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 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
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
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: 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
2026-09-29 · 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
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
2026-04-22 · 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
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
