AI in Education & AI-Resilient Assessment

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: 20150 → Screen: 18986 → Eligible: 2773 → 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	`w_M = 0.32`
E	Engineering Countermeasures	`w_E = 0.25`
C	Consensus Challenge	`w_C = 0.43`
N	Novelty	`w_N = 0.00`
R	Topic Relevance (0–10)	`R_min = 4.0, R_full = 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:

Score = {\begin{cases} 0, & if Gate \neq PASS or R < 4.0 \\ Q \cdot min (1, \frac{R}{7.0}), & if R \geq 4.0 \end{cases}

2. Weighted Quality Score (Q):

Q = \frac{\sum_{i} w_{i} \cdot S_{i}}{\sum_{i} w_{i}} = \frac{w_{M} \cdot M + w_{E} \cdot E + w_{C} \cdot C + w_{N} \cdot N}{w_{M} + w_{E} + w_{C} + w_{N}}

Q = \frac{0.32 \cdot M + 0.25 \cdot E + 0.43 \cdot C + 0.00 \cdot N}{1.00} = 0.32 \cdot M + 0.25 \cdot E + 0.43 \cdot C + 0.00 \cdot N

3. Logical Prefilter Screening Criterion:

Excluded \Leftrightarrow (Score < 5.0)

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	18044	2026-11-13 16:07
`ref` — reference lists of read works	2079	2026-11-13 14:28
`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`	18044	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`	9469
`gemini-3.5-flash-lite`	8853
`—`	730

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)	18588

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): 16240 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	10505	2 (too few)	0	0%
2.0–4.0	4380	0 (too few)	0	—
4.0–5.0	1355	2 (too few)	0	0%
5.0–6.0 ✓	1528	3	2	67%
6.0–8.0 ✓	1184	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.

#	Status / Stage	Paper	LLM Score	Expert Review
14701	Screened Out	The Body Acceptance by Others Scale-2: An examination of its psychometric properties in a French-Canadian adult sample Body Image (2026) · doi:10.1016/j.bodyim.2026.102045 · v1.0	2.1 (gemini-3.1-flash-lite)	—
14702	Screened Out	Investigating Gender-Based Differential Item Functioning in Secondary School Economics Test Items Using Item Response Theory International Journal of Innovative Science and Research Technology (IJISRT) (2026) · doi:10.38124/ijisrt/26feb192 · v1.0	1.9 (gemini-3.1-flash-lite)	—
14703	Screened Out	Development and validation of sport nutrition knowledge test for pre-service dieticians BMC Sports Science Medicine and Rehabilitation (2026) · doi:10.1186/s13102-026-01596-x · v1.0	2.1 (gemini-3.1-flash-lite)	—
14704	Screened Out	The Functionality Appreciation Scale: An examination of its psychometric properties in a French-Canadian adult sample Body Image (2026) · doi:10.1016/j.bodyim.2026.102044 · v1.0	2.1 (gemini-3.1-flash-lite)	—
14705	Screened Out	Using the M-DIF Online Platform: A Practical Tutorial for DIF Magnitude Estimation Educational methods and psychometrics (2026) · doi:10.65301/emp.2026.261 · v1.0	2.6 (gemini-3.1-flash-lite)	—
14706	Screened Out	Teachers’ perceptions of organizational dissent management in schools: Scale development and psychometric evaluation International Journal of Assessment Tools in Education (2026) · doi:10.21449/ijate.1735671 · v1.0	2.1 (gemini-3.1-flash-lite)	—
14707	Screened Out	Rethinking Item Fairness Using Single World Intervention Graphs Journal of Educational and Behavioral Statistics (2026) · doi:10.3102/10769986251415570 · v1.0	4.6 (gemini-3.1-flash-lite)	—
14708	Screened Out	Assessing the structural validity of the SCL-Dep6 depression subscale using rasch analysis Journal of Affective Disorders (2026) · doi:10.1016/j.jad.2026.121446 · v1.0	2.1 (gemini-3.1-flash-lite)	—
14709	Screened Out	A Comprehensive Differential Item Functioning Analysis of Physics Test Items in the Secondary School Certificate Examination Physical Education Health and Social Sciences (2026) · doi:10.63163/jpehss.v4i1.1111 · v1.0	1.9 (gemini-3.1-flash-lite)	—
14710	scored	Student altruism as a transversal educational value: a Rasch model analysis across cultural regions in Bali Retos (2026) · doi:10.47197/retos.v77.118535 · v1.0	0.0 (gemini-3.5-flash-lite)	—
14711	scored	Item response theory and differential item functioning analyses of the general self-efficacy scale for Koreans with disabilities Current Psychology (2026) · doi:10.1007/s12144-026-09061-1 · v1.0	0.0 (Flash Lite)	—
14712	scored	Realizing Fairness Through Accessibility for All Test Takers and for Specific Groups Educational Measurement (2026) · doi:10.1093/oso/9780197654965.003.0014 · v1.0	0.0 (gemini-3.5-flash-lite)	—
14713	scored	Beyond the Noise: Uncovering the Value of Inconsistent Signals in User-Generated Content for Hotel Performance Forecasting Journal of Travel Research (2026) · doi:10.1177/00472875261437258 · v1.0	0.0 (gemini-3.5-flash-lite)	—
14714	scored	Optimization and statistical detection for agricultural biosecurity and risk management arXiv/Crossref (2026) · doi:10.26153/tsw/64384 · v1.0	0.0 (gemini-3.5-flash-lite)	—
14715	Screened Out	Interrupted Time Series Methods for Nonrandom Sampling Study Designs With Known Sampling Weights Statistics in Medicine (2026) · doi:10.1002/sim.70560 · v1.0	5.0 (gemini-3.5-flash-lite)	—
14716	scored	Age modifies the vascular response to β3-adrenergic receptor agonism in females Physiology (2026) · doi:10.1152/physiol.2026.41.s1.2300726 · v1.0	0.0 (gemini-3.5-flash-lite)	—
14717	scored	A revision of the venous return-cardiac output graph using both "cardiac input" and a clinical key Physiology (2026) · doi:10.1152/physiol.2026.41.s1.2301186 · v1.0	0.0 (gemini-3.5-flash-lite)	—
14718	scored	A Survey on Financial Fraud Detection in E-commerce Transactions using Deep Learning Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21578005 · v1.0	0.0 (gemini-3.5-flash-lite)	—
14719	scored	An Adaptive Real-Time Threat Severity Assessment System Using Machine Learning for Low-Latency Decision Support in Defense Environments Preprints.org (2026) · doi:10.20944/preprints202605.0126.v1 · v1.0	0.0 (gemini-3.1-flash-lite)	—
14720	No Fulltext	An IRtree Model for Aberrant Response and Missing Data Multivariate Behavioral Research (2026) · doi:10.1080/00273171.2026.2612769 · v1.0	6.5 (gemini-3.5-flash-lite)	—
14721	scored	A Machine-Learning Model for Identifying Non-Credible Scholarly Journals Methods in Science and Technology Studies (2026) · doi:10.64539/msts.v2i2.2026.570 · v1.0	0.0 (gemini-3.1-flash-lite)	—
14722	Screened Out	Students’ Perceptions of Artificial Intelligence in English Language Learning International Journal of English and Applied Linguistics (IJEAL) (2026) · doi:10.47709/ijeal.v6i2.9582 · v1.0	1.7 (gemini-3.1-flash-lite)	—
14723	failed	Ethical Prompting Practices and Use of Generative AI Tools for Academic Writing by Librarians in Nigerian Universities: A Cross-Sectional Survey The Serials Librarian (2026) · doi:10.1080/0361526x.2026.2727376 · v1.0	-	—
14724	failed	The Application of Machine Learning and Pattern Recognition in Education: Opportunities and Challenges Exploring Science Academic Conference Series (2026) · doi:10.70267/aitia.2026160167 · v1.0	-	—
14725	Not Article	Chess Cheating at Master Level Figshare (2026) · doi:10.6084/m9.figshare.33684952 · v1.0	-	—
14726	failed	Teachers as Detectives? Professional Identity Conflict in the Era of Generative AI in Student Assessment Review of Artificial Intelligence in Education (2026) · doi:10.37497/rev.artif.intell.educ.v7ii.1391 · v1.0	-	—
14727	failed	DIGITAL LITERACY AND CRITICAL AI USE IN LIBRARIES: NAVIGATING THE HUMAN AND ETHICAL DIMENSIONS IN THE AI ERA Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23362137 · v1.0	-	—
14728	failed	ВОСПИТАТЕЛЬНЫЙ ПОТЕНЦИАЛ ИЗУЧЕНИЯ ЖИЗНЕННОГО ПУТИ НАЦИОНАЛЬНЫХ ЛИЧНОСТЕЙ НА УРОКАХ ИСТОРИИ Qazaqtaný (2026) · doi:10.54251/2522-4026.2026.31.07qaz · v1.0	-	—
14729	failed	PENGARUH KONSELING KELOMPOK REALITA UNTUK MENGUBAH POLA PIKIR MENYONTEK PADA PESERTA DIDIK KELAS VIII F SMP NEGERI 7 SURAKARTA TAHUN PELAJARAN 2023/2024 Pendas Jurnal Ilmiah Pendidikan Dasar (2026) · doi:10.23969/jp.v11i03.67122 · v1.0	-	—
14730	failed	Perspectivas Gubernamentales y No Gubernamentales sobre el Uso Ético de la Inteligencia Artificial Generativa en la Educación Universitaria: Desafíos y Consecuencias Revista de Estudios y Experiencias en Educación (2026) · doi:10.21703/rexe.v25i.3640 · v1.0	-	—
14731	failed	Governing Artificial Intelligence in East African Higher Education: A Comparative Policy Readiness Assessment of Eight EAC Partner States OSF Preprints (OSF Preprints) (2026) · doi:10.17605/osf.io/2vp3n · v1.0	-	—
14732	failed	Generative AI Adoption and Governance in African Higher Education: A Mixed-Methods Study of Ghanaian Universities Journal of Education and Learning Reviews (2026) · doi:10.60027/jelr.2026.e3787 · v1.0	-	—
14733	failed	How AI Writing Tools Change Student Learning Habits: A Review of Previous Research International Journal of Innovative Research in Technology (2026) · doi:10.64643/ijirt.208438-459 · v1.0	-	—
14734	failed	AI-Aligned Assessment Practices for a Future-Ready Higher Education System The Proceedings of the International Conference on Advanced Research in Teaching and Education. (2026) · doi:10.33422/icate.v3i1.2114 · v1.0	-	—
14735	failed	Hype versus Reality CRC Press eBooks (2026) · doi:10.1201/9781003662945-4 · v1.0	-	—
14736	failed	THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING ENGLISH LANGUAGE LEARNING Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.22750858 · v1.0	-	—
14737	failed	LINGUISTIC FEATURES IN ARTIFICIAL INTELLIGENCE TEXTS: STYLISTIC DIFFERENCES BETWEEN HUMAN AND AI-GENERATED CONTENT Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.22745149 · v1.0	-	—
14738	scored	FORMATION OF A CULTURE OF ACADEMIC INTEGRITY IN THE CONTEXT OF THE SPIRITUAL AND MORAL DEVELOPMENT OF LEARNERS Innovate Pedagogy (2026) · doi:10.32782/ip/98.33 · v1.0	0.0 (Flash Lite)	—
14739	failed	From Academic Integrity to Institutional Stewardship: A Reflexive and Responsible Innovation Paradigm for Generative AI in Higher Education Education Sciences (2026) · doi:10.3390/educsci16091504 · v1.0	-	—
14740	failed	Doctoral Expansion without Quality Gains: Lessons from Uzbekistan International Higher Education (2026) · doi:10.6017/895b9e0d.04ed398e · v1.0	-	—
14741	failed	Trends and Bibliometric Mapping of the Knowledge Structure of Generative Artificial Intelligence Research in Higher Education Black Sea Journal of Engineering and Science (2026) · doi:10.34248/bsengineering.1934728 · v1.0	-	—
14742	Not Article	Peer Review Report For: Protocol for evaluating the efficacy of GoaleTest, an AI-based vocational guidance system using RIASEC and Nonlinear Vector Alignment [version 1; peer review: 1 not approved] arXiv/Crossref (2026) · doi:10.5256/f1000research.206166.r509522 · v1.0	-	—
14743	failed	Measuring Machine Habitus: A Pre-Registered Multiple Correspondence Analysis of LLM Disposition Space Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21982392 · v1.0	-	—
14744	failed	Development of a Scale for Irrational Beliefs About Artificial Intelligence Applications and Prevalence Rates Among University Students Dirasat Human and Social Sciences (2026) · doi:10.35516/hum.2026.11757 · v1.0	-	—
14745	failed	Multiagent Large Language Model Framework for Psychotherapy Fidelity Assessment in Motivational Interviewing and Cognitive Behavioral Therapy Training: Cross-Sectional, Simulation-Based Evaluation Study JMIR Medical Education (2026) · doi:10.2196/92964 · v1.0	-	—
14746	Not Article	Neuromyths in Faculty Members Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21987813 · v1.0	-	—
14747	failed	Assessment for Learning in the Era of Artificial Intelligence: Rethinking Feedback, Evaluation, and Academic Integrity arXiv/Crossref (2026) · doi:10.66857/srh4-c166 · v1.0	-	—
14748	failed	Acceptance and Need for AI Applications in Healthcare: A Cross-Sectional Survey Study Comparing Physicians, Healthcare Professionals, and the General Population (Preprint) arXiv/Crossref (2026) · doi:10.2196/preprints.102580 · v1.0	-	—
14749	failed	Artificial Intelligence–Enabled Simulation for Competency Assessment, Feedback, and Progression in Postgraduate Medical Education arXiv/Crossref (2026) · doi:10.17605/osf.io/zkh9s · v1.0	-	—
14750	failed	Artificial Intelligence and the Evolution of English Language Acquisition: Opportunities and Challenges for ESL Pedagogy in the Digital Age Iconic Research and Engineering Journals (2026) · doi:10.64388/irev10i2-1722381 · v1.0	-	—