Level 3: Controlled comparisonLearners: Adult learnersOutcome: Learning or achievementPreprintPreregisteredShort exposureStudy type: Randomized controlled trial
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
Level 3: Controlled comparisonLevel-4 candidate (held at 3: preprint, needs full-text human check)Outcome: Learning or achievementPreprintStudy type: Randomized controlled trial
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 (human override): Label correction only, level unchanged. The arXiv abstract describes participants only as 'people' and does not say how long they used the AI, so the automated labels 'Learners: Adult learners' and 'Short exposure' are not supported by the source. Removed to match the corrected explainer.
- Changed from: Level 3: Controlled comparison
- 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. Human grade change 2026-10-24, approved by Matt, Oct 24 2026 Method
Level 2: CorrelationalLearners: Adults not in a learning role (raters)Outcome: Behavior or engagementPreprintStudy type: Cohort / case-control
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 exploratoryProvisional (rules only)
2026-10-03 · arXiv physics.ed-ph · Study (preprint)
In physics instruction, equations are often treated primarily as tools for obtaining numerical results. Yet physicists also use them to describe dependencies, explain why those dependencies hold, predict what happens in new situations, test expressions against physical knowledge, and explore consequences that are not...
Why this label for Sensemaking of physics equations: Rationale, conceptualization, and core strateg
- 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.
- Preprint: not peer reviewed; capped at Level 3
- Worked examples and explicit instruction: matched 'explicit instruction'
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 impreciseStudy type: Cross-sectional
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
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePreprintPerception onlySmall or impreciseStudy type: Case study / case series
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
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Learning or achievementPreprintResearcher-made measureSmall or impreciseStudy type: Case study / case series
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: K-12 studentsOutcome: Behavior or engagementPreprintOpen data or materialsVendor-linkedResearcher-made measureStudy type: Case study / case series
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
Level 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Teacher practicePreprintStudy type: Case study / case series
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...
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 achievementPreprintResearcher-made measureStudy type: Case study / case series
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 1: Descriptive or exploratoryLearners: Higher-ed studentsOutcome: Perception or attitudePreprintPerception onlyStudy type: Cross-sectional
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.
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 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)
2026-10-13 · 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: R4: code generation benchmark across 24 models.
- 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)
2026-10-13 · 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: R4: OCR degradation tests; readability check is not a learning outcome.
- 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)
2026-10-13 · 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: R4: LLM marking of 761 assignments vs human marks; outcome is model agreement.
- 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)
2026-10-11 · 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...
Why this label for Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh
- Technical test: R4: AI-text detector benchmark.
- 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 reportedOutcome: Perception or attitudePreprintSmall or impreciseStudy type: Technical test (no real learners)
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
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintStudy type: Technical test (no real learners)
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
CommentaryStudy type: Expert opinion / commentary
2026-10-19 · Frontiers in Education · Study
Over the past decade, fairness research in classroom assessment has progressively repositioned students from passive objects toward partial subjects of assessment. By advocating for student active engagement in assessment processes via discourses such as student voice and rights, student agency, student assessment...
Why this label for Beyond passive rights-holders: toward student moral agency in assessment fairnes
- Commentary: Perspective article (R3).
- 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
CommentaryStudy type: Expert opinion / commentary
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...
Why this label for From Algorithmic Marginalization to AI-Mediated Re-Centering: Can Culturally Gro
- Commentary: Conceptual research note (R3).
- 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
CommentaryStudy type: Not a study
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: 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
CommentaryStudy type: Not a study
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: 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 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: 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
CommentaryStudy type: Not a study
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: 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
CommentaryLearners: Not reportedOutcome: Not reportedPreprintStudy type: Systematic review / meta-analysis
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...
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
CommentaryStudy type: Not a study
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: 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 reportedPreprintStudy type: Expert opinion / commentary
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
CommentaryStudy type: Expert opinion / 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: 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
CommentaryStudy type: Not a study
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: 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
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: Expert opinion / 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: 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
CommentaryStudy type: Not a study
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.
Why this label for When AI does the work, who does the learning?
- Commentary: 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 reportedPreprintStudy type: Expert opinion / commentary
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...
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
CommentaryStudy type: Not a study
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: 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
CommentaryStudy type: Not a study
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: 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
CommentaryStudy type: Expert opinion / 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: 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
CommentaryProvisional (rules only)
2026-02-25 · ERIC (peer reviewed) · Study
Who is more creative: highor low-socioeconomic status (SES) individuals? This question is the focus of intense debates within both the scientific community and society at large, since creativity has been linked to innovation, productivity, and wealth generation.
Why this label for Reading Ability Conflates SES Creativity Gaps
- 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
Not yet classified
2026-10-17 · Computers & Education (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Computers & Education by Shijun (Cindy) Chen, Christian D. Schunn, Jie Cao et al. Available online 17 October 2026. Abstract on the publisher page.
Why this label for GenAI-Supported Internal Feedback Intervention for Writing Improvement: The Role
- 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 February 2027 issue · Learning and Instruction (Elsevier) · Study
Publisher listing (abstract not yet available): Journal article in Learning and Instruction, Volume 107 by Li Zheng. Issue: February 2027. Abstract on the publisher page.
Why this label for Teaching by example? Teacher ICT use and middle school students’ computer and in
- 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
