Level 2: CorrelationalLearners: K-12 studentsOutcome: Behavior or engagementPreprintStudy type: Cohort / case-control
2026-10-06 · arXiv (back catalog) · Study (preprint)
Algorithmic recommendations can help participants navigate large matching markets. For example, recommendations for school and college choices may reduce information frictions and disparities in access to high-performing programs.
Why this label for Personalized Recommendations Without Inducing Congestion: Mitigating Disparities
- Level 2: Correlational: Change to 2. Randomized deployment in the NYC high school match; the outcome is school-choice behavior, not learning, so held at 2 after final review.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: applicants were assigned in a randomized trial, with ranking and matching behavior measured; Level 4 learning-outcome criteria are unmet.
- Grader B: R7 behavior/engagement-only cap: an RCT in the NYC high school match with real applicants, but outcomes are ranking and matching behavior, not learning, so capped at 2; preliminary conference version, treated as a preprint
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
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 exploratoryStudy type: Cross-sectional
2026-10-19 · Frontiers in Education · Study
The advent of generative artificial intelligence (AI) is transforming academic production and confronting the social sciences with the challenge of preserving critical thinking in the face of mechanically generated content. This research examines the perceptions and practices of social science lecturers and students...
Why this label for Perceptions, practices and governance of generative AI in university social scie
- Level 1: Mixed-methods: interviews, non-probabilistic survey of 200 students, policy content analysis. Perception outcomes.
- 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: Not reportedOutcome: Not reportedPreprintStudy type: Case study / case series
2026-09-28 · arXiv (back catalog) · Study (preprint)
Academic access programs distribute frontier artificial intelligence as research infrastructure, but awards and institutional authorization are distinct stages. We examine the handoff through structured coding of 15 publicly documented access pathways captured on 19 September 2026, of which ten met prespecified...
Why this label for After the Award: The Authorization Gap in Academic Access to Frontier AI
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 descriptive process trace of access programs and one institution, with no learner outcome.
- Grader B: R5 design ceiling: descriptive document coding of 10 access programs plus a single-institution process trace; no learners or comparison group, so Level 1 (descriptive).
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Adults not in a learning role (raters)Outcome: Behavior or engagementPreprintSmall or impreciseStudy type: Case study / case series
2026-09-23 · arXiv (back catalog) · Study (preprint)
Educational technology (EdTech) platforms collect highly sensitive student data, including behavioral logs, disability records, and academic histories. However, privacy considerations are often postponed rather than treated as a foundational design requirement.
Why this label for "We'll Fix It Later": Education, AI, and the Deferral of Privacy in EdTech
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: interviews and a platform policy audit provide descriptive evidence without a controlled comparison.
- Grader B: R5 design ceiling: qualitative interviews (n=12 EdTech professionals) plus a privacy policy audit of 48 platforms (kappa 0.78); descriptive, no learners
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
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: Perception or attitudePreprintPerception onlyStudy type: Cross-sectional
2026-08-25 · arXiv (back catalog) · Study (preprint)
The purpose of this study was to analyze patterns of artificial intelligence (AI) use and attitudes toward AI among students, faculty, and administrative staff at a large university specializing in teacher education. The analytical sample comprised 1809 students, 250 faculty members, and 62 administrative staff...
Why this label for The AI Adaptation Gap in Higher Education: Students, Faculty, and Administrative
- Level 1: Descriptive or exploratory: Large attitude and use survey (N=2,121); perception-only descriptive evidence.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: the cross-sectional survey reports controlled associations; R7 caps perception and engagement outcomes at Level 2.
- Grader B: R5 design ceiling: a cross-sectional self-report survey (N=2121); the OLS associations could match Level 2, but a survey is Level 1 and R5 takes the lower; R7 perception-only cap applies anyway
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: TeachersOutcome: Perception or attitudePreprintSmall or impreciseStudy type: Case study / case series
2026-07-17 · arXiv (back catalog) · Study (preprint)
While generative AI tools are directly changing how undergraduate computer science is learned and taught, they are also reshaping the relationships between instructors and students. In contrast to existing tool-oriented research on how instructors view and adopt AI, this study investigates how instructors think about...
Why this label for To Police or to Guide: How Higher Education Computer Science Instructors Design
- Level 1: Descriptive or exploratory: Consensus level confirmed against the methodology.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: 13 instructor interviews are qualitative evidence.
- Grader B: R5 design ceiling: qualitative study of 13 semi-structured instructor interviews, which is descriptive (Level 1).
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 1: Descriptive or exploratoryLearners: Not reportedOutcome: Teacher practicePreprintStudy type: Cross-sectional
2026-07-14 · arXiv (back catalog) · Study (preprint)
With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions. In computer science (CS) courses GenAI adoption is especially high and the implications for student learning...
Why this label for A Comparative Analysis of Institutional and Course Generative AI Policies within
- Level 1: Descriptive or exploratory: Both graders agree; rules version missed it. Consensus level confirmed.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 design ceiling: secondary analysis of policy and syllabus documents is descriptive.
- Grader B: R5 design ceiling: descriptive secondary document analysis of institutional and course AI policies, with no learner outcomes.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
Level 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
Technical testLearners: Simulated or AI-onlyOutcome: Model performancePreprintSimulated learnersStudy type: Technical test (no real learners)
2026-10-15 · arXiv (back catalog) · Study (preprint)
Adaptive assessment systems personalize learning by estimating learner proficiency and recommending suitable questions. However, many existing approaches rely on fixed difficulty labels, static rating updates, or black-box prediction models that provide limited control over rating stability, interpretability, and...
Why this label for MARS: A Momentum-Aware Adaptive Rating System for Personalized Assessment
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: evaluation measures prediction and routing performance on logged and simulated data, not learner outcomes.
- Grader B: R4 real-learner gate: evaluated on a log dataset (ASSISTments) and a simulated exam (MARS-SimExam); the only outcomes are prediction, rating stability and routing scores, so it measures the model
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
Technical testOutcome: Model performanceStudy type: Technical test (no real learners)
2026-10-07 · arXiv search: AI + education · Study (preprint)
Corrective feedback is among the best-evidenced drivers of second-language acquisition, yet corrections delivered during lessons rarely accumulate into an actionable view of grammar mastery. Prompted frontier models can provide such a view from learner--tutor lesson transcripts, but they are costly at scale.
Why this label for Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models O
- Technical test: Matt's decision: model benchmark; the online experiment reports engagement and business metrics, not learning.
- Review status: Graded by one AI grader from the abstract, on Matt's delegation; Technical test level decided by Matt
Graded by one AI grader from the abstract, on Matt's delegation; Technical test level decided by Matt. Method
Technical testLearners: Adults not in a learning role (raters)Outcome: Model performancePreprintStudy type: Technical test (no real learners)
2026-08-27 · arXiv (back catalog) · Study (preprint)
Reliable assessment of causal research designs in the social sciences is critical for evidence-based policy-making, yet has so far relied entirely on manual expert analysis. We introduce Automated Research Design Tracking and Assessment (ARDTrA), a task that involves detecting the research design used in a paper and...
Why this label for Research Design Tracking and Assessment for the Social Sciences
- Technical test: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R4 real-learner gate: expert-annotated papers serve as a benchmark for an LLM research-design assessment pipeline.
- Grader B: R4 real-learner gate: RAG/LLM pipeline evaluated on an expert-annotated paper dataset; outcome is model performance (EMNLP 2026).
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
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: Adults not in a learning role (raters)Outcome: Perception or attitudePreprintShort exposurePerception onlySmall or impreciseStudy type: Technical test (no real learners)
2026-08-01 · arXiv (back catalog) · Study (preprint)
This innovative practice full paper presents BoilerSketch, a TA-supervised, diagram-first GenAI practice and tablet interface for providing structured visual explanations in CS1 and early CS2 support settings. Large early computing courses routinely face a support bottleneck during labs and office hours because many...
Why this label for BoilerSketch: A TA-Supervised, Diagram-First GenAI Practice for Structured Diagr
- Technical test: Tool paper; no learner outcomes reported.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R4 real-learner gate: instructional staff evaluated a prototype; no learner outcomes were studied.
- Grader B: R5 design ceiling: one 45-minute expert evaluation with a post-use survey of 21 instructional staff, with no comparison group, so descriptive Level 1 and no learner outcome
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
- Tutoring and personalization: matched 'tutoring'
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryProvisional (rules only)
2026-10-20 · American Educational Research Journal (SAGE) · Study
This paper examines how parents of students with disabilities from different social classes, racial backgrounds, geographic locations, and developmental stages construct school choice sets. Using an intersectional approach, we integrate ethnographic ...
Why this label for Constructing Choice Sets at the Intersections of Disability, Race, Class, and Ge
- Commentary: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
- Inclusion and accessibility: matched 'students with disabilities'
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
2026-10-13 · arXiv search: AI + education · Report / framework
Human-AI Interaction is commonly evaluated by what an artificial agent enables during use, yet interaction can also alter the human who acts next. We introduce Residual Human States, a trajectory-based framework for representing the potentially multidimensional human state carried forward after interaction with AI,...
Why this label for What Human-AI Interaction Leaves Behind: Residual Human States - A Trajectory-Ba
- Commentary (human override): The paper introduces a conceptual framework (Residual Human States) and illustrates it with four cases, most drawn from previously published or separate studies, rather than reporting one new graded study. Our genre rule places framework work in Commentary.
- Changed from: Level 1: Descriptive or exploratory
Human grade change 2026-10-21, approved by Matthew Rhoads, Ed.D. (approved in chat, 2026-10-21). Method
CommentaryStudy type: Not a study
2026-10-09 · EdTech Digest · News
Bans reward outdated skills. Grade judgment instead.
Why this label for Your AI Policy Is Slop
- Commentary: news, commentary or policy with no study
- Policy and governance: matched 'AI Policy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryProvisional (rules only)
2026-10-08 · Educational Administration Quarterly (SAGE) · Study
Educators who share knowledge deemed ‘dangerous’ by the state face unique pressures to comply with repressive policies, while at the same time uniquely positioned to resist them. In East Jerusalem, public schools are subject to coercive policies aimed at ...
Why this label for Training for Civics Teachers in East Jerusalem: Compliance, Compromise or Resist
- 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)
2026-10-08 · Educational Administration Quarterly (SAGE) · Study
This multiple case study examines how contextual elements shape elementary school principals’ enactments of state-level anti-LGBTQ+ legislation. Grounded in policy enactment theory, this work draws on interviews from a larger comparative case study to ...
Why this label for Elementary Principals’ Enactments of Anti-LGBTQ+ Legislation: Examining the Infl
- Commentary: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
- Policy and governance: matched 'Legislation'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryProvisional (rules only)
2026-09-26 · Educational Administration Quarterly (SAGE) · Study
Over the last several years, K-12 education has seen a renewed focus on diversity, equity, and inclusion (DEI) initiatives followed by a substantial counter response through anti-DEI laws, often developed and implemented via anti-democratic processes, ...
Why this label for “We’ve Gone 100 Years Back:” School Counseling District Leaders’ Making Sense of
- 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)
2026-09-25 · Educational Administration Quarterly (SAGE) · Study
This study examines the role of school leadership in shaping multilingual learners’ (MLs) college-going experiences, drawing on the perspectives of MLs and school counselors. It addresses the gap in understanding how leadership systems can ...
Why this label for How School Leadership Matters in Multilingual Learners’ College Access: Cultural
- Commentary: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
- Inclusion and accessibility: matched 'Multilingual Learners'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryProvisional (rules only)
2026-09-15 · Educational Administration Quarterly (SAGE) · Study
In many developing countries, including Pakistan, anti-democratic and authoritarian forces manifest through different forms, including centralized control, weak institutional autonomy, and politically influenced school governance. This study ...
Why this label for School Leaders Confronting Anti-Democratic Education Policy: Policy Enactment an
- Commentary: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
- 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
CommentaryProvisional (rules only)
2026-09-15 · Educational Administration Quarterly (SAGE) · Study
This paper argues for the systematic integration of African Indigenous Knowledge Systems (AIKS) - anchored in the normative philosophy of Ubuntu - into K - 12 education policy across Africa. Historically, the dominance of Eurocentric epistemologies has ...
Why this label for Reimagining K - 12 Schooling for Cognitive Justice and Equity in Africa
- Commentary: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
- 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
CommentaryProvisional (rules only)
2026-09-05 · American Educational Research Journal (SAGE) · Study
The first Trump administration opposed race-conscious federal educational policies to redress state-sponsored discrimination and related racial inequality. Drawing on extensive qualitative data, we analyzed the mechanisms, collaborations, and strategies ...
Why this label for Democratic Dissent: Resistance to Race-Evasive Education Policies Under the 2017
- Commentary: provisional rules-only grade, pending grader consensus and review: R3 genre gate: No empirical learner data described; conceptual, framework, policy or argument paper.
- Policy and governance: matched 'Education Policies'
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
2026-09-03 · eSchool News · News
AI technology is advancing rapidly in schools, but research is increasingly showing that Large Language Models (LLMs) lack the common sense, morality, and knowledge of individual classroom dynamics needed to be truly equitable.
Why this label for Building an everyday equitable practice for AI in schools
- Commentary: 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)
2026-08-30 · American Educational Research Journal (SAGE) · Study
Schools are central settings for youth mental health support, yet less research has examined policy environments shaping what is possible in school-based mental health systems. This study examines the zone of mediation for equity-minded school mental ...
Why this label for Affordances and Constraints for Equity-Minded School Mental Health Policy Change
- 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
CommentaryLearners: Not reportedOutcome: Not reportedPreprintStudy type: Expert opinion / commentary
2026-08-27 · arXiv (back catalog) · Study (preprint)
Generative artificial intelligence (GenAI) has been primarily framed as an impartial educational tool. However, this framing overlooks an even larger shift: the reassignment of epistemological authority from teachers to students to machines.
Why this label for Reclaiming Epistemic Agency: A Critical Framework for Human-Generative AI Co-Age
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 genre gate: the paper presents a conceptual evaluation and framework without an empirical study.
- Grader B: R3 genre gate: a conceptual evaluation of theories that proposes a framework; no empirical study
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
CommentaryProvisional (rules only)
2026-08-26 · American Educational Research Journal (SAGE) · Study
Research has found that exclusionary punishment is ineffective, harmful, and disproportionately wielded against Black, Latino/a, and Indigenous students. The proliferation of restorative justice as an alternative has signaled a paradigmatic shift in ...
Why this label for Restorative or Carceral: A Mixed-Methods Analysis of School Leaders’ Discipline
- 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
2026-08-25 · eSchool News · News
Long before computers entered our homes and classrooms, photographs helped shape what people believed. Images carried a particular kind of authority: We did not simply look at them; we often treated them as evidence.
Why this label for Beyond spotting deepfakes: Teaching multilingual learners to interrogate AI
- Commentary: news, commentary or policy with no study
- Inclusion and accessibility: matched 'multilingual learners'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryProvisional (rules only)
2026-08-24 · Educational Administration Quarterly (SAGE) · Study
Leveraging existing theoretical frameworks on social justice leadership, this paper asks: 1) How do school leaders support racial justice efforts in their schools? 2) What approaches do school leaders take to sustain this work over time?Research ...
Why this label for Leadership That Supports and Sustains Racial Justice in Schools
- 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.
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryLearners: Adult learnersOutcome: Not reportedPeer-reviewed (journal reference listed)Study type: Expert opinion / commentary
2026-08-19 · arXiv (back catalog) · Study
Systemic change across the digital research landscape is required to reduce the environmental impact of digital research, but while many researchers and technical professionals are motivated to act, they often lack the skills required to translate motivation into lasting organisational change. We present an...
Why this label for Turning interest into institutional change: teaching advocacy for sustainable re
- Commentary: Course description for researchers with no evaluation; conceptual.
- Review status: Confirmed against the published method (AI-assisted review)
- Grader A: R5 pilot ceiling: a short course pilot is mentioned, but no learner outcome or comparison is reported.
- Grader B: R3 genre gate: describes an open course and its design; the short pilot reports no data or outcomes, so there is no study to grade.
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review) Method
CommentaryStudy type: Not a study
2026-08-13 · EdSurge · News
At a gathering in Boston, ‘student senators’ proposed a first-of-its-kind national AI policy for K-12 classrooms.
Why this label for On AI Policy, Students Have Plenty to Say
- Commentary: news, commentary or policy with no study
- Policy and governance: matched 'AI Policy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryStudy type: Not a study
2026-08-05 · EdSurge · News
One national study, one local leader, and a reality check on how schools are really regulating AI.
Why this label for What Happens When AI Policy Meets a Real Classroom?
- Commentary: news summary of others' work; mentions 'study', graded C until the cited study is itself graded
- Policy and governance: matched 'AI Policy'
Provisional label from fixed rules applied to the collected text; research items are re-graded by our review pipeline. Method
CommentaryStudy type: Not a study
2026-04-24 · eSchool News · News
AI is here, and it’s moving fast. For schools, that speed is both an opportunity and a risk: The right tools can transform learning, but the wrong ones can compromise data, equity, and instructional goals.
Why this label for From innovation to impact: Three ways school districts can build a sustainable A
- Commentary: 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
2026-04-14 · arXiv (back catalog) · Study (preprint)
We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF). Pairwise preference labels are intended to reflect the compared outputs, but they may also reflect the rater's state during annotation.
Why this label for Rater State Bias in RLHF Preference Data: An Audit Framework
- Commentary: Two independent AI graders gave the same level.
- Review status: Confirmed against the published method (AI-assisted review): both graders agreed
- Grader A: R3 genre gate: develops a hypothesis and audit framework, with no completed empirical study reported.
- Grader B: R3 genre gate: conceptual hypothesis and proposed audit framework with a pilot plan and no collected data
- Rule version: METHODOLOGY v0.3, pipeline v1, rules rules-v0.4
Graded blind by two AI graders against our published method. Confirmed against the published method (AI-assisted review): both graders agreed Method
CommentaryStudy type: Not a study
2026-02-03 · eSchool News · News
Remember the early 2000s, back when high-speed internet felt like a luxury reserved for the tech elite and the lucky few with deep pockets? We called it the Broadband Gap or Equity of Access, and it influenced who got ahead and who got left behind.
Why this label for The digital divide redux: Why AI is the new broadband
- Commentary: 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)
2025-12-17 · ERIC (peer reviewed) · Study
The transient nature of K-12 English Learner (EL) status and services makes it difficult for researchers to follow their educational pipeline. ELs' backgrounds and experiences vary considerably, with differences in grade of entry into U.S.
Why this label for Variation in English Learners' Programmatic Experiences and Postsecondary Trajec
- 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
