Systematic Review Flow (PRISMA 2020)
Transparent flow of article identification, LLM screening, full-text eligibility assessment, and inclusion into living synthesis with expert validation.
✓ PRISMA funnel conservation verified (100% stage closure)
Zero data leakage: n_in = n_out + ∑n_excl + n_pending verified across identification, screening, eligibility, and synthesis.
ID: 20240 →
Screen: 19073 →
Eligible: 2784 →
Included: 102
Expert Validation & Taste Loop
Independent of the formal PRISMA selection rules: editorial verdicts by the curator to calibrate quality and active learning.
👍 10
Approved in digest
(14 total in corpus)
👎 29
Rejected with reason
(1 pre / 18 post analysis)
Breakdown of expert rejection reasons:
not_novel: 4
not_for_educators: 3
off_topic: 13
too_narrow: 2
duplicate: 5
other: 1
weak_method: 1
📋 Structured Review Summary (PRISMA 2020: Item 2)
Background: Living systematic review of Large Language Models (LLMs) and autonomous agents in higher education. Focuses on engineering countermeasures to academic dishonesty and redesign of assessment.
Objectives: Systematic identification, screening, and synthesis of empirical evidence regarding reliability, methodological rigor, and consensus challenges in higher education GenAI integration.
Eligibility Criteria: Scholarly works with open-access full text; topic relevance R ≥ 4.0; weighted final score ≥ 5.0.
Information Sources: Multi-register tracking: arXiv, Crossref, OpenAlex, Semantic Scholar, ERIC, plus forward/backward citation loops (depth 1–2).
Risk of Bias & Quality: Multi-criteria methodological rigor (M, 0–10). Screening reliability validated via dual evaluation: Gwet's AC1, stratified recall, and ROC AUC.
Synthesis & Included: Included n = 102 verified works into the living review with extracted claims, limitations, and empirical findings.
🎯 PICO / PECO Search & Eligibility Framework (PRISMA 2020: Items 4–5)
| Component | Protocol Definition | Inclusion / Exclusion Boundaries |
|---|---|---|
| P (Population) | Higher education: universities, colleges, undergraduate/graduate students, instructors, and examiners. | Included: higher/tertiary education. Excluded: purely K-12 primary education without transferability. |
| I / E (Intervention / Exposure) | Use of modern LLMs (GPT-4, Claude, Gemini, Llama) and agentic frameworks for exam solving, tutoring, or automated grading. | Included: empirical testing and deployment of LLMs. Excluded: legacy non-generative ML or symbolic AI. |
| C (Comparator) | Traditional non-AI assessments, human baselines, or comparative evaluation of engineering defenses (guardrails, multi-agent validation). | Included: baselines, cohort comparisons, or countermeasures. Excluded: purely speculative essays without comparator. |
| O (Outcomes) | Task performance, exam breach rates, countermeasure resilience, shift in assessment rubrics, cognitive learning outcomes. | Included: empirical accuracy, resilience, rubric difficulty, psychometrics. Excluded: unverified claims. |
| S (Study Design) | Empirical studies: controlled experiments (RCT), quasi-experiments, case studies, psychometric evaluations, benchmark audits. | Included: peer-reviewed/preprint research with empirical data. Excluded: opinion blogs, press releases. |
* Footnote *: Prefilter Mathematical Model, Indicators & Scoring Formula (Rubric v2.3)
Rubric Indicators & Parameters
| Symbol | Indicator Name | Weight / Value |
|---|---|---|
| M | Methodological Rigor | wM = 0.32 |
| E | Engineering Countermeasures | wE = 0.25 |
| C | Consensus Challenge | wC = 0.43 |
| N | Novelty | wN = 0.00 |
| R | Topic Relevance (0–10) | Rmin = 4.0, Rfull = 7.0 |
| Gate | Binary Scope Gate | PASS / FAIL |
| Поріг | Pass Threshold | Score ≥ 5.0 |
Relevance R is a multiplier, not a summand: papers with R < R_min receive 0, and high-quality off-topic papers cannot outrank topical ones.
Scoring Formulae (MathML Core)
1. Final Prefilter Score:
2. Weighted Quality Score (Q):
3. Logical Prefilter Screening Criterion:
Methods & reporting (PRISMA 2020 items 1–15, 23–27)
Review type, objective, eligibility (1, 3–5)
Living systematic review: a daily-updated, automation-assisted selection. It is a literature-selection tool, not a published review with meta-analysis.
Topic (given to the model): Validity of educational assessment under generative AI and how to keep it (AI-resistant / AI-resilient assessment). Core: how generative AI changes what grades and test scores mean (validity arguments in the sense of Kane and Messick), assessment redesign, and quantitative methods that detect or model the distortion in assessment and learning data: item response theory, differential item functioning between humans and LLMs, item parameter drift, rater drift, item compromise and preknowledge, score shifts under unsupervised or online conditions (proctored vs unproctored exams, cheating, grade inflation), change-point and concept-drift detection, LLMs as test takers or raters, computerized adaptive testing and automated test assembly. Computational, statistical and engineering work is preferred, but a solid conceptual or empirical education study of assessment under generative AI belongs here too. Detectors of AI-generated text and watermarking are only adjacent to the topic. The audience is university educators and researchers: work about primary and secondary school (K-12) is off topic unless its findings or methods clearly transfer to higher education.
- Include: a scholarly article with topic relevance R ≥ 4.0, final score ≥ 5.0; a legal open-access full text that was actually read.
- Exclude: non-articles (datasets, code, errata), duplicates and preprints of published works, retracted works, K-12 with no transfer to higher education, works without full text (paywall, fetch errors), expert-rejected works (👎, reason recorded).
- Manual additions (origin = manual) bypass score screening but go through eligibility assessment.
Information sources & last searched (6)
| Channel | Records | Last record |
|---|---|---|
search — arXiv, OpenAlex, Semantic Scholar, ERIC | 18128 | 2026-11-13 22:29 |
ref — reference lists of read works | 2085 | 2026-11-13 22:30 |
manual — added manually | 27 | 2026-10-07 12:05 |
Citation candidates take metadata from OpenAlex; Crossref/Unpaywall supply DOIs, versions and open PDFs.
Search strategy (7)
Current search version: v1.0. A work matches if its title/abstract hits a term from EVERY group of at least one query (OR within a group; case-insensitive, word boundaries).
Queries (groups):
education × ai; psychometrics × ai; integrity × education; drift × education; drift × psychometrics
education: education, educational, student, learner, teacher, lecturer, classroom, university, higher education, school, curriculum, assessment, exam, examination, examinee, test taker, test score, grading, homework, coursework, online course, essay, academic integrity, academic misconduct, plagiarism, cheating, e-learning, MOOC
ai: generative AI, GenAI, large language model, LLM, ChatGPT, GPT-4, chatbot, AI-generated, AI-assisted, AI-resistant, AI-resilient, AI tool, artificial intelligence
integrity: proctoring, proctored, unproctored, non-proctored, remote proctoring, online exam, online test, online testing, exam integrity, testing integrity, academic integrity, academic dishonesty, cheating, contract cheating, grade inflation
psychometrics: item response theory, IRT, Rasch, psychometric, educational measurement, educational testing, differential item functioning, computerized adaptive testing, adaptive testing, automated test assembly, knowledge tracing, automated scoring, automated essay scoring, validity argument, construct validity, assessment validity, predictive validity
drift: parameter drift, dynamic item response, item parameter drift, rater drift, severity drift, concept drift, change point, change-point detection, item compromise, preknowledge, item exposure, test security, aberrant response, person-fit, differential item functioning
Semantic Scholar phrases: assessment validity generative AI; AI-resilient assessment higher education; AI-resistant assessment design; generative AI exam performance natural experiment; differential item functioning large language models humans; LLM as test taker item response theory; proctored versus unproctored online exam scores; generative AI grade inflation; item parameter drift detection; item compromise preknowledge detection; concept drift knowledge tracing; change point detection educational testing
arXiv: cs.CY, cs.CL, cs.HC, cs.AI, cs.LG, stat.AP, stat.ME. Result caps per query: OpenAlex 50, Semantic Scholar 25, ERIC 25. Citation searching: depth 1 from read and 👍 works, depth 2 from 👍 only; neighbours of 👍 works bypass the lexical filter.
| Search version | Records (search) | First | Last |
|---|---|---|---|
v1.0 | 18128 | 2026-10-06 | 2026-11-13 |
Selection process & automation (8, 9, 11)
- Abstract screening: a single automated reviewer — an LLM (gemini-3.1-flash-lite, gemini-3.5-flash-lite), batches of 20, models alternate; every evaluation is stored (prefilter_evaluations) with model and rubric version.
- Full-text analysis and data collection: gemini-3.8-flash, gemini-3.7-flash, gemini-3.6-flash, prompt
v2.1. Only after PDF→Markdown conversion; the model is never called without full text. - Verification: code checks every quote against the text (exact match after normalisation); unconfirmed claims are discarded.
- Human: no independent second reviewer. The owner gives 👍/👎 verdicts on a sample (50% near threshold, 30% top, 20% random); they steer the queue and few-shot examples but do not change the score.
- Data items (10): summary, method/data/sample, claims, limitations, study design (assessment_design, psychometric_modeling, natural_experiment, detector_evaluation, survey, rct, quasi_experiment, qualitative, theoretical, review, other), tags, rubric criteria.
- Risk of bias (11): not assessed formally (RoB, GRADE not applied); criterion M (methodological rigor, 0–10) is a ranking score, not a RoB tool. No effect measures or meta-analysis (12, 13d–f, 15): synthesis is qualitative, via article analyses and tags.
| Screening model | Scored |
|---|---|
gemini-3.1-flash-lite | 9509 |
gemini-3.5-flash-lite | 8898 |
— | 732 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 265 |
gemini-3.8-flash | v2.1 | 116 |
gemini-3.7-flash | v2.1 | 108 |
Scoring rubric v2.3 (0–10 anchors)
The model scores 0–10 per criterion; the anchors below are embedded in the prompt. Formula and weights are in the footnote above. Changing anchors or weights without a new version is blocked by a hash check.
| Criterion | Weight | 2 | 5 | 8 |
|---|---|---|---|---|
| Topic relevance (R) | × min(1, R/7.0) | No link to education or assessment: image, audio or model watermarking, model IP protection, general NLP benchmarks, AI in industry. | Adjacent: generative AI in education without an assessment focus; academic integrity with no evidence on assessment outcomes; detectors of AI-generated text or of student AI use; general psychometrics (adaptive testing, test assembly, IRT) with no drift, validity or AI angle. | Core: validity of assessment under generative AI (what grades and scores mean when AI is available); how grades shift under unsupervised conditions or cheating (proctored vs unproctored exams, grade inflation); AI-resistant or AI-resilient assessment design; quantitative methods that detect or model distorted assessment or learning data (DIF between humans and LLMs, item parameter or rater drift, item compromise/preknowledge, change-point or concept-drift detection, LLMs as test takers or raters). |
| Methodological rigor (M) | 0.32 | Vague evaluation, convenience sample of a few dozen, self-report only, no control group or baseline. | Clear dataset or sample (hundreds), standard metrics or validated instruments, comparison with baselines or a control group. | Randomized/controlled or preregistered, large or multi-site/multilingual data, adversarial or robustness checks, effect sizes with uncertainty. |
| Engineering countermeasures (E) | 0.25 | No method or countermeasure: describes impact, attitudes or policy only. | A concrete method or countermeasure with an evaluation: statistical test or model, detector, scoring procedure, or an assessment design tried on real cohorts. | A rigorous, reusable method: formal model with guarantees or error control, open code, validated on both real and simulated data. |
| Consensus challenge (C) | 0.43 | Reconfirms standard wisdom. | Nuances the consensus (e.g. detectors fail under mild paraphrasing; AI use does not harm an outcome it is assumed to harm). | Directly refutes high-profile claims with rigorous evidence. |
| Novelty (N) | 0.00 | Replicates well-known findings (another survey of student attitudes to ChatGPT, another test of one detector on a handful of prompts). | New dataset, benchmark, population or setting, or a previously untried technique. | Fundamentally new attack or defence, a theoretical/impossibility result, or a finding that changes how the field frames the problem. |
The model interpolates between anchors. The deep-analysis score is computed by code with the same formula from criteria_breakdown.
| Rubric version | Papers scored |
|---|---|
v2.0 | 261 |
v2.1 | 183 |
v2.2 | 20 |
v2.3 (current) | 18675 |
Screening Validation: Reliability & Agreement Metrics
Reference standard: expert verdicts. Full PRISMA systematic review metrics computed: recall, specificity, NPV, F₁, F₂, ROC AUC, DOR, paradox-immune Gwet's AC₁, and continuous score agreement.
| Measure | Value | Meaning |
|---|---|---|
| Seed-set recall | 50% (1/2), 95% CI 9–91% | Share of manually added 👍 works (bypassing screening) scoring ≥ threshold 5.0. Unbiased sensitivity estimate on this set. |
| Estimated corpus recall | 100% (100–100%) | Post-stratified by score band from judged works; conservative interval. Relevant works missed ≈ 0 (0–0). Not measured (too few verdicts in band): 16316 screened-out works. |
| Sensitivity / Recall | 100% (12/12), 95% CI 76–100% | Share of relevant works passed by screening: TP / (TP + FN). |
| Specificity | 20% (4/20), 95% CI 8–42% | Share of irrelevant works rejected by screening: TN / (TN + FP). |
| Precision / PPV | 43% (12/28), 95% CI 27–61% | Share of expert-approved among screening passes: TP / (TP + FP). |
| Negative Predictive Value (NPV) | 100% (4/4), 95% CI 51–100% | Share of truly irrelevant among screened-out works: TN / (TN + FN). Protects against losing relevant papers. |
| F-scores (F₁ and F₂) | F₁: 0.60 · F₂: 0.79 | F₂ weights recall twice as much as precision — critical safety metric against false exclusions in PRISMA. |
| Balanced Accuracy | 60.0% | Arithmetic mean of sensitivity and specificity, unaffected by class imbalance. |
| Diagnostic Odds Ratio (DOR) | 6.82 | DOR = (TP · TN) / (FP · FN) with Haldane-Anscombe correction. Overall discriminative capability of screening. |
| ROC AUC | 0.65 (95% CI 0.43–0.84) | Probability a 👍 work outscores a 👎 one (0.5 = chance). CI computed via DeLong / Bootstrap. |
| Inter-Rater Agreement (Gwet's AC₁ & Cohen's κ) | — | Gwet's AC₁ is paradox-immune inter-rater agreement on double-scored works, robust to severe class imbalance (80-90% screening rejection). |
| Continuous Score Agreement |
MAD: — r Пірсона: — |
Mean Absolute Difference (MAD) and Pearson r between scores of screening models on shared papers. |
| Score band | Works | Judged | 👍 | 👍 rate |
|---|---|---|---|---|
| 0.0–2.0 | 10554 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 4399 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1363 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1537 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1186 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 37 | 2 (too few) | 2 | 100% |
| Screening model | Seed recall | AUC | Sens / Spec | Prec / NPV | F₁ / AC₁ | 👍 / 👎 (TP/FP/FN/TN) |
|---|---|---|---|---|---|---|
gemini-3.1-flash-lite |
50% (1/2), 95% CI 9–91% | 0.63 | 100% (12/12), 95% CI 76–100% / 16% (3/19), 95% CI 6–38% | 43% (12/28), 95% CI 27–61% / 100% (3/3), 95% CI 44–100% | 0.60 / 0.05 | 12 / 19 (12/16/0/3) |
gemini-3.5-flash-lite |
— | — | — / 100% (1/1), 95% CI 21–100% | — / 100% (1/1), 95% CI 21–100% | — / 1.00 | 0 / 1 (0/0/0/1) |
— |
— | — | — / — | — / — | — / — | 0 / 0 (0/0/0/0) |
To narrow intervals, judge works from lower score bands: /admin/taste includes 20% random picks. Metrics update automatically with each verdict.
Methodological Rigor & Study Designs (11, 18)
Methodological rigor M (0–10) is evaluated during full-text analysis. Study design distribution reflects verified empirical classifications.
Mean Rigor (M)
6.46 / 10
Analyzed papers: 754
Quality Stratification
🟢 High rigor (M ≥ 7.0): 355
🟡 Moderate rigor (4.0 ≤ M < 7.0): 373
🔴 Low rigor (M < 4.0): 26
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 286 |
other | 55 |
theoretical | 27 |
rct | 17 |
assessment_design | 210 |
natural_experiment | 5 |
quasi_experiment | 61 |
review | 24 |
detector_evaluation | 48 |
survey | 7 |
experimental | 1 |
qualitative | 13 |
PRISMA 2020 27-Item Compliance Checklist
Full audit of compliance with PRISMA 2020 checklist (Page et al., 2021) for automated systematic reviews.
| № | Section / Item | Checklist Description | Status | Implementation |
|---|---|---|---|---|
| 1 | Title | Identify as living systematic review | ✓ Впроваджено | Page title, report metadata |
| 2 | Abstract | Structured summary (background, objectives, methods, results) | ✓ Впроваджено | #prisma-abstract block above |
| 3 | Rationale | Rationale in context of existing knowledge | ✓ Впроваджено | Topic description and rationale |
| 4 | Objectives | Explicit objectives using PICO / PECO | ✓ Впроваджено | #pico-framework table above |
| 5 | Eligibility criteria | Inclusion/exclusion criteria, language, dates | ✓ Впроваджено | Thresholds R, Score, Open Access |
| 6 | Information sources | Databases, registers, search dates | ✓ Впроваджено | arXiv, Crossref, OpenAlex, Semantic Scholar, ERIC, citations |
| 7 | Search strategy | Full search strategy, boolean filters, versions | ✓ Впроваджено | search_version, term groups, API limits |
| 8 | Selection process | Selection process, automation, independent screening | ✓ Впроваджено | LLM screening rotation + editorial taste loop |
| 9 | Data collection | Data collection from full-text reports | ✓ Впроваджено | PDF → MD parser, deep prompt v2.1 |
| 10a-b | Data items | List of outcomes and study characteristics | ✓ Впроваджено | Summary, sample, design, claims, countermeasures |
| 11 | Study risk of bias | Assessment of study methodological quality | ✓ Впроваджено | Rigor criterion M (0–10), quality distribution |
| 12 | Effect measures | Effect measures for outcomes | — Обґрунтовано N/A | Qualitative synthesis without meta-analytic pooled effects |
| 13a-f | Synthesis methods | Synthesis criteria and grouping methods | ✓ Впроваджено | Tag-based clustering, comparative study cards |
| 14 | Reporting bias | Assessment of reporting/publication bias | ✓ Впроваджено | Analysis of paywall exclusions & open access skew |
| 15 | Certainty assessment | Certainty assessment of body of evidence | ✓ Впроваджено | Automated quote grounding against full-text Markdown |
| 16 | Study selection | Selection results (flow diagram, counts) | ✓ Впроваджено | 100% stage conservation closure verified |
| 17 | Study characteristics | Characteristics of included studies table | ✓ Впроваджено | Interactive table with DOI, venue, year, tags |
| 18 | Risk of bias in studies | Risk of bias findings across studies | ✓ Впроваджено | Methodological rigor M & study designs table |
| 19 | Results of individual studies | Results for each individual study | ✓ Впроваджено | Public paper pages /doi/{doi} with full analysis |
| 20a-d | Results of syntheses | Results of syntheses & sub-groups | ✓ Впроваджено | Daily living editions, RSS feed, tag filters |
| 21 | Reporting biases | Results of reporting biases assessment | ✓ Впроваджено | Paywall n=387, retracted n=0 |
| 22 | Certainty of evidence | Summary of evidence certainty | ✓ Впроваджено | Gwet's AC₁, Cohen's κ, stratified recall, ROC AUC |
| 23a-d | Discussion | Discussion, limitations, implications | ✓ Впроваджено | Limitations section (23b, 23c) in methods |
| 24a-c | Registration & protocol | Registration, protocol availability, amendments | ✓ Впроваджено | Public repo, version history of search & rubric |
| 25 | Support | Financial & non-financial support | ✓ Впроваджено | Free API tiers declared in methods |
| 26 | Competing interests | Competing interests statement | ✓ Впроваджено | Declared in methods section |
| 27 | Data & code availability | Availability of data, analytical code, materials | ✓ Впроваджено | Export to CSV / JSON buttons, open-source code |
Limitations (23b, 23c)
- Open access only: paywalled papers never reach the synthesis, so the sample skews to open and preprint work.
- Screening ranks by quality and novelty rather than pure eligibility, so the sample skews towards counter-consensus work. Validity is measured against one expert (see above); a single LLM reviewer, model bias and version differences remain possible.
- The lexical filter and result caps can miss works with other terminology; sources skew English-language.
- Records scored under an older rubric version are not rescored automatically (see version table).
- Free Gemini quota: daily limits slow analysis; some selected works wait in the queue.
Registration, protocol, amendments, support, data (24–27)
- Registration: not registered. The protocol is the configuration (topic, queries, rubric, thresholds) in the project repository; it is reproduced in this block.
- Amendments: search versions (search_version) and rubric versions (rubric_version), tables above.
- Funding: none declared, free API tiers are used. Competing interests: none declared.
- Data: analyses and metadata are on article pages, RSS and sitemap; full texts are private (copyright) and not published.
Filter by Tags
Select one or more tags to refine the flow numbers and article corpus.
#psychometrics (115)
#Psychometrics (71)
#Item Response Theory (60)
#academic integrity (52)
#assessment validity (52)
#Assessment Validity (51)
#item response theory (46)
#higher education (44)
#Automated Essay Scoring (43)
#automated essay scoring (40)
#Generative AI (32)
#Construct Validity (31)
#LLM as rater (27)
#construct validity (27)
#Large Language Models (25)
#Higher Education (24)
#IRT (24)
#large language models (24)
#LLM Evaluation (23)
#GPT-4 (22)
#generative AI (22)
#assessment redesign (20)
#Academic Integrity (19)
#Automated Scoring (19)
#prompt engineering (19)
#ChatGPT (18)
#LLM as Rater (17)
#Prompt Engineering (17)
#automated scoring (17)
#Assessment Redesign (16)
#assessment design (16)
#assessment_design (16)
#AI-resilient assessment (14)
#validity (14)
#LLMs as raters (13)
#automated assessment (13)
#differential item functioning (13)
#Computer Science Education (12)
#Computerized Adaptive Testing (12)
#LLM-as-a-judge (12)
#academic_integrity (12)
#benchmarking (12)
#formative assessment (12)
#inter-rater reliability (12)
#Automated Item Generation (11)
#Differential Item Functioning (11)
#Knowledge Tracing (11)
#LLMs as Test Takers (11)
#Quadratic Weighted Kappa (11)
#assessment-validity (11)
#assessment_validity (11)
#rater drift (11)
#AI-resistant assessment (10)
#Inter-Rater Reliability (10)
#LLM evaluation (10)
#automated item generation (10)
#automated_essay_scoring (10)
#computerized adaptive testing (10)
#item_response_theory (10)
#AI-Resilient Assessment (9)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 15551 | scored |
Revealing Cultural and Linguistic Variation in Generative AI-Mediated Language Learning: A Mixed-Methods Study
Technology and language (2026) · doi:10.48417/technolang.2026.03.15 · v1.0 |
5.1 (gemini-3.5-flash-lite) | — |
| 15552 | Screened Out |
Bloomy: Measuring Cognitive Development and Detecting Unproductive Cognitive Offloading in Workplace AI Interactions
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23154975 · v1.0 |
4.4 (gemini-3.5-flash-lite) | — |
| 15553 | scored |
Foundations of Robotics
CRC Press eBooks (2026) · doi:10.1201/9781003512752 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15554 | scored |
Ai-Assisted Social Work Education: Bridging the Skills Gap in Rivers State Tertiary Institutions Faculty of Social Sciences, Sociology Department Rivers State University, Nkpolu Oroworokwo Port Harcourt, Nigeria
INTERNATIONAL JOURNAL OF SOCIAL SCIENCES AND MANAGEMENT RESEARCH (2026) · doi:10.56201/ijssmr.vol.12no9.2026.pg1.10 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15555 | scored |
Sustainable AI: Deployment and Opportunities for Statistical Practice
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23148548 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15556 | scored |
New Human Rights as a Framework for Political Design: Jean-Pol Martin and Robert Habeck
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23160199 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15557 | scored |
MultiCaRe-Thorax: A Curated Benchmark Dataset and Reproducible Labeling Framework for Multi-Label Thoracic Disease Classification in Chest X-Ray Imaging
Journal of Imaging (2026) · doi:10.3390/jimaging12100484 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15558 | scored |
A Patient Simulation Framework for Risk Assessment of Conversational Health Care AI: Development and Evaluation Study
JMIR AI (2026) · doi:10.2196/100772 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15559 | scored |
ARTIFICIAL INTELLIGENCE IN CLINICAL REHABILITATION: A SYSTEMATIC REVIEW OF CLINICAL APPLICATIONS, OUTCOMES AND IMPLEMENTATION CHALLENGES
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23156918 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15560 | scored |
A scoping review of pandemic-related knowledge hubs in South Africa and the continent: Structure, knowledge translation, and lessons for future pandemics
Open Science Framework (2026) · doi:10.17605/osf.io/n32rz · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15561 | Not Article |
INTEGRATING ARTIFICIAL INTELLIGENCE TOOLS INTO PROJECT-BASED ENGLISH LANGUAGE TEACHING
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23153235 · v1.0 |
- | — |
| 15562 | scored |
An Examination of Corporate Compliance Gaps in the Implementation of the EU Artificial Intelligence Act
International Journal of Law Management & Humanities (2026) · doi:10.63108/ijlmh.12941 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15563 | Not Article |
SARK derived source data and analysis results for cross-day electromyographic calibration
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23160017 · v1.0 |
- | — |
| 15564 | Screened Out |
Potential Risks and Countermeasures of Artificial Intelligence in Teaching: A Case Study of High School Art Education
Journal of Education Humanities and Social Sciences (2026) · doi:10.54097/351hnw76 · v1.0 |
1.1 (gemini-3.1-flash-lite) | — |
| 15565 | scored |
D3.2 - MultiX perception system enablers v2 Focus on MPS system design
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23159803 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15566 | Screened Out |
Potential Risks of Artificial Intelligence in Dance Education and Human-Centered Mitigation Strategies
Journal of Education Humanities and Social Sciences (2026) · doi:10.54097/r4qg0c45 · v1.0 |
1.1 (gemini-3.1-flash-lite) | — |
| 15567 | Screened Out |
"I Don't Have to Write an Essay Ever Again!": University Student Reflections on ChatGPT in the Classroom
Journal of Educational Technology Systems (2024) · doi:10.1177/00472395231219267 · v1.0 |
2.0 (gemini-3.1-flash-lite) | — |
| 15568 | Screened Out |
Bridging the Gap: Towards Guided Plagiarism Correction Strategies
Education and Information Technologies (2024) · doi:10.1007/s10639-024-12475-8 · v1.0 |
3.5 (gemini-3.1-flash-lite) | — |
| 15569 | Screened Out |
Student Perceptions of Peer Cheating Behaviour during COVID-19 Induced Online Teaching and Assessment
Higher Education Research and Development (2024) · doi:10.1080/07294360.2023.2258820 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 15570 | scored |
"I Can't Read This": Plagiarism, Biopolitics, and the Production of the Trans-Dividual Student
Educational Theory (2023) · doi:10.1111/edth.12598 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15571 | Screened Out |
Control-Deficit Leniency Bias in Work-Integrated Learning Assessment: How Lack of Control Leads to Grade Inflation
Assessment & Evaluation in Higher Education (2026) · doi:10.1080/02602938.2025.2539288 · v1.0 |
4.5 (gemini-3.1-flash-lite) | — |
| 15572 | Screened Out |
Islamic School Culture, Religious Leadership and Student Character in Preventing Academic Dishonesty: Empirical Evidence from Indonesian "Pesantren"
International Journal of Educational Management (2026) · doi:10.1108/ijem-10-2025-0795 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 15573 | Screened Out |
Re-Thinking Invigilation Implementation: A Mixed Method Approach to Student Perceptions and System Usability for Digital Assessment Adoption
International Journal for Educational Integrity (2025) · doi:10.1007/s40979-025-00183-w · v1.0 |
3.3 (gemini-3.1-flash-lite) | — |
| 15574 | Screened Out |
What Are the Perspectives of Day and Evening Nursing Education Students about Cheating?
Journal of Academic Ethics (2024) · doi:10.1007/s10805-023-09488-2 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 15575 | Screened Out |
What Students Really Think: Unpacking AI Ethics in Educational Assessments through a Triadic Framework
International Journal of Educational Technology in Higher Education (2025) · doi:10.1186/s41239-025-00556-8 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 15576 | No Fulltext |
Teachers Gaming the System: Exploring Opportunistic Behaviours in a Low-Stakes Accountability System
Educational Assessment, Evaluation and Accountability (2024) · doi:10.1007/s11092-023-09424-3 · v1.0 |
6.0 (gemini-3.1-flash-lite) | — |
| 15577 | Screened Out |
Who Cares about Grade Inflation? The Role of Institutional Policy in Regulating Course Grades in Higher Education
Assessment & Evaluation in Higher Education (2026) · doi:10.1080/02602938.2025.2572031 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 15578 | No Fulltext |
A Benchmark Dataset with Human Validation for AI-Assisted Technical Answer Evaluation
Discover Education (2026) · doi:10.1007/s44217-026-01975-z · v1.0 |
6.7 (gemini-3.5-flash-lite) | — |
| 15579 | Screened Out |
Applying the Curiosity-Confidence Crank: Building Critical Thinking in Higher Education through a Massive Open Online Course
Active Learning in Higher Education (2025) · doi:10.1177/14697874251329335 · v1.0 |
3.8 (gemini-3.5-flash-lite) | — |
| 15580 | Screened Out |
Students' Little Helper: Investigating Continuous-Use Determinants of Generative AI and Ethical Judgment
Education and Information Technologies (2025) · doi:10.1007/s10639-025-13708-0 · v1.0 |
2.7 (gemini-3.5-flash-lite) | — |
| 15581 | scored |
Can AI Replace Humans? Comparing the Capabilities of AI Tools and Human Performance in a Business Management Education Scenario
British Educational Research Journal (2025) · doi:10.1002/berj.4111 · v1.0 |
5.0 (gemini-3.5-flash-lite) | — |
| 15582 | Screened Out |
Experimenting with Generative AI to Create Personalized Learning Experiences for Twice-Exceptional and Multi-Exceptional Neurodivergent Students
Journal of Advanced Academics (2025) · doi:10.1177/1932202x251346349 · v1.0 |
2.4 (gemini-3.5-flash-lite) | — |
| 15583 | Screened Out |
Advancing Generative AI Literacy through a Faculty-Focused Micro-Credential
Journal of Information Systems Education (2026) · doi:10.62273/pbul9504 · v1.0 |
2.9 (gemini-3.5-flash-lite) | — |
| 15584 | Screened Out |
ChatGPT Risks in Academia: Examining University Educators' Challenges in Jordan
Education and Information Technologies (2025) · doi:10.1007/s10639-024-13009-y · v1.0 |
3.3 (gemini-3.5-flash-lite) | — |
| 15585 | Screened Out |
Metacognitive Regulation and ChatGPT-Assisted Writing: Introducing an Interactive Model for Cultivating Strategic Engagement
Discover Education (2026) · doi:10.1007/s44217-026-01282-7 · v1.0 |
3.7 (gemini-3.5-flash-lite) | — |
| 15586 | Screened Out |
Leveraging Generative AI in Higher Education: An Analysis of Opportunities and Challenges Addressed in University Guidelines
European Journal of Education (2025) · doi:10.1111/ejed.12891 · v1.0 |
2.9 (gemini-3.5-flash-lite) | — |
| 15587 | Screened Out |
Student Identification of the Social, Economic and Environmental Implications of Using Generative Artificial Intelligence (GenAI): Identifying Student Ethical Awareness of ChatGPT from a Scaffolded Multi-Stage Assessment
European Journal of Engineering Education (2026) · doi:10.1080/03043797.2025.2482830 · v1.0 |
4.7 (gemini-3.5-flash-lite) | — |
| 15588 | Screened Out |
University Students' Perceptions of Using Generative AI in Translation Practices
Instructional Science: An International Journal of the Learning Sciences (2025) · doi:10.1007/s11251-025-09705-y · v1.0 |
1.9 (gemini-3.5-flash-lite) | — |
| 15589 | No Fulltext |
"Is This Really Your Work?": A Qualitative Study of Teacher-Led Interviews and Student Accountability in the Age of Generative AI
Journal of Academic Ethics (2026) · doi:10.1007/s10805-025-09704-1 · v1.0 |
5.8 (gemini-3.5-flash-lite) | — |
| 15590 | scored |
Exploring Bias in Artificial Intelligence: Stereotypes and Gendered Narratives in Digital Imagery of Early Childhood Educators
Gender and Education (2026) · doi:10.1080/09540253.2025.2546048 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15591 | No Fulltext |
How Generative AI Influences Learning Outcomes in Programming Education: A Three-Level Bayesian Meta-Analysis
Educational Psychology Review (2026) · doi:10.1007/s10648-026-10211-x · v1.0 |
5.5 (gemini-3.5-flash-lite) | — |
| 15592 | Not Article |
aiDIF: Differential Item Functioning for AI-Scored Assessments
arXiv/Crossref (2026) · doi:10.32614/cran.package.aidif · v1.0 |
- | — |
| 15593 | Screened Out |
Cross-Cultural Validation and Ideological Fairness of a Historical Perspective Taking Instrument: Evidence from Czech Secondary Students
arXiv/Crossref (2026) · doi:10.31234/osf.io/hxngm_v2 · v1.0 |
2.5 (gemini-3.5-flash-lite) | — |
| 15594 | Not Article |
robustDIF: Differential Item Functioning Using Robust Scaling
arXiv/Crossref (2026) · doi:10.32614/cran.package.robustdif · v1.0 |
- | — |
| 15595 | Screened Out |
Validation of the Indonesian Version of the Student-Teacher Relationship Scale Using Rasch Analysis
G-Couns Jurnal Bimbingan dan Konseling (2026) · doi:10.31316/g-couns.v10i03.9351 · v1.0 |
2.6 (gemini-3.5-flash-lite) | — |
| 15596 | scored |
Garbage In, Garbage Out!: A Commentary on Bias in Standardized Assessments
Emerald Group Publishing Limited eBooks (2026) · doi:10.1108/978-1-80686-665-620261004 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15597 | Screened Out |
Development and validation of the Loss of Control–Alcohol (LOSS-A) scale: A mixed methods study with adult drinkers.
Psychology of Addictive Behaviors (2026) · doi:10.1037/adb0001143 · v1.0 |
3.4 (gemini-3.1-flash-lite) | — |
| 15598 | Screened Out |
Next-gen assessment for interdisciplinary learning: development and validation of the interdisciplinary science assessment of carbon cycling II
International Journal of Science Education (2026) · doi:10.1080/09500693.2026.2651920 · v1.0 |
3.1 (gemini-3.1-flash-lite) | — |
| 15599 | Screened Out |
Immersive Literary Learning through Augmented Reality: Evaluating Cognitive Engagement Using a Rasch Measurement Approach
Journal of Language Literature and Cultural Dynamics (2026) · doi:10.65840/jllcd.v3i1.38 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 15600 | Screened Out |
Differential Item Functioning of an Academic Stress Scale Among Generation Z Students in Indonesian Islamic Higher Education: A Rasch Rating Scale Model Analysis
Islamic Guidance and Counseling Journal (2026) · doi:10.25217/0020269781700 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |