Level 3: Controlled comparisonProvisional (rules only)Review of many studies (44 studies)
2026-06-11 · ERIC (peer reviewed) · Study
Integrating technology in learning has been shown to positively influence mathematics outcomes across various educational levels. However, existing literature on the effectiveness of technology integration in mathematics education tends to focus on isolated aspects of technology's role, lacking a cohesive framework...
Why this label for The Effectiveness of Technology Integration in Secondary Mathematics: A Meta-Ana
- Level 3: Controlled comparison: provisional rules-only grade, pending grader consensus and review: R5 design (synthesis): Meta-analysis; capped at 3 as preprint; included-study quality needs human check.
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 engagementPreprintPerception onlyStudy type: Case study / case series
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...
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
Level 1: Descriptive or exploratoryLearners: K-12 studentsOutcome: Learning or achievementPreprintSmall or impreciseShort exposureStudy type: Case study / case series
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: 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 1: Descriptive or exploratoryProvisional (rules only)
2026-05-05 · ERIC (peer reviewed) · Study
Background: Academic resilience, defined as the capacity of socio-economically disadvantaged students to achieve at high academic levels, is a key indicator in international education policy. Large-scale assessments like the Programme for International Student Assessment (PISA) routinely report the prevalence of...
Why this label for Longitudinal Insights into Academic Resilience in PISA in Italy: The Role of Pri
- Level 1: Descriptive or exploratory: provisional rules-only grade, pending grader consensus and review: R5 design: descriptive (F3): Descriptive, qualitative, pilot or deployment study with real participants.
- Assessment and grading: matched 'Student Assessment'
- Policy and governance: matched 'education 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 exploratoryProvisional (rules only)
2026-04-15 · ERIC (peer reviewed) · Study
This study examined the associations between sedentary behavior (SB) at age 11 and mathematics ability at age 17, and assessed the mediating effect of internalizing and externalizing problems at age 14, using UK Millennium Cohort Study data (N = 3622; 53% male). Self-reported frequency of SB (listening to music,...
Why this label for Longitudinal Associations between Sedentary Behavior Types and Mathematics Abili
- Level 1: Descriptive or exploratory: provisional rules-only grade, pending grader consensus and review: R5 design: descriptive (F3): Descriptive, qualitative, pilot or deployment study with real participants.
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
Technical testLearners: Not a learning studyOutcome: Model performanceStudy type: Technical test (no real learners)
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
- Technical test: R4: validity of LLM classifications vs human coding; outcome is model performance.
- Review status: Graded by one AI grader from the abstract, on Matt's delegation; not yet human-verified
Graded by one AI grader from the abstract, on Matt's delegation; not yet human-verified. Method
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintOpen data or materialsStudy type: Technical test (no real learners)
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...
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
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintStudy type: Technical test (no real learners)
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
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintStudy type: Technical test (no real learners)
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
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintStudy type: Technical test (no real learners)
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
CommentaryProvisional (rules only)
2026-10-15 · arXiv physics.ed-ph · Study (preprint)
This study investigates integrative thinking in undergraduate computational physics design essays (CPDEs) within an engineering design-based introductory physics laboratory. Integrative thinking refers to the interaction of science, mathematics, computation, design, and metacognitive reflection during problem solving.
Why this label for Integrative Thinking in Computational Physics Design Essays
- Commentary: 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
- Metacognition and self-regulation: matched 'metacognitive'
- 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
CommentaryProvisional (rules only)
2026-10-06 · arXiv physics.ed-ph · Study (preprint)
The moving-coil meter provides a simple context in which to model deterministic electromechanical dynamics, thermal fluctuations, and electronic readout. The movement is a linear electromechanical system in which electrical resistance and intrinsic mechanical damping act as two independent dissipative channels, each...
Why this label for Thermal fluctuations and electronic readout using a moving-coil movement
- Commentary: 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
CommentaryProvisional (rules only)
2026-09-28 · arXiv physics.ed-ph · Study (preprint)
We present a traceable instructional module that maps documented learner difficulties in quantum superposition and measurement to explicit learning goals, classroom activities, and assessment evidence. Using the general two-level Hamiltonian as the organizing physics framework, the module implements this mapping...
Why this label for From Two-Level Hamiltonians to Quantum Superposition and Measurement: A Traceabl
- Commentary: 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
- 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
CommentaryStudy type: Not a study
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: 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
CommentaryStudy type: Not a study
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: 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
CommentaryStudy type: Not a study
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: 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
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 · Learning and Instruction (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Learning and Instruction, Volume 107 by Kelsey E. Schenck, Mitchell J. Nathan. Issue: February 2027. Abstract on the publisher page.
Why this label for An integrated cognitive-affective spatial system for geometry
- 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 December 2026 issue · Teaching and Teacher Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Teaching and Teacher Education, Volume 184 by Mayssa Hayeen-Halloun, Michal Ayalon. Issue: December 2026. Abstract on the publisher page.
Why this label for From isolated foci to coordinated attending: Critical events identified by novic
- 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 · Learning and Instruction (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Learning and Instruction, Volume 107 by Qian Zhao, Xiaopeng Wu, Lin Luo et al. Issue: February 2027. Abstract on the publisher page.
Why this label for Teachers' attitudes toward students' mistakes and students’ mathematical creativ
- 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 · Learning and Instruction (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Learning and Instruction, Volume 107 by S.M.M.de Mooij, C. Paans, F. Hasselman et al. Issue: February 2027. Abstract on the publisher page.
Why this label for Arithmetic development: a longitudinal observational study into the use of adapt
- 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
