Level 1: Descriptive or exploratoryStudy type: Cross-sectional
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...
Why this label for Educating future engineers about LLMs: A scalable workshop
- Level 1: 226 students, post-workshop self-report questionnaire and prompt analysis; no comparison group.
- 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
Level 1: Descriptive or exploratoryLearners: K-12 studentsOutcome: Behavior or engagementPeer-reviewed (journal reference listed)Vendor-linkedPerception onlyStudy type: Case study / case series
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
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Behavior or engagementPreprintSmall or impreciseShort exposureStudy type: Case study / case series
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: Learning or achievementPeer-reviewed (journal reference listed)Small or impreciseStudy type: Case study / case series
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
Technical testLearners: Not a learning studyOutcome: Model performanceStudy type: Technical test (no real learners)
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: R4: academics prompted ChatGPT; outcome is model-generated review scores, no learners.
- 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 testSimulated or AI-onlyOutcome: Model performanceStudy type: Technical test (no real learners)
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: R4: LLM-simulated students; simulated-only is always T.
- 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: Not a learning studyOutcome: Model performanceStudy type: Technical test (no real learners)
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: R4: language model training and benchmarks.
- 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)
2026-10-08 · 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
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learnersStudy type: Technical test (no real 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
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learnersStudy type: Technical test (no real 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
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePeer-reviewed (journal reference listed)Study type: Technical test (no real learners)
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: Simulated or AI-onlyOutcome: Model performancePeer-reviewed (journal reference listed)Study type: Technical test (no real learners)
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
CommentaryProvisional (rules only)
2026-10-20 · American Educational Research Journal (SAGE) · Study
When education draws on student and community funds of knowledge to enhance collective wellbeing, young people experiencelearning that matters, applying knowledge and skills to contribute to social good. This case examines the implementation ofAkomanga ...
Why this label for Implementing Learning for Social Innovation: A School-Based Case Study of Progra
- Commentary: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
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
2026-10-15 · 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: 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
2026-10-14 · 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: 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
CommentaryProvisional (rules only)
2026-10-08 · Educational Administration Quarterly (SAGE) · Study
Purpose:Research indicates that building schools and districts capable of problem solving and continuous improvement requires focused effort to develop internal capacity for organizational learning. As a field, however, we lack substantial evidence ...
Why this label for Building District Capacity for Organizational Learning: Nurturing a Culture of L
- Commentary: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryProvisional (rules only)
2026-10-03 · American Educational Research Journal (SAGE) · Study
Schools are both vulnerable to climate change and uniquely positioned to be part of the solution. We conduct an integrative review of interdisciplinary research and data to better understand the bidirectional relationship between education systems and our ...
Why this label for Education and Climate Change: Synthesizing the Evidence to Guide Future Research
- Commentary: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
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
2026-09-30 · 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: 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
2026-09-29 · 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: 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
CommentaryStudy type: Not a study
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: 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
2026-09-23 · 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: 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
CommentaryStudy type: Not a study
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: 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
2026-09-14 · 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: 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
2026-09-09 · 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: 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
2026-09-04 · 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: 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
2026-09-02 · 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: 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
2026-08-27 · 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: 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
2026-08-19 · 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: 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
2026-08-12 · 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: 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
2026-08-04 · 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: 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
CommentaryStudy type: Not a study
2026-08-03 · EdSurge · News
A teacher created an app to fight cheating - and discovered a bigger takeaway.
Why this label for I Used AI to Build AI-Resistant Assignments
- Commentary: 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
CommentaryStudy type: Not a study
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: 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
CommentaryStudy type: Expert opinion / 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: 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
2026-06-05 · 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: 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
CommentaryStudy type: Expert opinion / 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: 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
CommentaryStudy type: Not a study
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: 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
2026-05-14 · 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: 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
2026-05-05 · 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: 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
2026-04-17 · 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: 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
CommentaryStudy type: Not a study
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: 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
2026-03-25 · 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: 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
2026-02-23 · 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: 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
2026-02-05 · 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: 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
CommentaryStudy type: Not a study
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: 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
2026-01-29 · 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: 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 citedStudy type: Not a study
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
No study citedStudy type: Not a study
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
No study citedStudy type: Not a study
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
No study citedStudy type: Not a study
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
No study citedStudy type: Not a study
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
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
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
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
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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
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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
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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 Tong Zhou, Yi Liao, Xiaoyu Cheng et al. Issue: February 2027. Abstract on the publisher page.
Why this label for From growth mindset to social-emotional skills in early and mid-adolescence: A e
- 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
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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 Paula Johnstone, Christian Andrä, Manuela Macedonia et al. Issue: February 2027. Abstract on the publisher page.
Why this label for Gesture benefits in second language learning take time to develop
- 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
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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 Shuang Wu, Shufeng Ma, Senyu Chen et al. Issue: February 2027. Abstract on the publisher page.
Why this label for Dynamic engagement patterns during collaborative reasoning discussions and their
- 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
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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 Jingjing Hu, Xuesong Gao. Issue: February 2027. Abstract on the publisher page.
Why this label for Rethinking cultural awareness in think-aloud research
- 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
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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 Christina Wekerle, Michael Sailer, Ingo Kollar et al. Issue: February 2027. Abstract on the publisher page.
Why this label for The effects of ICAP learning activities on learning outcomes
- 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
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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 Anthony J. Onwuegbuzie. Issue: February 2027. Abstract on the publisher page.
Why this label for Beyond think-aloud: Toward a representational - recursive agenda for multimodal
- 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
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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 Claire Ballot, Pascal Zesiger. Issue: February 2027. Abstract on the publisher page.
Why this label for Development of mental imagery strategy in a recall task as a function of word im
- 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
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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 Lucas Lörch, Elizabeth Bonawitz, Garvin Brod. Issue: February 2027. Abstract on the publisher page.
Why this label for The power of varying more than one thing at a time
- 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
