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: 20016 →
Screen: 18854 →
Eligible: 2754 →
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 | 17917 | 2026-11-13 10:26 |
ref — reference lists of read works | 2072 | 2026-11-13 10:28 |
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 | 17917 | 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 | 9392 |
gemini-3.5-flash-lite | 8804 |
— | 724 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 263 |
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) | 18456 |
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): 16127 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 | 10432 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 4349 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1346 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1520 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1173 | 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: 752
Quality Stratification
🟢 High rigor (M ≥ 7.0): 355
🟡 Moderate rigor (4.0 ≤ M < 7.0): 371
🔴 Low rigor (M < 4.0): 26
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 286 |
other | 54 |
theoretical | 27 |
rct | 17 |
assessment_design | 209 |
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=384, 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)
#Automated Essay Scoring (43)
#higher education (43)
#automated essay scoring (40)
#Generative AI (32)
#Construct Validity (31)
#construct validity (27)
#LLM as rater (26)
#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)
#automated item generation (10)
#automated_essay_scoring (10)
#computerized adaptive testing (10)
#item_response_theory (10)
#AI-Resilient Assessment (9)
#DIF (9)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 12551 | Screened Out |
From tools to pedagogical ecologies: a systematic review of artificial intelligence in EFL/ESL education
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1859248 · v1.0 |
3.2 (gemini-3.1-flash-lite) | — |
| 12552 | Not Article |
Journal of Educational Technology Development and Exchange (2026) · doi:10.18785/jetde.1903 · v1.0 |
- | — |
| 12553 | scored |
Development and Validation of the ATRAI Questionnaire to Assess Attitudes Toward Large Language Models in Clinical Setting (ATRAI-LLM)
European Journal of Investigation in Health Psychology and Education (2026) · doi:10.3390/ejihpe16070094 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 12554 | Screened Out |
'Shaping Tomorrow's Dental Education'- Novel Teaching, Learning, and Assessment Innovations- A Review.
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21069021 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 12555 | scored |
Escala de percepciones hacia la inteligencia artificial: adaptación cultural y validación en Ciencias del Deporte
Revista Iberoamericana de Ciencias de la Actividad Física y el Deporte (2026) · doi:10.24310/riccafd.15.2.2026.23081 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 12556 | In Buffer |
AI-Based Question Paper Generation and Quality Optimization: An Integrated Computational Approach to Intelligent Assessment Construction
Indian Journal of Computer Science and Technology (2026) · doi:10.59256/indjcst.20260502085 · v1.0 |
2.6 (gemini-3.7-flash) | — |
| 12557 | scored |
Development of a Troll Behavior Perception Scale in Artificial Intelligence Tools
Journal of Family Counseling and Education (2026) · doi:10.32568/jfce.1842083 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 12558 | Screened Out |
Artificial Intelligence, Self-Regulated Learning, and Academic Achievement: A Structural Equation Modeling Approach in Higher Education
Research consortium archive. (2026) · doi:10.5281/zenodo.21059361 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 12559 | Not Article |
Modeling Elementay Teachers' Readiness to Adopt Generative AI in Instructional Decision-Making: A Nationwide Quantitative Survey in Indonesia
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21076928 · v1.0 |
- | — |
| 12560 | Screened Out |
ЦИФРОВІ ІНСТРУМЕНТИ У МУЛЬТИЛІНГВАЛЬНОМУ ОСВІТНЬОМУ СЕРЕДОВИЩІ ПІДГОТОВКИ СУЧАСНОГО ФАХІВЦЯ
Physical and Mathematical Education (2026) · doi:10.31110/fmo2026.v41i3-03 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 12561 | Screened Out |
The Effects of Large Language Models on the Generation of Multiple-choice Questions in Medical Education: A Scoping Review
Sudan Journal of Medical Sciences (2026) · doi:10.18502/sjms.v21i2.19868 · v1.0 |
2.5 (gemini-3.1-flash-lite) | — |
| 12562 | scored |
ВІД САМОПЕРЕВІРКИ ДО ВЗАЄМНОГО РЕЦЕНЗУВАННЯ: РОЗВИТОК КОГНІТИВНО-ВЕРИФІКАЦІЙНОГО ПІДХОДУ ДО ОЦІНЮВАННЯ МАТЕМАТИЧНИХ ЗАВДАНЬ В УМОВАХ ГЕНЕРАТИВНОГО ШТУЧНОГО ІНТЕЛЕКТУ
Physical and Mathematical Education (2026) · doi:10.31110/fmo2026.v41i3-05 · v1.0 |
5.2 (gemini-3.1-flash-lite) | — |
| 12563 | scored |
Benchmarking SLM and LLM in Feature Generation for Interpretable Automated Essay Scoring
IEEE International Conference on Information Reuse and Integration for Data Science (IRI) (2026) · doi:10.1109/iri69576.2026.00023 · v1.0 |
0.0 (Flash Lite) | — |
| 12564 | Screened Out |
TEACHING SURGERY BEFORE EXECUTION ARTIFICIAL INTELLIGENCE IN PRE-OPERATIVE SURGICAL TRAINING
Iterative International Publishers eBooks (2026) · doi:10.58532/nbennur3151c4 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 12565 | Screened Out |
What Factors Influence Chinese EFL Learners’ Adoption of GenAI Tools for English Speaking Practice? An Extended Unified Theory of Acceptance and Use of Technology Perspective
SAGE Open (2026) · doi:10.1177/21582440261468418 · v1.0 |
2.3 (gemini-3.1-flash-lite) | — |
| 12566 | scored |
An overview of infrared thermography in bovine reproduction
International Journal of Veterinary Sciences and Animal Husbandry (2026) · doi:10.22271/veterinary.2026.v11.i7e.3650 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 12567 | scored |
A Decade of Interventional Radiotherapy (Brachytherapy): Progress, Challenges, and Future Prospects – A Bibliometric Analysis
Technology in Cancer Research & Treatment (2026) · doi:10.1177/15330338261415801 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12568 | Screened Out |
HRM and AI: An HR-Centered Perspective on Fairness, Transparency, and Human Adaptation at Work
Academy of Management Proceedings (2026) · doi:10.5465/amproc.2026.12105symposium · v1.0 |
3.1 (gemini-3.5-flash-lite) | — |
| 12569 | scored |
No More “Tell Me About Yourself”: Generative AI for Scaling Structured Job Interview Guides
Academy of Management Proceedings (2026) · doi:10.5465/amproc.2026.12139abstract · v1.0 |
5.0 (gemini-3.5-flash-lite) | — |
| 12570 | scored |
Making Discussion Content Visible: Language Models and a New Frontier in Teams Research
Academy of Management Proceedings (2026) · doi:10.5465/amproc.2026.21343abstract · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12571 | scored |
Human-AI Trust: Theory Development and Empirical Examination in Organizational Contexts
Academy of Management Proceedings (2026) · doi:10.5465/amproc.2026.18440abstract · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12572 | scored |
Artificial Intelligence Orientation: Towards an Integrated Model
Academy of Management Proceedings (2026) · doi:10.5465/amproc.2026.20056abstract · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12573 | Screened Out |
How large language models react to contradictions in one prompt
Figshare (2026) · doi:10.6084/m9.figshare.34057152 · v1.0 |
2.5 (gemini-3.5-flash-lite) | — |
| 12574 | scored |
Designing AIM-Y: Design Considerations for AI Conversational Agents for Health Communication
Figshare (2026) · doi:10.6084/m9.figshare.34060002 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12575 | scored |
Integrating STEM and Artificial Intelligence with English Educa-tion in Cram School
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23102057 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12576 | scored |
Artificial Intelligence in School Mathematics Education: A Bibliometric Trend Analysis
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23093834 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12577 | scored |
Remote‐Sensing Innovations for Mapping Biodiversity Patterns and Forest Ecosystem Functions
arXiv/Crossref (2026) · doi:10.1002/9781394416578.ch20 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12578 | scored |
Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems
Next Sustainability (2026) · doi:10.1016/j.nxsust.2026.100535 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12579 | scored |
Artificial intelligence co-creation of e-learning tools, a pilot trial of a targeted fidelity training simulator for relative afferent pupillary defects (Preprint)
arXiv/Crossref (2026) · doi:10.2196/preprints.113507 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12580 | scored |
Artificial Intelligence and Accountability of Automated Decisions
arXiv/Crossref (2026) · doi:10.63108/vab.ldd.1.9 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12581 | Screened Out |
AI-Assisted Instructional Design Studios for Literacy and UDL in School-University Partnerships
IGI Global eBooks (2026) · doi:10.4018/979-8-3373-9519-7.ch001 · v1.0 |
2.3 (gemini-3.5-flash-lite) | — |
| 12582 | Screened Out |
DESAFIOS ÉTICOS E ESTRATÉGIAS DE GESTÃO ESCOLAR FRENTE À INTELIGÊNCIA ARTIFICIAL: UMA REVISÃO SISTEMÁTICA DA LITERATURA
Revista Ibero-Americana de Humanidades, Ciências e Educação (2026) · doi:10.51891/rease.v12i10.30726 · v1.0 |
1.6 (gemini-3.5-flash-lite) | — |
| 12583 | scored |
A Focused Narrative Review of Selected Evidence on Gymnastics Training Optimization, Technology, Athlete Health, and Injury Prevention
Healthcare (2026) · doi:10.3390/healthcare14193285 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12584 | scored |
Attention, Emotion, and Authenticity: Eye-Tracking Evidence from AI vs. Human Visual Design
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23101348 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12585 | Screened Out |
CreaRobot - Improving creativity using a social robot
Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) (2026) · doi:10.1145/3776591.3832523 · v1.0 |
2.8 (gemini-3.5-flash-lite) | — |
| 12586 | scored |
AI-Driven Personalization and Adaptive Learning in Decentralized Environments
IGI Global eBooks (2026) · doi:10.4018/979-8-2600-1363-2.ch008 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12587 | scored |
**From AI to Superintelligence: Rogue Agents, Voluntary Accords, and the Global Governance Impasse — A Research Analysis of the September 2026 AI Containment Crisis and the Limits of Industry Self-Regulation**
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23096316 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12588 | scored |
Predicting Faults in Mechanical Systems Using Artificial Intelligence and Machine Learning
International Journal of Innovative Science and Research Technology (IJISRT) (2026) · doi:10.38124/ijisrt/26sep1075 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12589 | scored |
Data and supplementary materials for a systematic literature review on AI-generated images as educational visual resources
Mendeley Data (2026) · doi:10.17632/p564gfk49n · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12590 | scored |
Clinician-centered evaluation of large language model-generated discharge summaries for longer hospitalizations
npj Digital Medicine (2026) · doi:10.1038/s41746-026-03320-y · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12591 | scored |
Artificial Intelligence in 3D Aortic CTA Segmentation for Abdominal Aortic Aneurysm Management: A Narrative Review
Journal of Imaging (2026) · doi:10.3390/jimaging12100479 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12592 | scored |
Artificial Intelligence and machine learning in toxicogenomics for drug safety assessment: a critical review
Toxicology Mechanisms and Methods (2026) · doi:10.1080/15376516.2026.2741364 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12593 | Not Article |
Index
arXiv/Crossref (2026) · doi:10.1002/9781394416578.index · v1.0 |
- | — |
| 12594 | Screened Out |
Extending the Affective Process and Strategic Engagement Framework to Address Writer's Block in Ethiopian English as a Foreign Language Contexts through Critical Artificial Intelligence Literacy and Ethical Generative Artificial Intelligence in Low-Resource Settings
Discover Education (2026) · doi:10.1007/s44217-026-02049-w · v1.0 |
4.2 (gemini-3.1-flash-lite) | — |
| 12595 | Screened Out |
Unpacking teachers’ cognitive engagement strategies with technology by employing the ICAP-TS
Computers and Education Open (2025) · doi:10.1016/j.caeo.2025.100259 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 12596 | scored |
Evaluating pain outcomes in Chinese ophthalmology patients using the APS-POQ-R-C: a Rasch analysis
Frontiers in Psychology (2025) · doi:10.3389/fpsyg.2025.1558111 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 12597 | scored |
A Global Scale of Economic Left–Right Party Positions: Cross-National and Cross-Expert Perceptions of Party Placements
The Journal of Politics (2025) · doi:10.1086/736578 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 12598 | scored |
Evidence of Measurement Invariance in the Working Alliance Inventory Across In-Person and Videoconferencing Psychotherapy
Psychotherapy Research (2025) · doi:10.1080/10503307.2025.2495827 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 12599 | scored |
Validation of the Global Scale for Early Development (GSED) for children 0 to 36 months of age in the Pacific: protocol
Public Health in Practice (2025) · doi:10.1016/j.puhip.2025.100615 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 12600 | scored |
Study of the psychometric properties of the HLS-EU-12 questionnaire in rural Bangladesh
PLoS ONE (2025) · doi:10.1371/journal.pone.0323608 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |