMs) in the context of education has introduced significant challenges in enforcement of academic integrity, especially in programming courses. We present Argus, an automated detection system for LLM-assisted student work in undergraduate C programming assignments.
Academic integrity
Why this label for Argus: Academic Integrity in the Era of Generative AI
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: retrospective flagged-use and exam-performance findings report correlation without statistical controls.
- Grader B: R5 design ceiling: system description plus a simple correlation between detector flags and exam scores, with no statistical controls reported, so Level 1; the detector measure is unvalidated
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Not reportedOutcome: Not reportedPreprint
2026-09-28 · arXiv (back catalog) · Study (preprint)
Academic access programs distribute frontier artificial intelligence as research infrastructure, but awards and institutional authorization are distinct stages. We examine the handoff through structured coding of 15 publicly documented access pathways captured on 19 September 2026, of which ten met prespecified...
Why this label for After the Award: The Authorization Gap in Academic Access to Frontier AI
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 descriptive process trace of access programs and one institution, with no learner outcome.
- Grader B: R5 design ceiling: descriptive document coding of 10 access programs plus a single-institution process trace; no learners or comparison group, so Level 1 (descriptive).
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Not yet classified
2026-09-27 · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education by Ramazan Yilmaz, Fatma Gizem Karaoglan Yilmaz. Available online 27 September 2026. Abstract on the publisher page.
Why this label for From computer-supported collaborative learning to human - AI collaboration: CSCL
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 3: Controlled comparisonLearners: Adult learnersOutcome: Learning or achievementPreprintPreregisteredShort exposure
2026-09-26 · arXiv (back catalog) · Study (preprint)
Chatbots deliver real-time fact-checks, but whether a chatbot correction leaves anything behind once the chatbot is gone - metalearning, distinct from correcting misbeliefs - is untested. We report a preregistered, three-country randomized experiment (USA, India, Singapore; N ~ 2,200) on out-of-context image...
Why this label for Chatbot Engagement Does Not Always Beget Metalearning: Evidence from Three Count
- Level 3: Controlled comparison: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 randomized participants across four conditions with a one-week unaided discernment retest; R7 preprint cap.
- Grader B: R5: preregistered randomized experiment with N~2,200 real adults and a discernment (learning) outcome at one-week retest gives Level 3; R7 preprint and single-session caps hold it at 3, and as a preprint it cannot be a Level-4 candidate.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learners
2026-09-25 · arXiv (back catalog) · Study (preprint)
Oral assessment with generative AI requires more than a conversational interface: educators must connect a spoken response to its source material, scoring criteria, model outputs and subsequent human judgement. This technical report presents CALLIOPE, a source-grounded oral assessment system integrating versioned...
Assessment and grading
Why this label for CALLIOPE: A Source-Grounded Oral Assessment System and Synthetic Readiness Evalu
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 synthetic spoken fixtures test the assessment system; no human-participant outcomes are reported.
- Grader B: R4 real-learner gate: synthetic software verification with spoken fixtures; the abstract says no human-participant outcomes are reported.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-09-24 · EdTech Digest · News
The nation’s broadest restriction may buy educators time - but only teacher literacy can provide a durable path forward. GUEST COLUMN | by Sohan Choudhury On September 2, Mayor Mamdani announced a one-year moratorium on student-facing generative AI in New York City public schools, covering pre-K through eighth grade.
Why this label for NYC Banned AI in Schools. That Won’t Work.
- Commentary or coverage: news, commentary or policy with no study
- Reading and writing: matched 'literacy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-24 · EdTech Magazine K-12 · News
When schools talk about students using artificial intelligence, the conversation often begins with assignments. Are students using AI to brainstorm, summarize or complete work they should be doing themselves?
Why this label for The Hidden Side of Student AI Use: Mental Health, Access and Risk
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-09-24 · arXiv (back catalog) · Study (preprint)
Generative artificial intelligence has moved from a disruptive novelty to a recurring part of software-development and computing-education workflows, while software agents are beginning to act across repositories, command lines, browsers, tests, and other tools. The educational problem is no longer whether students...
Policy and governance
Why this label for Judgment-Centred Software Engineering Education: A Post-Hype Review and Framewor
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 integrative narrative review and proposed education framework, with no systematic study synthesis described.
- Grader B: R3 genre gate: a structured integrative (narrative) review plus a conceptual framework, with no stated systematic search criteria or controlled-study synthesis.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-09-23 · EdSurge · News
The true metric of AI in school leadership is not time saved, but the presence and clarity restored to daily decision-making.
Why this label for Why AI Made Me a Better Principal
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-23 · EdTech Digest · News
A new Cengage report finds entry-level jobs increasingly demand work-ready skills - pushing graduates toward internships, stepping-stone roles, and self-directed AI learning. An entry-level job is supposed to be where experience begins.
Why this label for The Experience Gap
- Commentary or coverage: news summary of others' work; mentions 'report finds', graded C until the cited study is itself graded
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learners
2026-09-23 · arXiv (back catalog) · Study (preprint)
Secondary use of educational records is increasingly mediated by platforms that share a differentially private synthetic version of a dataset and validate specific findings against the real data on request. The synthetic version is evaluated by comparing summary statistics of each variable, yet reported confirmation...
Why this label for What fidelity metrics miss: a structural check on synthetic educational data
- Technical test: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: compares synthetic educational data with real logs, but measures data fidelity rather than learner outcomes.
- Grader B: R4 real-learner gate: checks how faithful synthetic educational data is against real learner logs; the outcome is data-generator fidelity, not learner outcomes
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Adults not in a learning role (raters)Outcome: Behavior or engagementPreprintSmall or imprecise
2026-09-23 · arXiv (back catalog) · Study (preprint)
Educational technology (EdTech) platforms collect highly sensitive student data, including behavioral logs, disability records, and academic histories. However, privacy considerations are often postponed rather than treated as a foundational design requirement.
Policy and governance
Why this label for "We'll Fix It Later": Education, AI, and the Deferral of Privacy in EdTech
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: interviews and a platform policy audit provide descriptive evidence without a controlled comparison.
- Grader B: R5 design ceiling: qualitative interviews (n=12 EdTech professionals) plus a privacy policy audit of 48 platforms (kappa 0.78); descriptive, no learners
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
No study cited
2026-09-22 · EdSurge · Launch / announcement
The CZI-backed initiative helps educators and developers use state standards, curricula, and datasets to create and evaluate classroom AI, but critics ...
Why this label for Learning Commons Launches Open Platform for K-12 Edtech
- No study cited: launch or announcement; no traceable study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Adults not in a learning role (raters)Outcome: Model performancePreprintResearcher-made measure
2026-09-22 · arXiv (back catalog) · Study (preprint)
This study aimed to automate hierarchical diagnostic reasoning (HDR), a constructive method for evaluating practical judgment skills, by developing and testing an evaluation process using a large language model. HDR is a descriptive task that measures higher-order cognitive skills by requiring students to identify and...
Why this label for Automating Constructive Assessment with Large Language Models: Toward Scalable a
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 the reported results validate automated problem generation, scoring, and feedback against ratings, not learner outcomes.
- Grader B: R4 real-learner gate: the outcomes are LLM item-generation validity, human-AI scoring agreement and rater judgments of feedback, which is model performance only; no student learning outcome.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Behavior or engagementPreprintPerception only
2026-09-22 · arXiv (back catalog) · Study (preprint)
This paper presents a multi-source classroom study conducted during a 10-week quarter in data science courses at Drexel University. We first investigate the behaviors of student engagement with large language models (LLMs) using four surveys across three data science courses.
Why this label for Student Use of LLMs and the Limits of AI-Generated Question Difficulty in Data S
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 classroom surveys describe student use and perceptions; response data also test LLM question ratings without a learner comparison.
- Grader B: R5: surveys and an item-difficulty correlational analysis in a 10-week course with no comparison group are descriptive (Level 1); the R7 perception/engagement cap also applies to learning claims.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Retrieval practice: matched 'retrieval practice'
- Motivation and engagement: matched 'student engagement'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-09-21 · EdSurge · News
From baseline AI literacy to early career exposure, Jobs for the Future president Maria Flynn shares how education systems can prepare young people for ...
Why this label for Navigating the Future of Work: A Conversation with JFF’s Maria Flynn
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-09-21 · arXiv (back catalog) · Study (preprint)
Third-party evaluations for frontier AI have mostly tested models through external interfaces before deployment. But the risks from frontier AI models depend on how their developers use and govern them internally.
Why this label for Embedded Assessments for Frontier AI
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 position paper argues for embedded assessments and recommends their design without reporting an empirical study.
- Grader B: R3 genre gate: a policy and position paper recommending embedded assessments for frontier AI.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Level 2: CorrelationalLearners: Higher-ed studentsOutcome: Learning or achievementPreprintPreregisteredSmall or imprecise
2026-09-21 · arXiv (back catalog) · Study (preprint)
Students increasingly study alongside generative artificial intelligence (AI), yet unguided access to fluent answers invites cognitive offloading, and there is little evidence on which configurations of AI tutoring produce learning. Two design margins are usually bundled together: pedagogical structure (how the tutor...
Why this label for When AI Tutors Speak: Evidence from a Randomized Field Experiment
- Level 2: Correlational: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 randomized 86 MBA students with a learning outcome starts at Level 3; R6 imprecision lowers it because no interval or power information is reported.
- Grader B: R5: a preregistered randomized field experiment with a learning outcome starts at Level 3; R6 imprecision (n=86, under 100, and only p-values with no interval or power information) lowers it to Level 2.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Cognitive load: matched 'cognitive offloading'
- Tutoring and personalization: matched 'Tutors'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-09-18 · eSchool News · News
In 2023, Catherine Hartmann, a professor at the University of Wyoming, gave students in a history of meditation course a deeply personal assignment: try a contemplative practice and write a reflective analysis on the experience.
Assessment and grading
Why this label for The oral exam is back, and AI may be the reason
- Commentary or coverage: news, commentary or policy with no study
- Assessment and grading: matched 'exam'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 2: CorrelationalLearners: Adults not in a learning role (raters)Outcome: Behavior or engagementPreprint
2026-09-18 · arXiv (back catalog) · Study (preprint)
AI is rapidly transforming higher education, including the application process, yet relatively little is known about its use and consequences. To help close this gap, we analyze nearly 7{,}500 applications submitted between 2020 and 2025 to a large public policy master's program in the United States.
Why this label for AI-written admissions essays are widespread but penalized
- Level 2: Correlational: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 observational comparisons of applicants and an admissions-officer rating experiment do not establish learner assignment or baseline equivalence.
- Grader B: R5: an observational analysis of ~7,500 applications with comparable-applicant controls and a ChatGPT-introduction natural experiment is correlational (Level 2); the outcomes are essay quality and admission, not learning. The admissions-officer arm is a rater study.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Learning or achievementPreprintSmall or impreciseShort exposureResearcher-made measure
2026-09-18 · arXiv (back catalog) · Study (preprint)
Generative AI can improve students' programming performance, but successful task completion may not reflect what they retain. We examined performance, retention, cognitive load, and ownership in a controlled between-subjects experiment with 59 undergraduate computer science students, 55 were retained for analysis.
Why this label for Your Programming Students' Cognition with ChatGPT: Higher Performance, Lower Ret
- Level 1: Descriptive or exploratory: Between-subjects experiment, 55 analyzed, no baseline equivalence and no interval; downgraded to 1.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 controlled comparison lacks explicit random assignment or baseline data; R6 imprecision lowers Level 2 for 55 analyzed students without an interval or power report.
- Grader B: R5: a controlled between-subjects experiment with a recall (learning) outcome is Level 3; R6 imprecision (n=55, no intervals) lowers it to Level 2. R7 short-exposure cap also noted.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Cognitive load: matched 'cognitive load'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-09-16 · EdSurge · News
By viewing our classrooms as interconnected ecosystems, we can move beyond the surface-level debate over technology to focus on cultivating judgment, ...
Why this label for What Yellowstone, Rwanda and AI Taught Me About Change
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-09-16 · arXiv (back catalog) · Study (preprint)
Digital competence frameworks define the knowledge, skills, attitudes and values required for participation in digital society. The European Digital Competence Framework for Citizens (DigComp) and UNESCO digital literacy and artificial intelligence competency frameworks provide reference structures for policy,...
Why this label for From Digital Competence to Demonstrated Digital Capability: Positioning the Inte
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 explicitly conceptual comparison of digital-competence frameworks without participant outcomes.
- Grader B: R3 genre gate: a self-described conceptual paper comparing competency frameworks.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePreprintPerception onlySmall or imprecise
2026-09-16 · arXiv (back catalog) · Study (preprint)
Generative artificial intelligence (GenAI) is changing how students learn, yet the roles of course context, cognitive reliance, evaluation literacy, and early reliance remain underexplored. Using survey responses from 118 students across 12 AI-related courses at our institution, we examined differences in GenAI use...
Why this label for The Uneven Impact of Generative AI on Student Learning: Examining the Roles of R
- Level 1: Descriptive or exploratory: Survey of 118 students on perceived learning; perception-only descriptive evidence.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 multivariable survey associations support Level 2; R7 perceived learning and use do not establish learning achievement.
- Grader B: R5: a cross-sectional survey of 118 students with regression on self-reported perceived learning is a survey (Level 1); the R7 perception-only cap also applies.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Reading and writing: matched 'Literacy'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-09-14 · EdSurge · News
The bans, coupled with a new APA report, signal a shift from rapid AI adoption toward more deliberate evaluation.
Why this label for New York and Los Angeles Ban Student-Facing AI (For Now). Should Other Schools F
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintOpen data or materials
2026-09-13 · arXiv (back catalog) · Study (preprint)
Teaching abstract theoretical computer science (TCS) concepts such as algorithm analysis and complexity theory is challenging because students must handle formal proofs and asymptotic reasoning that conventional resources rarely explain in an adaptive, on-demand way. We present AlgoRAG, a specialized...
Assessment and grading
Why this label for AlgoRAG: Retrieval-Augmented Generation for Theoretical Computer Science Educati
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: evaluates system answers to curated questions, not learner outcomes.
- Grader B: R4 real-learner gate: RAG system scored on 179 exam questions (ROUGE, pedagogical quality score); no students took part, so the claim about personalized instruction is untested
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-09-11 · EdSurge · News
Using AI in IEP meetings can benefit parents and teachers - if done correctly.
Why this label for Parents Are Bringing AI to the IEP Meeting. How Should Teachers Respond?
- Commentary or coverage: news, commentary or policy with no study
- Inclusion and accessibility: matched 'IEP'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-09 · EdSurge · News
Students asking why learning matters in the age of AI forces two teachers to respond.
Why this label for Two Students, Two Questions They Wouldn’t Let Go
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-09 · eSchool News · Report / framework
A new national framework from AACTE (American Association of Colleges for Teacher Education) aims to help educator preparation programs (EPPs) navigate the rapidly changing role of artificial intelligence in education.
Why this label for AACTE releases national framework on AI in educator preparation
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-08 · EdSurge · News
The rise of so-called “baby slop” is turning early childhood educators into media critics.
Why this label for AI Videos Are Warping Early Learning, Teachers Say
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-05 · Carl Hendrick (learning science) · Commentary
Roundup of new research on explaining answers, building knowledge, reading instruction, social media and the conditions under which AI helps students learn.
Why this label for New Research on Learning Science: The September Dispatch
- Commentary or coverage: news summary of others' work; mentions 'New Research', graded C until the cited study is itself graded
- Reading and writing: matched 'reading instruction'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-04 · eSchool News · News
Schools are racing to teach students how to use AI. That is necessary, but it is not enough.
Why this label for Career readiness in the AI era needs decision practice, not just tool access
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePeer-reviewed (journal reference listed)Perception onlySmall or imprecise
2026-09-04 · arXiv (back catalog) · Study
The study investigates students' interest and expectations in a Big Data Engineering course integrated with a Master curricula, as well as ethical implications of using Big Data. An anonymous online survey was conducted with 42 of the 67 students enrolled in the Big Data course offered to Computer Science and...
Why this label for Students' Perception of Big Data Engineering in Higher Education Curricula: Expe
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 survey ceiling: 42 students reported interests and expectations; no learning outcome was measured.
- Grader B: R5 design ceiling: a one-time anonymous survey of 42 master's students with thematic analysis is descriptive (Level 1); R7 perception-only cap also applies; the conference proceedings DOI and ISBN indicate it is peer-reviewed
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-09-03 · eSchool News · News
AI technology is advancing rapidly in schools, but research is increasingly showing that Large Language Models (LLMs) lack the common sense, morality, and knowledge of individual classroom dynamics needed to be truly equitable.
Why this label for Building an everyday equitable practice for AI in schools
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-09-03 · arXiv (back catalog) · Study (preprint)
Contributions: A reflection model suitable for the era of Generative Artificial Intelligence (GenAI) is introduced. The proposed model is an integrated model that extracts features from various existing models and also incorporates technological aspects of GenAI.
Academic integrity
Why this label for The 5P Reflection Model for Education in the Generative Artificial Intelligence
- Commentary: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R3 genre gate: the paper proposes a reflection model from existing literature without reporting learner outcomes.
- Grader B: R3 genre gate: it is labelled design-based research, but it analyses existing reflection models and proposes a new one; no participants or outcome data
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-09-02 · EdSurge · News
True innovation isn’t found in mastering every platform, but in being willing to ask deeper questions and take risks.
Why this label for How AI Taught Me to Embrace Vulnerability in the Classroom
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-09-01 · EdSurge · News
The new offering is promising, but there are some real-world reservations.
Why this label for A Principal and a Student Reviewed the New ChatGPT for Teens. They Had Plenty to
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: Adult learnersOutcome: Learning or achievementPeer-reviewed (journal reference listed)Small or impreciseShort exposureResearcher-made measure
2026-09-01 · arXiv (back catalog) · Study
Does unrestricted AI access bypass the cognitive effort required for learning, or does it streamline knowledge acquisition? This paper reports on a study where we compare three designs for user-AI interaction in a learning context: (1) an unrestricted conversational bot like ChatGPT, (2) a pedagogically constrained...
Why this label for Socrates went Nuclear: Comparing Interaction Strategies for AI systems in a Lear
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling permits Level 2 for a pre/post comparison without stated equivalence; R6 imprecision and measurement lower it to Level 1 for 50 learners and an unvalidated immediate test.
- Grader B: R5 three-condition comparison with pre-test, capped at 3; R7 short-exposure cap applies; R6 imprecision (n=50, d and p only, no interval) lowers to 2; R6 measurement (researcher-made immediate post-test, no reliability) lowers to 1; construct flag: the authors say the chatbot's gains reflect answer retrieval rather than deeper learning
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Cognitive load: matched 'cognitive effort'
- Tutoring and personalization: matched 'tutoring'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Not yet classified
2026-08-28 · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education by Chen-Chung Liu, Chin-Chung Tsai, Ramazan Yilmaz et al. Available online 28 August 2026. Abstract on the publisher page.
Why this label for The design framework for pedagogical agents supporting constructivist learning w
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-08-28 · EdSurge · News
Artificial intelligence can bridge the middle grades literacy gap by providing individualized student reading instruction at scale.
Why this label for AI Can Help One of K-12’s Biggest Challenges: Middle School Reading
- Commentary or coverage: news, commentary or policy with no study
- Reading and writing: matched 'literacy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
No study cited
2026-08-28 · Product Hunt · Launch / announcement
AI courses you can watch, quiz through, and create
Why this label for Gauth AI Course
- No study cited: launch or announcement; no traceable study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePreprintPerception onlySmall or imprecise
2026-08-28 · arXiv (back catalog) · Study (preprint)
As generative AI (GenAI) use among students increases, educators face growing questions about how to support learning while addressing ethical and institutional concerns. This exploratory study examines a guided inquiry activity in which students co-designed a GenAI course policy.
Why this label for A Guided Inquiry Approach to Students Co-Designing Generative AI Course Policies
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: analysis of student artifacts and discussions is exploratory and has no controlled learning outcome.
- Grader B: R5 design ceiling: an exploratory qualitative analysis of student artifacts and discussions from one course activity; no comparison group
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-08-27 · EdSurge · News
Four student artists map out what artificial intelligence means for their communities today - and 50 years from now.
Why this label for Student Artists Wrestle with AI’s Promise and Peril
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-08-27 · arXiv (back catalog) · Study (preprint)
Generative artificial intelligence (GenAI) has been primarily framed as an impartial educational tool. However, this framing overlooks an even larger shift: the reassignment of epistemological authority from teachers to students to machines.
Why this label for Reclaiming Epistemic Agency: A Critical Framework for Human-Generative AI Co-Age
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 genre gate: the paper presents a conceptual evaluation and framework without an empirical study.
- Grader B: R3 genre gate: a conceptual evaluation of theories that proposes a framework; no empirical study
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-08-27 · arXiv (back catalog) · Study (preprint)
Companies that deploy AI agents and make them available to others are creating the sociotechnical circumstances under which this technology integrates into existing social and economic structures. AI-deploying companies are institutional actors that actively shape society's capacity to withstand and govern the...
Why this label for Assessing Company Contributions to Societal Resilience: Extending the Societal C
- Commentary: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the structured assessment of company documents is a descriptive application of a framework.
- Grader B: R3 genre gate: adapts a conceptual framework and applies it to public company documents; no participants or learning outcome
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Adults not in a learning role (raters)Outcome: Model performancePreprint
2026-08-27 · arXiv (back catalog) · Study (preprint)
Reliable assessment of causal research designs in the social sciences is critical for evidence-based policy-making, yet has so far relied entirely on manual expert analysis. We introduce Automated Research Design Tracking and Assessment (ARDTrA), a task that involves detecting the research design used in a paper and...
Why this label for Research Design Tracking and Assessment for the Social Sciences
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: expert-annotated papers serve as a benchmark for an LLM research-design assessment pipeline.
- Grader B: R4 real-learner gate: RAG/LLM pipeline evaluated on an expert-annotated paper dataset; outcome is model performance (EMNLP 2026).
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-08-26 · EdSurge · News
Educators must shift their focus from preventing cognitive offloading to preserving the critical thinking skills AI threatens to replace.
Why this label for Protecting Student Cognition in the Age of AI
- Commentary or coverage: news, commentary or policy with no study
- Cognitive load: matched 'cognitive offloading'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-08-25 · eSchool News · News
Long before computers entered our homes and classrooms, photographs helped shape what people believed. Images carried a particular kind of authority: We did not simply look at them; we often treated them as evidence.
Why this label for Beyond spotting deepfakes: Teaching multilingual learners to interrogate AI
- Commentary or coverage: news, commentary or policy with no study
- Inclusion and accessibility: matched 'multilingual learners'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Learning or achievementPreprintResearcher-made measureSmall or imprecise
2026-08-25 · arXiv (back catalog) · Study (preprint)
Worked examples are an important part of introductory programming, but reading their expert explanations is passive. Self explanation, students explaining the problem and its solution to themselves with subgoal level analysis, converts passive reading into an active study of worked example, yet it is hard to scale...
Why this label for Self-Explanation Tutor for Active Study of CS1 Worked Examples
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the course deployment reports student revisions and learning evidence without a stated comparison group.
- Grader B: R4 mixed study, so only the real-learner arm is graded (the LLM-vs-human-rater arm is T); that arm is a single-group course deployment, so R5 sets Level 1
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Worked examples and explicit instruction: matched 'Worked Examples'
- Tutoring and personalization: matched 'Tutor'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePreprintPerception only
2026-08-25 · arXiv (back catalog) · Study (preprint)
The purpose of this study was to analyze patterns of artificial intelligence (AI) use and attitudes toward AI among students, faculty, and administrative staff at a large university specializing in teacher education. The analytical sample comprised 1809 students, 250 faculty members, and 62 administrative staff...
Academic integrity
Why this label for The AI Adaptation Gap in Higher Education: Students, Faculty, and Administrative
- Level 1: Descriptive or exploratory: Large attitude and use survey (N=2,121); perception-only descriptive evidence.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the cross-sectional survey reports controlled associations; R7 caps perception and engagement outcomes at Level 2.
- Grader B: R5 design ceiling: a cross-sectional self-report survey (N=2121); the OLS associations could match Level 2, but a survey is Level 1 and R5 takes the lower; R7 perception-only cap applies anyway
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: K-12 studentsOutcome: Behavior or engagementPreprintOpen data or materialsVendor-linkedResearcher-made measure
2026-08-21 · arXiv (back catalog) · Study (preprint)
The Atom Learning Model (ALM) tokenises a school curriculum. 757 pages of GCSE and Further Mathematics material were read by machine into 1,934 atoms, each one thing a learner can do in a single step, ordered by 4,616 machine-written prerequisite links.
Why this label for Atom Learning Model (ALM): how a real classroom got tokenised
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the classroom deployment reports descriptive usage and question performance without a controlled comparison.
- Grader B: R5 design ceiling: a descriptive deployment report (373 pupils, 7 weeks) with no comparison group; it reports behavior and system metrics, not a learning effect
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learners
2026-08-20 · arXiv (back catalog) · Study (preprint)
Whether AI assistance helps or harms a population depends less on the model's accuracy than on whether people rely on it appropriately trusting it when it is right and checking it when it is not. Yet reliance is usually studied one user at a time.
Why this label for Modeling AI Overreliance as a Complex Adaptive System
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: the reliance findings come from simulated agents.
- Grader B: R4 real-learner gate: an agent-based Bayesian simulation of a population's reliance on AI; all participants are simulated
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-08-19 · EdSurge · News
On This Week with EdSurge, searching to see where artificial intelligence quietly adds up.
Why this label for Assignments, Invoices, and the AI Fix
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: K-12 studentsOutcome: Perception or attitudePreprintSmall or imprecisePerception only
2026-08-19 · arXiv (back catalog) · Study (preprint)
K-12 students often possess creative engineering ideas but lack technical skills to build them, while undergraduates have coding expertise but few opportunities to lead real-world projects or mentor others. The rapid development of AI-assisted tools offers a potential bridge to connect these groups, yet the structure...
Why this label for Multi-Tier Mentorship with AI-Assisted Development: Authentic Engineering for K-
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the nine-person pilot reports surveys and thematic findings without a comparison group.
- Grader B: R5 design ceiling: a pilot with 4 high-school students and 3 undergraduates, using surveys and thematic analysis; descriptive, and R7 perception-only applies
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryLearners: TeachersOutcome: Perception or attitudePreprintReport (grey literature)
2026-08-19 · arXiv (back catalog) · Study (preprint)
On 28 July 2026, the SIGCSE Virtual 2026 Working Group on computing assessment and generative AI held an open online workshop attended by 73 computing educators. The first hour ran seven strategy rooms, one per approach to adapting (or deliberately preserving) computing assessment in the AI era: open-ended and...
Why this label for AI Can Do Your Homework. Now What? Report from an Online Workshop on Computing A
- Commentary: Workshop report, not a study.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 descriptive ceiling: workshop report records 73 educators' accounts without a controlled learning study.
- Grader B: R3 genre gate: a workshop report recording what 73 computing educators discussed; it has no study design and no measured outcome.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Motivation and engagement: matched 'student motivation'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryLearners: Adult learnersOutcome: Not reportedPeer-reviewed (journal reference listed)
2026-08-19 · arXiv (back catalog) · Study
Systemic change across the digital research landscape is required to reduce the environmental impact of digital research, but while many researchers and technical professionals are motivated to act, they often lack the skills required to translate motivation into lasting organisational change. We present an...
Why this label for Turning interest into institutional change: teaching advocacy for sustainable re
- Commentary: Course description for researchers with no evaluation; conceptual.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 pilot ceiling: a short course pilot is mentioned, but no learner outcome or comparison is reported.
- Grader B: R3 genre gate: describes an open course and its design; the short pilot reports no data or outcomes, so there is no study to grade.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-08-17 · eSchool News · News
A year ago, many educators were still asking whether artificial intelligence belonged in the classroom. Some were cautiously experimenting with AI-generated lesson plans, while others were focused on preventing students from using it altogether.
Why this label for 5 ways AI can strengthen your teaching this school year
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learners
2026-08-17 · arXiv (back catalog) · Study (preprint)
Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction requires labels that are unevenly distributed across institutions: smaller hospitals have less local evidence, and pooling data may be infeasible.
Why this label for FedPref: Federated Preference Learning for Structured Radiology Report Extractio
- Technical test: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: simulated hospitals and report extraction are evaluated by model F1.
- Grader B: R4 real-learner gate: federated model training on simulated hospitals, and the only outcome is extraction F1, which is model performance.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
No study cited
2026-08-16 · Product Hunt · Launch / announcement
AI workspace for students
Why this label for Skriptr
- No study cited: launch or announcement; no traceable study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-08-13 · EdSurge · News
At a gathering in Boston, ‘student senators’ proposed a first-of-its-kind national AI policy for K-12 classrooms.
Policy and governance
Why this label for On AI Policy, Students Have Plenty to Say
- Commentary or coverage: news, commentary or policy with no study
- Policy and governance: matched 'AI Policy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-08-12 · EdSurge · News
I left my school to ask tech executives how to prepare students for the future.
Why this label for The K-12 Silo Won’t Survive AI - But Our Relationships Might
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-08-07 · eSchool News · News
America’s schools are moving quickly to respond to the rise of artificial intelligence, with parents, teachers, administrators, and lawmakers working to wrap their arms around what this means for student education.
Why this label for Schools are building AI rules before they know the destination
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: K-12 studentsOutcome: Behavior or engagementPeer-reviewed (journal reference listed)Vendor-linkedPerception only
2026-08-07 · arXiv (back catalog) · Study
Many AI tutors leverage large language models (LLMs) today. Given that LLMs are opaque black boxes, robust evaluation and live experimentation to measure the impact of every change are essential.
Why this label for Methodologies for Improving the Quality of AI Tutoring in K-12 Education
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 descriptive ceiling: product metrics and experiments are described without an explicit learner-assignment design or learning outcome.
- Grader B: R5 design ceiling: a vendor (Khan Academy) describes its metrics and unspecified experiments without design or sample details, so it is descriptive; R7 engagement-only cap also applies.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Motivation and engagement: matched 'student engagement'
- Tutoring and personalization: matched 'Tutoring'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-08-05 · EdSurge · News
One national study, one local leader, and a reality check on how schools are really regulating AI.
Policy and governance
Why this label for What Happens When AI Policy Meets a Real Classroom?
- Commentary or coverage: news summary of others' work; mentions 'study', graded C until the cited study is itself graded
- Policy and governance: matched 'AI Policy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-08-05 · arXiv (back catalog) · Study (preprint)
As generative AI permeates computing instruction, the emergent design challenge is to configure each tool's pedagogical role, authority, and accountability for the instructional work it performs. We argue for instructional governance by design: governance should be encoded in a teaching tool's interaction model,...
Assessment and gradingPolicy and governance
Why this label for Instructional Governance by Design: A Framework for AI in Computing Education
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 genre gate: the paper explicitly develops a position and governance framework.
- Grader B: R3 genre gate: a position paper and framework illustrated with case descriptions, with no learner outcome data.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-08-04 · EdSurge · News
A new report highlights a growing push for tools tailored specifically to the classroom.
Why this label for Teachers Forge Ahead on Integrating AI
- Commentary or coverage: news summary of others' work; mentions 'new report', graded C until the cited study is itself graded
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learners
2026-08-04 · arXiv (back catalog) · Study (preprint)
Large language models (LLMs) power educational applications from tutoring to essay scoring, but each is a point solution to a single task, and only recently have these point solutions been integrated into agents operating over a learning management system (LMS). Yet tutoring is long-horizon, since a learner improves...
Why this label for EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulate
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: the reported tutoring results come from simulated learners; the classroom check validates the simulation.
- Grader B: R4 real-learner gate: agent tutors are benchmarked on simulated learners; the live-classroom study only calibrates the simulator and reports no learning outcome from real learners.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'tutoring'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprint
2026-08-04 · arXiv (back catalog) · Study (preprint)
An emerging literature examines the critical question of when and how prediction can be useful in allocating scarce societal resources. We examine a novel variant of this question: What happens when predictive uncertainty differs systematically across the population?
Why this label for Scarcity and Predictive Uncertainty: Implications for Societal Resource Allocati
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 technical gate: a mathematical allocation model is illustrated with a dataset, without learner outcomes.
- Grader B: R4 real-learner gate: a mathematical allocation model illustrated on the PISA dataset, with no learner intervention or learning outcome.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-08-03 · EdSurge · News
A teacher created an app to fight cheating - and discovered a bigger takeaway.
Academic integrity
Why this label for I Used AI to Build AI-Resistant Assignments
- Commentary or coverage: news, commentary or policy with no study
- Academic integrity: matched 'cheating'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintVendor-linked
2026-08-01 · arXiv (back catalog) · Study (preprint)
MagicSchool's K-12 AI product suite is used by millions of teachers and serves millions of teacher and student messages each month. Our team monitors output along four priority dimensions--student safety, tone and instructional role, pedagogical value, and structural output quality--and tracks how often it fails on...
Assessment and grading
Why this label for When Evaluators Cry Wolf: Lessons from Production LLM-as-Judge Evaluation in Edu
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: the reported outcomes evaluate model judges on synthetic cases, not student learning.
- Grader B: R4 real-learner gate: LLM-as-judge evaluators are tuned on synthetic datasets, and the only outcome is evaluator precision; the authors are from the vendor, MagicSchool.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSmall or impreciseResearcher-made measure
2026-08-01 · arXiv (back catalog) · Study (preprint)
Generative AI tools increasingly produce educational materials: documents, slides, images, audio, and video, yet little is known about whether this content meets accessibility requirements. This paper presents a protocol for evaluating the accessibility of AI-generated educational materials against the Web Content...
Assessment and grading
Why this label for A Protocol for Evaluating the Accessibility of AI-Generated Educational Material
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 technical gate: an exploratory test scores AI-generated artifacts, with no learner outcome.
- Grader B: R4 real-learner gate: a protocol scoring the accessibility of AI-generated materials, using one model and one evaluator, with no learners.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Adults not in a learning role (raters)Outcome: Perception or attitudePreprintShort exposurePerception onlySmall or imprecise
2026-08-01 · arXiv (back catalog) · Study (preprint)
This innovative practice full paper presents BoilerSketch, a TA-supervised, diagram-first GenAI practice and tablet interface for providing structured visual explanations in CS1 and early CS2 support settings. Large early computing courses routinely face a support bottleneck during labs and office hours because many...
Why this label for BoilerSketch: A TA-Supervised, Diagram-First GenAI Practice for Structured Diagr
- Technical test: Tool paper; no learner outcomes reported.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: instructional staff evaluated a prototype; no learner outcomes were studied.
- Grader B: R5 design ceiling: one 45-minute expert evaluation with a post-use survey of 21 instructional staff, with no comparison group, so descriptive Level 1 and no learner outcome
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'tutoring'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Adults not in a learning role (raters)Outcome: Perception or attitudePreprintPerception onlySmall or imprecise
2026-08-01 · arXiv (back catalog) · Study (preprint)
This innovative practice full paper presents CodeStylist, a web application that provides course-standard-aware code style feedback for early undergraduate programming courses. CodeStylist addresses a common instructional gap: students are expected to follow local conventions for naming, formatting, comments,...
Why this label for CodeStylist: Supporting Early Undergraduate Programmers with Course-Aware Code S
- Technical test: Tool paper; formative review by instructional staff, who are not learners.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: instructional staff rated the tool; anticipated student learning was not measured learning.
- Grader B: R5 design ceiling: formative expert review survey of 18 staff from one course (accepted at FIE 2026), so descriptive Level 1 with only perceived-utility outcomes
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-07-27 · arXiv (back catalog) · Study (preprint)
Spaced repetition systems (SRS) have demonstrated robust effects on long-term retention, yet existing tools reduce the flashcard interaction to a single binary gesture: flip and self-rate. This impoverished interaction model fails to leverage decades of cognitive science evidence on retrieval practice, and requires...
Why this label for Memdora: Designing Cognitively-Grounded Flashcard Interactions for AI-Powered Sp
- Commentary: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R3 genre gate: the paper presents a system and design rationale but reports no evaluation of learners.
- Grader B: R3 genre gate: system design and rationale paper for a flashcard app, with no evaluation with learners reported
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Retrieval practice: matched 'Flashcard'
- Spacing and interleaving: matched 'Spaced Repetition'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 3: Controlled comparisonLearners: K-12 studentsOutcome: Learning or achievementPeer-reviewed (journal reference listed)Researcher-made measure
2026-07-26 · arXiv (back catalog) · Study
Climate change is a socio-scientific issue: it rests on science but cannot be settled by science, because any serious response forces people to weigh costs, values, and competing interests under uncertainty. Helping students make such decisions well is a central aim of science education, and the arrival of generative...
Assessment and grading
Why this label for Can AI-Assisted Inquiry Enhance Students' Decision-Making Skills in Socio-Scient
- Level 3: Controlled comparison: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: three student groups had pretest data and comparable starting levels; random assignment is not reported.
- Grader B: R5 design ceiling: three-group pretest-posttest quasi-experiment (270 secondary students, 90 per group, comparable baseline) gives Level 3; inter-coder agreement reported, so no R6 measurement downgrade; n>=100 so no imprecision downgrade; not an RCT, so not a Level 4 candidate
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePeer-reviewed (journal reference listed)
2026-07-24 · arXiv (back catalog) · Study
Students and working professionals have to go through the attendance process every day. Traditional methods of marking attendance using pen and paper or online platforms are human-intensive and time-consuming.
Why this label for ClassVision: AI-Powered Classroom Attendance System
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: the reported result concerns face-recognition model performance, not student learning.
- Grader B: R4 real-learner gate: compares face detection and recognition models for automated attendance; the only outcome is model performance
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Not reportedOutcome: Model performancePreprintSmall or imprecise
2026-07-24 · arXiv (back catalog) · Study (preprint)
Most educational technology for children is built around visual interfaces, which excludes the many children worldwide who live with visual impairment -- an estimated 1.4 million children are blind and many more have low vision. We present Kutti AI, a voice-first learning companion designed so that audio is the...
Why this label for Kutti AI: A Voice-First, Offline-Capable Learning Companion with Real-Time Strug
- Technical test: Prototype with no evaluation with target children.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: the hackathon prototype has no reported evaluation with its intended child learners.
- Grader B: R5 design ceiling: hackathon prototype with informal qualitative observations and no evaluation with target children yet, so descriptive or pilot Level 1
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'adaptive learning'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 3: Controlled comparisonLearners: Higher-ed studentsOutcome: Learning or achievementPreprint
2026-07-23 · arXiv (back catalog) · Study (preprint)
The availability of generative AI (GenAI) has raised concerns that college students substitute GenAI for cognitive effort, earning high grades without learning. If this "GenAI substitution hypothesis" is true, grades should rise disproportionately in GenAI-susceptible courses--those relying more on assessments like...
Why this label for Generative AI Availability, Grades, and Student Satisfaction at a Large Universi
- Level 3: Controlled comparison: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the difference-in-differences analysis compares courses before and after AI availability using baseline administrative data.
- Grader B: R5 design ceiling: difference-in-differences quasi-experiment on administrative data (138,386 students) with a comparison and a pre-period gives Level 3; R7 preprint cap is 3; grades are a proxy for learning, so review for R6 indirectness
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Cognitive load: matched 'cognitive effort'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Not reportedOutcome: Perception or attitudePreprintSmall or imprecise
2026-07-23 · arXiv (back catalog) · Study (preprint)
Teachers, conference chairs, and public readers all judge writing from limited evidence, seeing only a finished document and not the process that produced it. Final text alone cannot reveal whether a document was produced through human typing, AI generation, or mixed human-AI collaboration.
Why this label for Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Wri
- Technical test: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: the user study and typing-detector test report usability and detection results, not learner outcomes.
- Grader B: R5 design ceiling: system paper with an unspecified user study (helpfulness ratings) plus a technical red-team test, so descriptive Level 1
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-07-22 · arXiv (back catalog) · Study (preprint)
Generative AI is changing a basic premise of educational assessment: that submitted work can reliably evidence the human capacities a credential claims to certify. The challenge is not simply whether students use AI, but what remains inferable about learning when some cognitive work has been delegated to a system.
Assessment and grading
Why this label for What Does the Credential Still Certify? Cognitive Stewardship for AI-Mediated Ed
- Commentary: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R3 genre gate: the paper develops an assessment framework and audits university policies rather than studying learners.
- Grader B: R3 genre gate: conceptual framework plus an LLM-coded audit of 30 university policy documents; no learners, so no learner-level evidence
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Behavior or engagementPreprintSmall or impreciseShort exposure
2026-07-22 · arXiv (back catalog) · Study (preprint)
Context: Secondary studies are fundamental practices in Evidence- Based Software Engineering, but teaching them requires activities that expose students to authentic methodological decisions. Objective: This paper reports an experience in a graduate course in which ten doctoral students in Software Engineering,...
Why this label for Observing the Conduct of Systematic Reviews with Generative AI Support: An Exper
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the ten-student, single-day experience report is observational; group activities do not establish a controlled comparison.
- Grader B: R5 design ceiling: observational experience report from one single-day session with 10 doctoral students, so descriptive Level 1
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePeer-reviewed (journal reference listed)Perception only
2026-07-22 · arXiv (back catalog) · Study
The rise of generative AI (GenAI) in higher education has prompted urgent debates surrounding academic integrity and ethical use. This study examines cross-cultural differences in student perceptions of GenAI use, comparing responses from students at Canadian and South Korean universities.
Academic integrityPolicy and governance
Why this label for Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generat
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the scenario survey compares student judgments but reports no controlled intervention or statistical controls.
- Grader B: R5 design ceiling: cross-sectional scenario-based survey comparing Canadian and Korean students' perceptions, so descriptive Level 1 with a perception-only outcome
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Higher-ed studentsOutcome: Model performancePreprintOpen data or materials
2026-07-21 · arXiv (back catalog) · Study (preprint)
This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs). TTXs enable learner teams to prepare for workplace tasks and practice crisis responses, such as resolving cybersecurity incidents.
Assessment and grading
Why this label for Assessment in Team Problem-Solving Exercises in Computing Education
- Technical test: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: student team records are used to test assessment methods; the reported outcome is method performance.
- Grader B: R4 real-learner gate: data from 81 participants is used only to check clustering and LLM scoring against instructor scores; the only outcome is how well the methods perform, with no learner outcome (accepted at FIE 2026, peer-reviewed version not matched)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-07-18 · arXiv (back catalog) · Study (preprint)
Academic peer review is under mounting strain: NeurIPS 2025 received 21,575 submissions, ICLR 2025 received 11,603, and ICML 2025 received 12,107. This volume has outpaced the supply of qualified reviewers, and large language models (LLMs) are already filling the gap, largely undisclosed.
Why this label for Local AI pre-screening for human triple-blind peer review in health sciences
- Commentary: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R3 genre gate: proposes a peer-review framework without evaluating it.
- Grader B: R3 genre gate: proposes a peer-review pre-screening framework with no study; it only cites someone else's benchmark.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Learning or achievementPreprintSmall or impreciseResearcher-made measureShort exposure
2026-07-17 · arXiv (back catalog) · Study (preprint)
Generative AI (GenAI) is increasingly used by students for programming explanation, debugging, and assignment support. Yet unrestricted large language model (LLM) tutors can hallucinate, contradict course policy, reveal complete solutions, and foster passive dependence.
Why this label for EduGuard: A Safe RAG-Based LLM Tutor for Programming Education
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 controlled pilot with 10 learners has a Level 2 ceiling; R6 imprecision lowers it to Level 1.
- Grader B: R4 mixed study graded only on the real-learner arm. That arm is a counterbalanced pilot with 10 undergraduates and an immediate post-test only, and pilots are Level 1 under sec. 6. The benchmark part would be T.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'Tutor'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: TeachersOutcome: Perception or attitudePreprintSmall or imprecise
2026-07-17 · arXiv (back catalog) · Study (preprint)
While generative AI tools are directly changing how undergraduate computer science is learned and taught, they are also reshaping the relationships between instructors and students. In contrast to existing tool-oriented research on how instructors view and adopt AI, this study investigates how instructors think about...
Assessment and gradingPolicy and governance
Why this label for To Police or to Guide: How Higher Education Computer Science Instructors Design
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: 13 instructor interviews are qualitative evidence.
- Grader B: R5 design ceiling: qualitative study of 13 semi-structured instructor interviews, which is descriptive (Level 1).
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Model performancePreprintSimulated learnersSmall or imprecise
2026-07-15 · arXiv (back catalog) · Study (preprint)
This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tutoring agent combining course-specific Retrieval-Augmented Generation (RAG) with structured Knowledge Component (KC) models across integrated Chat, Tutor, and Quiz...
Why this label for Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Eval
- Level 1: Descriptive or exploratory: Change to 1. First real classroom deployment with 8 students plus simulation; graded on the real-learner arm as descriptive.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: despite a classroom deployment with eight students, the reported evaluations measure system performance rather than learner outcomes.
- Grader B: R4 mixed study graded only on the real-learner arm, which is a descriptive classroom deployment with 8 students and no comparison group (Level 1). The RAGAS and simulation parts would be T.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'Tutoring'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-07-14 · eSchool News · News
In an era defined by rapid technological advancement and an unprecedented explosion of information, public education stands at a pivotal and exciting crossroads.
Why this label for The best time to be a teacher: Leading with AI and technology in the age of pers
- Commentary or coverage: news, commentary or policy with no study
- Tutoring and personalization: matched 'personalized learning'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprint
2026-07-14 · arXiv (back catalog) · Study (preprint)
The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data. Previously time-consuming and labor-intensive tasks can be completed much more efficiently with the use of AI.
Why this label for Analyzing Curricular Pattern Complexity Using AI to Improve On-Time Graduation R
- Technical test: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: evaluates LLM analysis of curricula without reporting student outcomes.
- Grader B: R4 real-learner gate: LLMs analyze curriculum structures and suggest revisions. No learners took part and no graduation outcome was measured.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Not reportedOutcome: Teacher practicePreprint
2026-07-14 · arXiv (back catalog) · Study (preprint)
With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions. In computer science (CS) courses GenAI adoption is especially high and the implications for student learning...
Policy and governance
Why this label for A Comparative Analysis of Institutional and Course Generative AI Policies within
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: secondary analysis of policy and syllabus documents is descriptive.
- Grader B: R5 design ceiling: descriptive secondary document analysis of institutional and course AI policies, with no learner outcomes.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 2: CorrelationalLearners: K-12 studentsOutcome: Learning or achievementPreprintShort exposureResearcher-made measure
2026-07-14 · arXiv (back catalog) · Study (preprint)
This study proposes a semi-automated system for generating dialogue-based lessons using Large Language Models (LLMs) and Text-to-Speech (TTS) technology, and exploratorily examines its educational potential via a practical quasi-experiment. The system augments rather than replaces educators through a three-stage...
Why this label for A Semi-Automated System for Generating Dialogue-Based TTS Lessons Using Large La
- Level 2: Correlational: Change to 2. Within-subject comparison of three lesson formats with 245 high school students; no baseline equivalence or counterbalancing reported, so 2.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 quasi-experiment lacks an equivalent baseline comparison because lesson formats were given in fixed order with different content.
- Grader B: R5/R6: a fixed-order within-subject quasi-experiment with 245 high school students. Format is confounded with content and there is no equivalent comparison, so the ceiling is Level 2. The measurement downgrade (researcher-made outcomes with no reliability reported) lowers it to Level 1.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Cognitive load: matched 'cognitive load'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learners
2026-07-10 · arXiv (back catalog) · Study (preprint)
Social desirability and impression management are pervasive sources of response distortion in human personality assessment, yet their effects on Large Language Models (LLMs) remain underexplored. This study investigates whether contemporary LLMs systematically modulate the expression of Dark Triad traits...
Why this label for Faking Good and Faking Bad in LLMs: Response Distortion Across Dark Triad Person
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: all experimental responses come from LLMs.
- Grader B: R4 real-learner gate: psychometric prompting of seven LLMs, with no human participants.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Adults not in a learning role (raters)Outcome: Model performancePreprintOpen data or materials
2026-07-09 · arXiv (back catalog) · Study (preprint)
Despite rapid AI adoption in education, rigorous evaluation of AI-powered educational (AIED) systems remains critically underdeveloped, particularly in second language (L2) education, one of the most common yet least evaluated AI applications. We introduce L2-Bench, an open-source benchmark of 1,000+ task-response...
Assessment and gradingPolicy and governance
Why this label for L2-Bench: An Evaluation Benchmark for Measuring LLM Capabilities in Second Langu
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: expert validation supports a benchmark whose measured outcome is LLM performance.
- Grader B: R4 real-learner gate: an LLM benchmark for L2 education tasks. The experts only validated the taxonomy, and no learners took part.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-07-09 · arXiv (back catalog) · Study (preprint)
Teaching computer science at universities is often structured rather classically and theory oriented. The former refers to "transmission"-style lectures accompanied by exercises which are submitted and graded manually, providing delayed feedback (if any).
Why this label for DOMtutor: Automated Autograding for Logic in Computer Science
- Commentary: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R3 genre gate: proposes an autograding approach without reporting an evaluation.
- Grader B: R3 genre gate: proposes using DOMjudge autograders for theory courses, with no evaluation reported.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Adults not in a learning role (raters)Outcome: Perception or attitudePreprint
2026-07-09 · arXiv (back catalog) · Study (preprint)
Europe faces a critical "translation gap" where doctoral excellence in academia often fails to convert into industrial impact. While Industry 5.0 demands a blend of technical depth, sustainability, and human-centric design, traditional higher academic education remains siloed.
Policy and governance
Why this label for From Thesis to Transition: An INSIGHT-Inspired Approach to Co-Designing Industry
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: expert interviews and co-design workshops provide exploratory evidence.
- Grader B: R5 design ceiling: qualitative co-design from expert interviews and workshops, which is descriptive with no learner outcomes.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryLearners: Higher-ed studentsOutcome: Not reportedPreprint
2026-07-08 · arXiv (back catalog) · Study (preprint)
We present documentation of a classroom assignment that teaches runtime monitoring through a creative embedded systems build: an interactive Pachinko game. The assignment centers on a dual-core ESP32 workflow in which students write RTLola specifications for monitors, compile these monitors to C, and deploy them...
Why this label for Experiential Learning of Runtime Monitoring Using Pachinko
- Commentary: Assignment documentation with no study.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: documents a classroom assignment without reporting a measured learning outcome.
- Grader B: R3 genre gate: documentation of a classroom assignment with no data or evaluation of student outcomes.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: K-12 studentsOutcome: Learning or achievementPreprintSmall or impreciseResearcher-made measure
2026-07-07 · arXiv (back catalog) · Study (preprint)
This paper presents the design and outcomes of a seven-weekend AI storytelling program developed for Black girls aged 10-12. Grounded in Afrofuturism and Black feminist thought, the program adopted AI-enabled counter-storytelling, supported the development of foundational AI literacies, and fostered future-oriented...
Policy and governance
Why this label for What If AI Carried Her Imagination? Black Girls as Creators in an AI Storytellin
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: learner artifacts come from a case study without a comparison group.
- Grader B: R5 design ceiling: qualitative case study of a weekend program for girls aged 10-12, based on analysis of learner artifacts, with no comparison group
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryLearners: Higher-ed studentsOutcome: Behavior or engagementPeer-reviewed (journal reference listed)Report (grey literature)
2026-07-06 · arXiv (back catalog) · Study
This paper examines the psychological determinants that shape academically honest and dishonest uses of generative artificial intelligence (GenAI) in higher education. Rather than treating academic misconduct as a purely technological problem, the study conceptualizes academic integrity as a psychologically mediated...
Academic integrity
Why this label for Psychological Determinants of Academic Integrity in the Use of Generative AI in
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 genre gate: the paper is a narrative review and conceptual synthesis.
- Grader B: R3 genre gate: self-described focused narrative review and conceptual synthesis of a purposive corpus of 16 sources, not a systematic review
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-07-05 · Carl Hendrick (learning science) · Commentary
Why minimally guided AI does not work. Chatbots, novices, the automation of learning and the case for Alpha School
Why this label for Children of the Magenta Line
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Learning or achievementPreprintSmall or impreciseShort exposureResearcher-made measure
2026-07-04 · arXiv (back catalog) · Study (preprint)
Writing is cognitively demanding and anxiety-provoking for English as a Foreign Language (EFL) learners, especially under time pressure. This paper presents The Brand War, a web-based gamified writing application combining competitive game mechanics with iterative GPT-4.1-powered formative feedback for undergraduate...
Why this label for The Brand War: A Gamified AI-Feedback System for Time-Limited EFL Writing
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the 29-student exploratory, single-session study lacks an equivalent comparison group.
- Grader B: R5 design ceiling: exploratory single-session, single-group within-student study (n=29) with AI-scored revisions; user vs non-user comparison is self-selected and not significant
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Feedback: matched 'formative feedback'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Learning or achievementPeer-reviewed (journal reference listed)Small or imprecise
2026-07-03 · arXiv (back catalog) · Study
While Large Language Models (LLMs) can provide personalized support in learning, several studies have raised concerns regarding their use in education. Importantly, learning depends on how students engage with LLMs.
Why this label for Reflective Dialogue or Prompt Refinement? Effects of Tutor Scaffolding on Studen
- Level 1: Descriptive or exploratory: Two-condition course study, 66 then 52 students, no baseline equivalence, n under 100; 1.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R6 imprecision: the comparison reports no interval or power information and has fewer than 100 learners.
- Grader B: R5 two-tutor comparison over 6 weeks with learning gains (Level 3 ceiling; random assignment not stated), lowered one level by R6 imprecision (n=66, no interval reported)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'Tutor'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Teacher practicePreprint
2026-07-02 · arXiv (back catalog) · Study (preprint)
Generative artificial intelligence (GenAI) has challenged the validity of unsupervised online assessment, especially in technical subjects where plausible answers can be produced with little effort. This paper reports lessons from designing and implementing an AI-aware, AI-testing assessment in a large second-year...
Assessment and grading
Why this label for Assessment Design in the GenAI Era: The X1-X2-X3 Assessment Pattern for Testing
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: this is an assessment-design account without measured comparative learning gains.
- Grader B: R5 design ceiling: design case report of an assessment method in one module; explicitly makes no claim of measured learning gains
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: K-12 studentsOutcome: Learning or achievementPreprintSmall or impreciseShort exposure
2026-06-26 · arXiv (back catalog) · Study (preprint)
Generative AI systems (GenAI) are increasingly used by students as learning companions, yet little is known about how they regulate these tools in open-ended learning settings, where the goal is to improve understanding for later independent performance. This study examined Grade-9 students' use of a general-purpose...
Why this label for Changes in Help-Seeking Strategies Predict unaided Performance during AI-based M
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R6 imprecision: the controlled association uses 89 learners with computable trajectories and reports no interval or power information.
- Grader B: R5 correlational design with pre-test control (Level 2 ceiling) on Grade-9 students, lowered one level by R6 imprecision (analytic n=95/89, no interval reported)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Metacognition and self-regulation: matched 'self-regulated'
- Tutoring and personalization: matched 'tutor'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePreprintSmall or imprecise
2026-06-26 · arXiv (back catalog) · Study (preprint)
Large language models can complete most of the assignments in an introductory artificial intelligence course. This paper is an experience report on redesigning one such course, CSS~382 at the University of Washington Bothell, in response.
Why this label for Teaching Intro AI When the Tools Can Do the Homework: A Course Redesign and a St
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the single-cohort course experience report describes student artifacts and objections without a comparison group.
- Grader B: R5 design ceiling: single-cohort experience report on a course redesign, with no outcome measurement or comparison
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: K-12 studentsOutcome: Learning or achievementPreprintResearcher-made measure
2026-06-26 · arXiv (back catalog) · Study (preprint)
Research indicates students require customized written composition feedback to enhance critical thinking and writing competence, yet teachers face barriers to delivering timely personalized guidance due to heavy workloads. To address this gap, this study developed the Writing Improvement and Smart Evaluation Agent...
Why this label for Using AI-Generated Feedback to Improve Critical Thinking and Writing Proficiency
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: three-month single-group intervention; no comparison group reported.
- Grader B: R5 design ceiling: a three-month intervention with 260 sixth graders, but no comparison group is described, so it is a single-group pre/post (Level 1)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Feedback: matched 'AI-Generated Feedback'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 2: CorrelationalLearners: K-12 studentsOutcome: BehaviorPreprint
2026-06-25 · arXiv (back catalog) · Study (preprint)
Generative AI (GAI) creates new opportunities for collaborative problem-solving (CPS), yet its role in shaping student interaction remains unclear. To address this gap, we conducted a six-week quasi-experimental study with 201 fifth-grade students in two conditions: with and without GAI.
Why this label for Beyond Information Retrieval: Generative AI as an Epistemic Arbiter to Enhance C
- Level 2: Correlational: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: quasi-experimental comparison without reported baseline equivalence; R7 caps engagement outcomes at 2.
- Grader B: R7 cap: a six-week quasi-experiment with 201 fifth graders and a control group, but the only outcome is collaborative behavior and no baseline data is reported, so behavior/engagement-only is capped at Level 2
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: BehaviorPreprintPerception only
2026-06-23 · arXiv (back catalog) · Study (preprint)
The rise of open and distance education has made it more important than ever to have communication tools that are simple, flexible, and good for helping students work together, talk to each other, and feel connected. Researchers have already looked at how instant messaging apps like WhatsApp and Telegram can be used...
Why this label for A Qualitative Comparative Study of Communication in Higher Distance Education
- Level 1: Descriptive or exploratory: Change to 1. Qualitative comparative case study of distance-education students; descriptive real-learner evidence.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R3 genre gate: comparative discussion extends a prior case study without reporting a new systematic study.
- Grader B: R5 design: a qualitative case study and comparative discussion of Viber use at a distance university, which is descriptive (Level 1)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Higher-ed studentsOutcome: Model performancePreprint
2026-06-22 · arXiv (back catalog) · Study (preprint)
Large language models (LLMs) are increasingly used for estimating item difficulty in educational assessment. However, it remains unclear whether such estimates reflect how learners actually experience difficulty.
Why this label for The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: student responses serve as reference data; the evaluated outcome is LLM difficulty-rating performance.
- Grader B: R4 real-learner gate: the only outcome is how well LLM difficulty ratings match item difficulty; student responses (n=770) are only the ground truth
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Level 2: CorrelationalLearners: Higher-ed studentsOutcome: Learning or achievementPreprintSmall or imprecise
2026-06-21 · arXiv (back catalog) · Study (preprint)
Pretesting - attempting problems before instruction - supports learning by activating prior knowledge and sharpening attention to subsequent instruction. Recent work suggests that adaptive AI-assisted pretesting can yield further advantages, particularly for tasks requiring higher-order reasoning, yet it remains...
Why this label for Do Gains from Generative AI-Enabled Adaptive Pretesting Persist? Evidence from a
- Level 2: Correlational: Randomized retention study, but sample size not reported; held at 2 until n is verified.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: undergraduates were randomly assigned to study conditions and assessed on posttest performance.
- Grader B: R5 RCT (undergraduates randomly assigned, seven-week retention, learning outcome) gives Level 3; R6 imprecision lowers it to 2 because n and intervals are not reported (RCT without n, needs human review)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Retrieval practice: matched 'retrieval practice'
- Spacing and interleaving: matched 'spaced retrieval'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePreprintPerception only
2026-06-18 · arXiv (back catalog) · Study (preprint)
The rapid integration of generative artificial intelligence (AI) has reshaped the landscape of higher education. Students have embraced tools such as ChatGPT with striking speed, while teaching staff and institutions have responded with greater caution.
Academic integrity
Why this label for From Novelty to Normalisation: Tracking Changing Perceptions of AI in Higher Edu
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: longitudinal survey tracks reported attitudes and use without an intervention comparison.
- Grader B: R5 design: a repeated survey (n=1,665) of perceptions and reported use, which is descriptive (Level 1)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: K-12 studentsOutcome: Model performancePreprint
2026-06-17 · arXiv (back catalog) · Study (preprint)
Personalised learning systems often assume that mathematical ability is combined of discrete abilities, acquired sequentially and dependent upon first acquiring foundational abilities, and students often report different strengths. In this work, we explore the validity of these assumptions by applying clustering...
Why this label for Archetypes or ability? Clustering for modelling student mathematical competence
- Level 1: Descriptive or exploratory: Change to 1. Secondary analysis of exam data from 119,034 real students; descriptive and modelling study, not a model benchmark.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: exam results train and benchmark predictive models; no learner outcome is evaluated.
- Grader B: R5 design: exploratory clustering of exam data from 119,034 students with no comparison or intervention, so it is descriptive (Level 1)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'Personalised learning'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 2: CorrelationalLearners: Not reportedOutcome: Learning or achievementPreprintSmall or imprecise
2026-06-16 · arXiv (back catalog) · Study (preprint)
Motivational factors such as self-efficacy and how favorably students feel toward practice play a crucial role in shaping learning, particularly in technology-supported environments. Yet, educational interventions often overlook how these factors interact with practice format.
Why this label for Self-Efficacy and Favorability Shape Learning from Tutoring Systems and Paper Pr
- Level 2: Correlational: Counterbalanced within-subject design with learning outcome; no sample size or interval reported, so held at 2.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: counterbalanced within-subject comparison uses matched problem sets and baseline self-efficacy, but Level 4 checks are unavailable.
- Grader B: R5: the headline finding is a moderation link between self-efficacy or favorability and learning gains (correlational, Level 2) inside a counterbalanced within-subject ITS vs paper comparison; n and intervals are not reported, so it stays at 2 pending review (accepted at EC-TEL 2026, not matched)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Motivation and engagement: matched 'students' motivation'
- Tutoring and personalization: matched 'Tutoring'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprint
2026-06-15 · arXiv (back catalog) · Study (preprint)
Large language models are increasingly proposed as educational tutors, yet stronger task-solving ability does not necessarily imply stronger learning support. Motivated by recent calls to measure the social impact of NLP systems in practice, we study whether public LLM tutoring benchmarks distinguish...
Why this label for Beyond Helpfulness: A Teaching-over-Solving Diagnostic for Measuring Educational
- Technical test: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: diagnostic compares model benchmark scores without learner outcomes.
- Grader B: R4 real-learner gate: secondary analysis of tutoring benchmark leaderboards; the only outcome is model performance
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'Tutors'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePreprintPerception only
2026-06-13 · arXiv (back catalog) · Study (preprint)
Peer grading is widely used in education, yet it elicits mixed reactions from educators and students. Although many studies have examined students' views of peer grading, their findings are scattered, and no clear overall picture has emerged.
Assessment and grading
Why this label for Students' Perceptions of Peer Grading
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: thematic analysis of papers and student discussions is descriptive and reports perceptions, not measured learning.
- Grader B: R5 design ceiling: qualitative thematic analysis of 107 papers plus Reddit posts about students' views of peer grading, with no stated systematic search method; perception only (accepted at ECTEL 2026, not matched)
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Higher-ed studentsOutcome: Model performancePeer-reviewed (journal reference listed)
2026-06-12 · arXiv (back catalog) · Study
Educational chatbots powered by large language models (LLMs) show promising effects on learning outcomes, yet most systems delegate pedagogical decisions such as content selection and didactic structuring implicitly to the LLM, making tutoring strategies difficult to trace, evaluate, and reproduce. This paper presents...
Why this label for A didactical-driven teacher assistant for a dimensional modeling course
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: authentic student questions are test inputs, while the outcome is system detection precision.
- Grader B: R4 real-learner gate: student questions are only test inputs; the only outcome is the system's detection precision
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'tutoring'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprint
2026-06-12 · arXiv (back catalog) · Study (preprint)
Large Language Model (LLM) based AI educational content generation systems are increasingly being developed, yet no standardised benchmark exists to systematically evaluate them. This study introduces LessonBench-V1, a benchmark dataset comprising 647 human-written lessons paired with LLM-based reverse-engineered...
Why this label for LessonBench-V1: A Benchmark Dataset for Evaluating AI Lesson Generation Agents
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: lesson-generation benchmark has no participating learners or learning outcome.
- Grader B: R4 real-learner gate: a benchmark dataset for lesson-generation agents with no learners
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-06-08 · Carl Hendrick (learning science) · Commentary
The most-cited AI-in-education paper gets retracted, retrieval practice hits a wall under high cognitive load, and a little knowledge turns out to be worse than none at all.
Why this label for The Monthly Dispatch - What's New in Learning Science? - June 2026
- Commentary or coverage: news, commentary or policy with no study
- Retrieval practice: matched 'retrieval practice'
- Cognitive load: matched 'cognitive load'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-06-05 · eSchool News · News
As I wrapped up my student conferences, one conversation stuck with me. Steven had barely touched his final project for our computer science course, a virtual simulation of a piano, despite showing real promise earlier in the year.
Why this label for My students need real connection, not AI feedback
- Commentary or coverage: news, commentary or policy with no study
- Feedback: matched 'AI feedback'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
No study citedLearners: Not reportedOutcome: Not reportedPreprint
2026-06-05 · arXiv (back catalog) · Study (preprint)
Critique is central to design education and project-based learning, yet high-quality critique is often scarce, uneven, hard to document, and disconnected from evolving artefacts. We present Critsly, an artefact-aware AI critique workspace that turns AI from a detached feedback tool into a critique teammate in the...
Why this label for Critsly: An Artefact-Aware AI Critique Teammate for Design Education and Project
- No study cited: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R1 source gate: product demo describes features but cites no evaluation study.
- Grader B: R1 source gate: a demo describing a tool, with no evaluation or study of learners
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
No study citedLearners: Not reportedOutcome: Not reportedPreprint
2026-06-05 · arXiv (back catalog) · Study (preprint)
Teachers and researchers need to adapt educational AI to local goals, but most systems remain difficult to customize or study without coding expertise. We present LearnAdapt Agentic Studio on PedOS 1.1 Lumina, a no-code authoring and governed runtime environment for educational AI plugins.
Why this label for From Tools to Teacher-Built Teammates: No-Code Pedagogical Plugin Authoring with
- No study cited: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R1 source gate: product demo describes a workflow but cites no evaluation study.
- Grader B: R1 source gate: a demo describing an authoring platform, with no evaluation or study
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-06-02 · eSchool News · News
In the second week of January, a senior mathematics teacher with 22 years in the classroom raised a hand at the end of a staff meeting and asked a question that changed the way I now design AI literacy work for entire faculties.
Why this label for How districts can build a shared AI structure
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-05-30 · Carl Hendrick (learning science) · Commentary
AI tutoring will work when it stops pretending to be human
Why this label for Spandrels of the Mind: The Case Against Humanlike AI
- Commentary or coverage: news, commentary or policy with no study
- Tutoring and personalization: matched 'tutoring'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-05-27 · eSchool News · News
The overreliance on AI is a widely discussed topic for teachers, administrators, and families alike. The last thing we want is for technology to stifle the creativity, expertise, and human connection that educators bring to the classroom or hinder our students’ ability to think critically.
Why this label for How AI helps teachers spend less time on assessments and more time on impactful
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-05-20 · eSchool News · News
There’s never been a more turbulent time for young people to plan for and embark on their futures, and a new survey gives insights on their feelings and plans.
Why this label for Despite concerns, Gen Z students are optimistic about AI
- Commentary or coverage: news summary of others' work; mentions 'survey', graded C until the cited study is itself graded
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-05-14 · eSchool News · News
AI is a daily reality in the nation's schools, and in Illinois, it shapes how students research, problem-solve, and create. Now, Teach Plus Illinois and the Illinois Digital Educators Alliance (IDEA) are releasing “From ‘Rules and Tools’ to Schools,” a follow-up to the 2024 report that first sounded the alarm on AI's...
Why this label for In Illinois, charting a path for responsible AI use
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePeer-reviewed (journal reference listed)
2026-05-14 · arXiv (back catalog) · Study
Generative AI increasingly supports educational design tasks, e.g., through Large Language Models (LLMs), demonstrating the capability to design assessment questions that are aligned with pedagogical frameworks (e.g., Bloom's taxonomy). However, they often rely on subjective or limited evaluation methods; focus...
Why this label for Small, Private Language Models as Teammates for Educational Assessment Design
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: the study evaluates model-generated assessment questions and judging, with no learners.
- Grader B: R4 real-learner gate: LLM/SLM question generation judged by models and expert ratings; human raters judging model outputs counts as T
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprint
2026-05-13 · arXiv (back catalog) · Study (preprint)
As large language models (LLMs) are increasingly used by students to generate natural language responses and program code, there is growing interest in whether LLMs themselves can be used to distinguish AI-generated work from human-authored submissions. In this paper, we investigate the extent to which LLMs can detect...
Why this label for Distinguishing Artificial from Authentic: Evaluating LLMs for Detecting LLM-Gene
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: authentic student responses are test data for model detection, not a real-learner arm.
- Grader B: R4 real-learner gate: student responses are used only as data; the only outcome is LLM detection accuracy
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-05-12 · eSchool News · News
AI plays a supportive educational role for nearly 70 percent of top-performing math students asked about their study habits, according to a new survey.
Why this label for In a new survey, AI scores high as a math learning tool
- Commentary or coverage: news summary of others' work; mentions 'survey', graded C until the cited study is itself graded
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learners
2026-05-12 · arXiv (back catalog) · Study (preprint)
Large language models (LLMs) can fluently generate student-like responses, making them attractive as simulated students for training and evaluating AI tutors and human educators. Yet such simulators are typically evaluated by output similarity to real students, not by whether they behave like students with coherent...
Why this label for Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulnes
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: all tested students are LLM simulators.
- Grader B: R4 real-learner gate: an evaluation and post-training of LLM simulated students, with no real learners
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'tutors'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprint
2026-05-12 · arXiv (back catalog) · Study (preprint)
Automatic generation of educational materials using large language models (LLMs) is becoming increasingly common, but assigning difficulty levels to such materials still requires substantial human effort. LLM-as-a-Judge has therefore attracted attention, yet disagreement with human raters remains a major challenge.
Why this label for Predicting Disagreement with Human Raters in LLM-as-a-Judge Difficulty Assessmen
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: experiments assess model difficulty ratings against human raters, with no learners.
- Grader B: R4 real-learner gate: predicts LLM-judge disagreement with human difficulty ratings; the outcome is model AUC only; EDM poster acceptance not matched
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintOpen data or materials
2026-05-10 · arXiv (back catalog) · Study (preprint)
Large language models are increasingly used in K-12 education, but existing benchmarks mainly test exam question answering rather than understanding how curriculum knowledge is structured and visually presented. We call this capability curriculum cognition.
Assessment and grading
Why this label for K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training E
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: evaluates educational LLMs on curriculum benchmarks, not K-12 students.
- Grader B: R4 real-learner gate: curriculum knowledge-graph benchmark and fine-tuning corpus for LLMs; K-12 content but no learners took part
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprint
2026-05-08 · arXiv (back catalog) · Study (preprint)
Automated short answer scoring (ASAS) is shifting from discriminative, fine-tuned models to large language models (LLMs) used in few-shot settings. This paradigm leverages LLMs broad world knowledge and ease of deployment, but limited task-specific data may reduce alignment on complex scoring tasks.
Why this label for Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degra
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: student answers are existing test data for automated scoring, not a real-learner arm.
- Grader B: R4 real-learner gate: automated scoring agreement on existing student responses; the outcome is model-human agreement, not learner outcomes
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Level 1: Descriptive or exploratoryLearners: Adult learnersOutcome: Learning or achievementPreprintSmall or impreciseShort exposure
2026-05-07 · arXiv (back catalog) · Study (preprint)
Advance in technology offer the potential for future adoption of a combination of virtual reality (VR) and real-time adaptivity to enhance training and education. Providing a valid neuro-ergonomic measure of cognitive load can enable an adaptive training regime to continuously adjust tas difficulty to an optimal level...
Why this label for Leveraging fNIRS to Evaluate Workload for Adaptive Training in Virtual Reality
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: 36 people complete one training task and retention test, with no comparison group reported.
- Grader B: R5 design ceiling: single-session study validating an fNIRS workload measure (n=36); the outcome is the cognitive-load measure, not a learning comparison
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Cognitive load: matched 'cognitive load'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-05-06 · Carl Hendrick (learning science) · Commentary
New evidence on the limits of retrieval practice, phone bans that don't move test scores, ChatGPT as cognitive crutch, and why early vocabulary growth tells us surprisingly little about later reading
Why this label for The Monthly Dispatch - What’s New in Learning Science - May 2026
- Commentary or coverage: news, commentary or policy with no study
- Retrieval practice: matched 'retrieval practice'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-05-05 · eSchool News · News
In many schools, AI is being handled through individual teacher decisions rather than a shared structure. That makes sense in the short term.
Why this label for When AI means something different in every classroom
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePeer-reviewed (journal reference listed)
2026-05-04 · arXiv (back catalog) · Study
Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale. Building on previous work that employed traditional machine learning to classify text ("So Many Responses, So Little Time: A Machine-Learning Approach to Analyzing Open-Ended Survey Data") [1], this study investigates...
Why this label for So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional M
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: student survey responses are test data for text classifiers; the measured outcome is model performance.
- Grader B: R4 real-learner gate: LLM vs ML classification of NSSE survey text; the outcome is classification accuracy only; DOI to a journal version
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-04-28 · eSchool News · News
AI is rapidly reshaping education, but not always in ways that support learning. A growing number of AI tools promise to “help” students by doing assignments, writing papers, solving problem sets, or even completing exams automatically.
Assessment and grading
Why this label for When AI does the work, who does the learning?
- Commentary or coverage: news, commentary or policy with no study
- Assessment and grading: matched 'exams'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-04-26 · arXiv (back catalog) · Study (preprint)
Generative Artificial Intelligence (GenAI) presents a governance challenge for STEM assessment. Unrestricted access can enable task outsourcing that undermines the validity of traditional assessments, while blanket prohibitions are difficult to enforce, may drive use underground, and do little to prepare students for...
Policy and governance
Why this label for Positioning Generative Artificial Intelligence in STEM Assessment: When to Requi
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 genre gate: the proposed STEM assessment framework is illustrated with examples, not tested with learners.
- Grader B: R3 genre gate: conceptual ECD-based governance framework for GenAI in STEM assessment, illustrated with physics examples, with no learner data.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-04-24 · eSchool News · News
AI is here, and it’s moving fast. For schools, that speed is both an opportunity and a risk: The right tools can transform learning, but the wrong ones can compromise data, equity, and instructional goals.
Why this label for From innovation to impact: Three ways school districts can build a sustainable A
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-04-22 · eSchool News · News
Some might worry that the introduction of AI tools in the English classroom will simply lead to more cheating and even worse literacy rates, leaving students unprepared for college and careers that demand strong writing and communication skills.
Why this label for 3 ways students can use AI tools to improve their literacy skills
- Commentary or coverage: news, commentary or policy with no study
- Reading and writing: matched 'literacy'
- Academic integrity: matched 'cheating'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-04-17 · eSchool News · News
Recent findings on the negative impacts of AI on learning might be sparking national debate, but they are unsurprising to learning scientists.
Why this label for We can’t wait for another Mississippi Miracle
- Commentary or coverage: news summary of others' work; mentions 'findings', graded C until the cited study is itself graded
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Behavior or engagementPreprintSmall or impreciseResearcher-made measure
2026-04-16 · arXiv (back catalog) · Study (preprint)
Generative AI (GenAI) holds significant promise for advancing educational equity among ethnic minority students by broadening access to learning resources and mitigating linguistic barriers. However, these benefits are counterbalanced by the risk of cognitive laziness, whereby students may treat GenAI as an answer...
Why this label for Scaffolding Critical Engagement with GenAI: Transforming Ethnic Minority Prepara
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: design-based research with 78 students, discourse analysis, and paired pre/post self-efficacy measures has no comparison group.
- Grader B: R5 design ceiling: design-based research with a single group (n=78) and paired pre/post t-tests = Level 1; outcomes are discourse and self-efficacy, not achievement; accepted at AIED 2026.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-04-15 · eSchool News · News
Last fall, during a professional development session I was running with a group of teachers in São Paulo, a fifth-grade teacher raised her hand and asked a question I have since heard in every country I work in: “I want to use AI to plan better lessons. But how do I do that without just putting kids in front of...
Why this label for The screen-time debate’s blind spot
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Not reportedOutcome: Not reportedPreprint
2026-04-14 · arXiv (back catalog) · Study (preprint)
We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF). Pairwise preference labels are intended to reflect the compared outputs, but they may also reflect the rater's state during annotation.
Why this label for Rater State Bias in RLHF Preference Data: An Audit Framework
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 genre gate: develops a hypothesis and audit framework, with no completed empirical study reported.
- Grader B: R3 genre gate: conceptual hypothesis and proposed audit framework with a pilot plan and no collected data
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Commentary
2026-04-11 · Carl Hendrick (learning science) · Commentary
Retrieval practice under scrutiny, feedback's missing mechanism, AI that actually teaches, and the quiet erosion of cognitive effort
Why this label for The Monthly Dispatch - What’s New in Learning Science - April 2026
- Commentary or coverage: news, commentary or policy with no study
- Retrieval practice: matched 'Retrieval practice'
- Cognitive load: matched 'cognitive effort'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-04-10 · eSchool News · News
Most project-based learning workshops are built around three domains: design, assessment, and implementation.
Why this label for A new need-to-know for the AI classroom
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-04-09 · eSchool News · News
While nearly every industry is racing to integrate artificial intelligence, most schools are still teaching high school math the way it’s been done for decades--rooted in instructional material that is abstract, disconnected, and detached from the world students actually live in.
Why this label for It’s time to rewrite math standards for the future - and to stop expecting AI to
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 3: Controlled comparisonLevel-4 candidate (held at 3: preprint, needs full-text human check)Learners: Adult learnersOutcome: Learning or achievementPreprintShort exposure
2026-04-06 · arXiv (back catalog) · Study (preprint)
People often optimize for long-term goals in collaboration: A mentor or companion doesn't just answer questions, but also scaffolds learning, tracks progress, and prioritizes the other person's growth over immediate results. In contrast, current AI systems are fundamentally short-sighted collaborators -- optimized for...
Why this label for AI Assistance Reduces Persistence and Hurts Independent Performance
- Level 3: Controlled comparison: Level-4 candidate kept at 3: preprint; full-text check of attrition, baseline and measures required before any Level 4.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: randomized controlled trials assign human participants and measure unassisted performance; Level 4 verification and uncertainty information are not reported.
- Grader B: R5 design: series of RCTs (N=1,222) on unassisted performance and persistence; R7 preprint and short-exposure caps hold at max Level 3; human review needed before any upgrade.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Cognitive load: matched 'cognitive offloading'
- Reading and writing: matched 'reading comprehension'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Commentary
2026-03-26 · eSchool News · News
When I shipped Gramms AI to the App Store, I ran straight into a question that every developer building for kids will eventually face: What does “age-appropriate” actually mean in practice? And how do you build systems that enforce it reliably?
Why this label for Building AI for kids: A developer’s guide to age-appropriate safety architecture
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-03-25 · eSchool News · News
Student use of AI for homework increased in 2025, even as more students are worried the technology may be harming their ability to think critically, according to a new RAND report.
Why this label for Student use of AI for homework rises as concerns grow about critical thinking sk
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-03-18 · eSchool News · News
Last year, a third-grade teacher in São Paulo told me she had "finally found the perfect AI tool." It generated colorful worksheets in seconds. Vocabulary lists, reading comprehension questions, even a quiz.
Why this label for The layer every AI roundup is missing: Why methodology matters more than content
- Commentary or coverage: news, commentary or policy with no study
- Reading and writing: matched 'reading comprehension'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-03-14 · Carl Hendrick (learning science) · Commentary
The Cognitive Price of AI at Work, and the Learning Science Case for Optimism
Why this label for AI Brain Fry, Workslop and the Ironies of Automation
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-03-12 · eSchool News · News
Artificial intelligence is no longer approaching the classroom--it is already embedded in it. Students are using generative tools to brainstorm, summarize, translate, draft, and revise.
Why this label for The AI-resistant classroom is a myth: Designing assessments that assume AI is pr
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-03-08 · Carl Hendrick (learning science) · Commentary
New studies on retrieval practice, seductive details, engagement myths, reading instruction, and the cognitive costs of modern technology
Why this label for The Monthly Dispatch - What's New in Learning Science? - March 2026
- Commentary or coverage: news summary of others' work; mentions 'studies', graded C until the cited study is itself graded
- Retrieval practice: matched 'retrieval practice'
- Reading and writing: matched 'reading instruction'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-03-04 · eSchool News · News
AI is transforming the way students discover, evaluate, and choose colleges, according to a national survey of more than 5,000 high school students conducted by education company EAB.
Why this label for Nearly half of high school students now use AI in college search
- Commentary or coverage: news summary of others' work; mentions 'survey', graded C until the cited study is itself graded
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-02-23 · eSchool News · News
Who among us has never copied a homework answer in a hurry? Borrowed a friend’s paragraph?
Why this label for AI didn’t break homework: It exposed what was already broken
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-02-05 · eSchool News · News
Rather than replacing student thinking, when teachers design and guide AI experiences, the technology is most often used to deepen critical thinking and strengthen instruction
Why this label for New research challenges fears about AI in the classroom
- Commentary or coverage: news summary of others' work; mentions 'New research', graded C until the cited study is itself graded
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-02-03 · eSchool News · News
Remember the early 2000s, back when high-speed internet felt like a luxury reserved for the tech elite and the lucky few with deep pockets? We called it the Broadband Gap or Equity of Access, and it influenced who got ahead and who got left behind.
Why this label for The digital divide redux: Why AI is the new broadband
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-02-02 · eSchool News · News
Sixty-five percent of educators use AI to bridge resource gaps, even as platform fatigue and a lack of system integration threaten productivity, according to Jotform's EdTech Trends 2026 report.
Why this label for Despite platform fatigue, educators use AI to bridge resource gaps
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-01-29 · eSchool News · News
AI has crossed a threshold. In 2026, it is no longer a pilot category or a differentiator you add on.
Why this label for AI in edtech: The 2026 efficacy imperative
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Commentary
2026-01-27 · eSchool News · News
Picture someone sitting at a kitchen table after the kids are finally in bed, laptop open, half-drunk mug of herbal tea nearby. For years, she has had a vague idea for a business--custom curriculum design for small learning pods, for example, or a micro-studio creating bespoke art for local nonprofits.
Why this label for 2026 prediction: AI may unleash the most entrepreneurial generation we’ve ever s
- Commentary or coverage: news, commentary or policy with no study
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Level 2: CorrelationalLearners: Higher-ed studentsOutcome: Perception or attitudePreprintPerception only
2026-01-02 · arXiv (back catalog) · Study (preprint)
The development of effective autograders is key for scaling assessment and feedback. While NLP based autograding systems for open-ended response questions have been found to be beneficial for providing immediate feedback, autograders are not always liked, understood, or trusted by students.
Why this label for The Effect of Transparency on Students' Perceptions of AI Graders
- Level 2: Correlational: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling and R7 outcome cap: students' attitudes are compared with a control condition, but assignment and baseline equivalence are not reported, and outcomes are perception only.
- Grader B: R7 perception-only cap: transparency vs control comparison measuring only student attitudes toward autograders = max Level 2; n not reported; accepted to SIGCSE 2027.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Technical testLearners: Adults not in a learning role (raters)Outcome: Model performancePeer-reviewed (journal reference listed)Simulated learners
2025-08-08 · arXiv (back catalog) · Study
Generative Artificial Intelligence (GenAI) holds a potential to advance existing educational technologies with capabilities to automatically generate personalised scaffolds that support students' self-regulated learning (SRL). While advancements in large language models (LLMs) promise improvements in the adaptability...
Why this label for Towards Reliable Generative AI-Driven Scaffolding: Reducing Hallucinations and E
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: evaluation datasets and expert judgments assess generated scaffolds, without measuring student learning.
- Grader B: R4 real-learner gate: LLM-generated SRL scaffolds evaluated by multi-agent and LLM-as-judge systems against expert ratings; no learner outcomes (Computers & Education).
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Metacognition and self-regulation: matched 'Self-Regulated'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Learning or achievementPreprintSmall or impreciseShort exposureResearcher-made measure
2025-07-08 · arXiv (back catalog) · Study (preprint)
Retrieval practice is a well-established pedagogical technique known to significantly enhance student learning and knowledge retention. However, generating high-quality retrieval practice questions is often time-consuming and labor intensive for instructors, especially in rapidly evolving technical subjects.
Why this label for Enhancing Student Learning with LLM-Generated Retrieval Practice Questions: An E
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 places the week-to-week comparison without equivalence data at Level 2; R6 imprecision lowers it for about 60 students with no interval or power information.
- Grader B: R5: a week with LLM-generated retrieval questions vs a week without (~60 students, no equivalence data) is Level 2, then the R6 imprecision downgrade (n<100, no interval) gives 1; one-week exposure and a researcher-made quiz
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Retrieval practice: matched 'Retrieval Practice'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Not yet classified
Listed for the February 2027 issue · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education, Volume 257 by Jihyun Rho, Rui Guo, Hai Li et al. Issue: February 2027. Abstract on the publisher page.
Why this label for Disengaged conversational behaviors and learning in student-AI dialogues for mat
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
Listed for the February 2027 issue · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education, Volume 257 by Ari Kim. Issue: February 2027. Abstract on the publisher page.
Why this label for Designing a pipeline linking professional development to AI-supported instructio
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
Listed for the February 2027 issue · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education, Volume 257 by Wenjun Zhong, Jianghua Luo, Shuhui Yu et al. Issue: February 2027. Abstract on the publisher page.
Why this label for Navigating AI affective support: The cognitive-affective pathway from appraisal
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
Listed for the January 2027 issue · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education, Volume 256 by Chengchen Li, Ye Han, Enhao Feng. Issue: January 2027. Abstract on the publisher page.
Why this label for Engagement with GenAI-Assisted L2 writing: Conceptualization, measurement, and a
- Not yet classified: publisher listing with no abstract yet
- Reading and writing: matched 'L2 writing'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
Listed for the January 2027 issue · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education, Volume 256 by Bart Rienties, Duygu Bektik, Emily Coughlan et al. Issue: January 2027. Abstract on the publisher page.
Why this label for A five-study design-based research approach to co-design an institutional AI dig
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
Listed for the January 2027 issue · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education, Volume 256 by Siya Liang, King Woon Yau, Helen Meng et al. Issue: January 2027. Abstract on the publisher page.
Why this label for Does school-based AI education narrow readiness gaps? The role of prior agency-r
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
Listed for the January 2027 issue · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education, Volume 256 by Qing Ma, Fang Mei, Peter Crosthwaite et al. Issue: January 2027. Abstract on the publisher page.
Why this label for Combining AI and corpus technology in language teaching: Professional developmen
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Not yet classified
Listed for the January 2027 issue · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education, Volume 256 by Hongfeng Zhang, Liwei Ding, Jinqiao Zhou. Issue: January 2027. Abstract on the publisher page.
Why this label for Unpacking AI - education discourse: Topic modeling and fsQCA of Technology - Res
- Not yet classified: publisher listing with no abstract yet
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
