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: 17315 →
Screen: 16299 →
Eligible: 2428 →
Included: 84
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 = 84 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 | 15497 | 2026-11-07 10:23 |
ref — reference lists of read works | 1791 | 2026-11-07 10:25 |
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 | 15497 | 2026-10-06 | 2026-11-07 |
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 | 8071 |
gemini-3.5-flash-lite | 7675 |
— | 619 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 172 |
gemini-3.8-flash | v2.1 | 106 |
gemini-3.7-flash | v2.1 | 99 |
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) | 15901 |
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): 13898 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 | 8987 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3747 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1164 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1325 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1049 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 30 | 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)
6.56 / 10
Analyzed papers: 642
Quality Stratification
🟢 High rigor (M ≥ 7.0): 322
🟡 Moderate rigor (4.0 ≤ M < 7.0): 302
🔴 Low rigor (M < 4.0): 18
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 247 |
other | 45 |
theoretical | 27 |
rct | 13 |
assessment_design | 169 |
natural_experiment | 5 |
quasi_experiment | 56 |
detector_evaluation | 42 |
survey | 7 |
review | 19 |
experimental | 1 |
qualitative | 11 |
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=338, 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 (103)
#Psychometrics (61)
#Item Response Theory (49)
#academic integrity (47)
#Assessment Validity (38)
#assessment validity (38)
#item response theory (38)
#higher education (37)
#Automated Essay Scoring (31)
#automated essay scoring (31)
#Generative AI (29)
#construct validity (26)
#Construct Validity (24)
#IRT (24)
#LLM as rater (22)
#generative AI (20)
#Academic Integrity (19)
#Higher Education (19)
#Large Language Models (18)
#Automated Scoring (17)
#assessment redesign (17)
#LLM Evaluation (16)
#assessment_design (16)
#large language models (16)
#automated scoring (15)
#prompt engineering (15)
#AI-resilient assessment (14)
#GPT-4 (14)
#LLM as Rater (14)
#Assessment Redesign (13)
#assessment design (13)
#automated assessment (13)
#differential item functioning (13)
#validity (13)
#ChatGPT (12)
#Prompt Engineering (12)
#academic_integrity (12)
#LLMs as raters (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#inter-rater reliability (11)
#Automated Item Generation (10)
#LLM-as-a-judge (10)
#LLMs as Test Takers (10)
#item_response_theory (10)
#Computerized Adaptive Testing (9)
#DIF (9)
#Inter-Rater Reliability (9)
#LLM evaluation (9)
#concept drift (9)
#formative assessment (9)
#rater drift (9)
#AI-resistant assessment (8)
#Assessment Design (8)
#Computer Science Education (8)
#Differential Item Functioning (8)
#Knowledge Tracing (8)
#Validity (8)
#automated_essay_scoring (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 5101 | Screened Out |
Comparing student and educator perceptions of generative artificial intelligence for teaching, learning and assessment: results from two institutional surveys
Artificial Intelligence in Education (2026) · doi:10.1108/aiie-08-2025-0211 · v1.0 |
3.3 (gemini-3.5-flash-lite) | — |
| 5102 | scored |
Teachers’ TPACK Considerations Regarding Opportunities and Challenges of Using GenAI in K-12 Education
Technology Knowledge and Learning (2026) · doi:10.1007/s10758-026-10002-x · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5103 | Screened Out |
A Phenomenological Study of Undergraduate Students Experiences Using Artificial Intelligence Tools to Enhance Academic Writing Skills
JOURNAL OF EDUCATIONAL SCIENCES (2026) · doi:10.31258/jes.10.7.p.579-593 · v1.0 |
2.1 (gemini-3.5-flash-lite) | — |
| 5104 | Screened Out |
Bridging the Faculty- Student Paradox: A Pragmatic One Year Roadmap for AI Integration in Indian Higher Education
Integrated Journal for Research in Arts and Humanities (2026) · doi:10.55544/ijrah.6.4.4 · v1.0 |
2.2 (gemini-3.5-flash-lite) | — |
| 5105 | Screened Out |
Generative AI Governance in Higher Education (Systematic Literature Review)
Academic Journal of Research and Scientific Publishing (2026) · doi:10.52132/ajrsp.e.2026.87.2 · v1.0 |
2.8 (gemini-3.5-flash-lite) | — |
| 5106 | Screened Out |
Artificial intelligence in higher education: Psychological implications for students
International journal of life sciences & earth sciences (2026) · doi:10.21744/ijle.v9n1.2493 · v1.0 |
1.9 (gemini-3.5-flash-lite) | — |
| 5107 | Not Article |
Redesigning Assessment Against Generative AI: A Controlled Comparison of Four Formats on Academic Integrity and Learning Outcomes (Dataset)
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21201954 · v1.0 |
- | — |
| 5108 | Screened Out |
Moroccan University Teachers’ Perceptions of Moodle Use in Learning Assessment: A Disciplinary Comparison
Journal of Educational and Social Research (2026) · doi:10.36941/jesr-2026-0379 · v1.0 |
2.1 (gemini-3.5-flash-lite) | — |
| 5109 | scored |
Evaluating the performance of 3 large language models in higher education essay-like assessments in 2024 and 2026
American Journal of STEM Education (2026) · doi:10.32674/tfptev94 · v1.0 |
5.3 (gemini-3.5-flash-lite) | — |
| 5110 | In Buffer |
Criminal Minds: How First-Year CS Students Plagiarize Code
ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) (2026) · doi:10.1145/3803437.3805789 · v1.0 |
4.1 (gemini-3.6-flash) | — |
| 5111 | scored |
Model Transmisi Keilmuan dalam Peradaban Islam Abad Pertengahan dan Kontribusinya terhadap Pengembangan Pendidikan Islam Kontemporer
MUARA PENDIDIKAN Jurnal Ilmiah Ilmu Pendidikan & Sosial Humaniora (2026) · doi:10.64365/muradik.v2i3.423 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5112 | Screened Out |
A Boon or Bane? Challenges and Opportunities of AI Adoption in Higher Education
Journal of Higher Education Theory and Practice (2026) · doi:10.33423/7y7p5d66 · v1.0 |
1.7 (gemini-3.5-flash-lite) | — |
| 5113 | scored |
Artificial Intelligence and Academic Integrity: Challenges to Educational Integrity in the Digital Transformation
International Journal of Sustainable Social Science (IJSSS) (2026) · doi:10.59890/ijsss.v4i3.260 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5114 | scored |
Teachers' Discipline Management and Students' Learning Outcomes in Public Lower Secondary Schools in Kirehe District, Rwanda
arXiv/Crossref (2026) · doi:10.5281/zenodo.21234059 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5115 | scored |
ARTIFICIAL INTELLIGENCE AND SPECIFICATION WRITING IN ARCHITECTURAL EDUCATION: A FRAMEWORK FOR RESPONSIBLE AI INTEGRATION
International Journal of Renewable Energy and Environment (2026) · doi:10.5281/zenodo.21223928 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5116 | Screened Out |
Navigating generative AI in higher education: Freshers’ perceptions of ethics in AI, digital learning behavior, and institutional norms
Asian Journal of Contemporary Education (2026) · doi:10.55493/5052.v10i2.6098 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 5117 | Screened Out |
Decision Milestones and Decision-Based Learning in a Canine Lab Exercise: How Students Decide
Journal of Statistics and Data Science Education (2026) · doi:10.1080/26939169.2026.2699298 · v1.0 |
4.6 (gemini-3.1-flash-lite) | — |
| 5118 | No Fulltext |
PBIS-RISE: FROM TRADITIONAL TO GENERATIVE AI PSYCHOMETRIC SYSTEM FOR CONTEXT-SENSITIVE ASSESSMENT OF STUDENT BEHAVIOR
INTED proceedings (2026) · doi:10.21125/inted.2026.2020 · v1.0 |
6.4 (gemini-3.1-flash-lite) | — |
| 5119 | Screened Out |
CareerPath AI: A Hybrid Machine Learning And Large Language Model Framework For Multi-level Career Recommendation With Real-Time Job Market Integration
International journal of engineering science and advanced technology. (2026) · doi:10.64771/ijesat.2026.v26.i03.2149 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 5120 | scored |
Defining competence in health care professions: Peer consensus, credentialing standards, and psychometric rigor
Excellence and Credentialing in Health Care (2026) · doi:10.1097/echc.0000000000000010 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5121 | scored |
Artificial Intelligence–Based Automated Scoring to Support Dermatology Consultations
JEADV Clinical Practice (2026) · doi:10.1002/jvc2.70270 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5122 | abstract_only |
Blind Spots in AI-Based Longitudinal Psychological Inference: A Single-Subject Validation Study
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.18825306 · v1.0 |
7.5 (gemini-3.1-flash-lite) | — |
| 5123 | scored |
A Unified Psychological Language System for the Functional Layer: Introducing the M-R-D-S State Grammar and G-M-A-R Operation Grammar
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.18818306 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5124 | No Fulltext |
PERSONALITY BY PROMPT: EVALUATING GPT-GENERATED PERSONALITY QUESTIONNAIRES FOR EDUCATIONAL ASSESSMENT
INTED proceedings (2026) · doi:10.21125/inted.2026.1181 · v1.0 |
5.9 (gemini-3.1-flash-lite) | — |
| 5125 | Screened Out |
Integration of Artificial Intelligence Into Extended Reality Debriefing in Healthcare Simulation: A Narrative Review
Advances in Medical Education and Practice (2026) · doi:10.2147/amep.s578857 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 5126 | scored |
AI-Assisted Sentencing Modeling Under Explainability Constraints: Framework Design and Judicial Applicability Analysis
Information (2026) · doi:10.3390/info17030234 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5127 | In Buffer |
A methodology for using large language models to compile funds of assessment tools in higher education
RUDN Journal of Informatization in Education (2026) · doi:10.22363/2312-8631-2026-23-1-57-74 · v1.0 |
0.0 (gemini-3.6-flash) | — |
| 5128 | scored |
An AI-Driven Vendor Risk Assessment and Compliance Management System with Automated Scoring and Document Intelligence
International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) (2026) · doi:10.1109/icmsci67830.2026.11469274 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5129 | Screened Out |
AI Literacy and Training Needs in Midwifery Education: A National Mixed-Methods Study in France. (Preprint)
arXiv/Crossref (2026) · doi:10.2196/preprints.94310 · v1.0 |
3.5 (gemini-3.1-flash-lite) | — |
| 5130 | Screened Out |
Benchmarking the Quality of Pedagogical Design in a Massive Open Online Learning Proposal from UNED
American Journal of Distance Education (2026) · doi:10.1080/08923647.2026.2635220 · v1.0 |
2.3 (gemini-3.1-flash-lite) | — |
| 5131 | Screened Out |
Mapping the Integration of AI into Business Education: Insights from a Decade of Research
Journal of University Teaching and Learning Practice (2026) · doi:10.53761/5yhdnk13 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 5132 | Screened Out |
Construction and validation of the AI Ethics Scale in language research
Australasian Journal of Educational Technology (2026) · doi:10.14742/ajet.11034 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 5133 | scored |
The multiverse of zebrafish within the nanoworld
Journal of Controlled Release (2026) · doi:10.1016/j.jconrel.2026.114758 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5134 | Screened Out |
ARTIFICIAL INTELLIGENCE AND DIFFERENTIATED INSTRUCTION IN 9TH-GRADE INFORMATICS: THEORETICAL FOUNDATIONS AND ALGORITHMIC FRAMEWORK
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.18844340 · v1.0 |
4.5 (gemini-3.5-flash-lite) | — |
| 5135 | scored |
PAKISTANI ESL UNDERGRADUATES' PERCEPTIONS ABOUT THE ROLE OF CHATGPT IN ACADEMIC WRITING
Natural Language Processing Journal (2026) · doi:10.1016/j.nlp.2026.100240 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5136 | scored |
Bridging Personalization and Pedagogy: A Systematic Review of Artificial Intelligence in Vocabulary Learning
SAGE Open (2026) · doi:10.1177/21582440261498103 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5137 | Screened Out |
Enhancing EFL Writing With GenAI: Design, Testing, and Pedagogical Guidelines for Chinese University Classrooms
SAGE Open (2026) · doi:10.1177/21582440261497766 · v1.0 |
3.9 (gemini-3.5-flash-lite) | — |
| 5138 | scored |
The Accessibility of Compositional Thinking: Artificial Intelligence and Special Educational Needs
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23433575 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5139 | scored |
A comparative cross-sectional study on the accuracy, readability, and understandability of AI-generated (ChatGPT and Google Gemini) versus traditional patient information sheets for labor epidural analgesia
Journal of Anaesthesiology Clinical Pharmacology (2026) · doi:10.4103/joacp.joacp_437_25 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5140 | scored |
Routine MRI in Pediatric Scoliosis: A Narrative Review of Risk-Stratified and Abbreviated Protocols
Orthopedic Research and Reviews (2026) · doi:10.2147/orr.s649642 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5141 | scored |
MT3 INFLUENCE OF ARTIFICIAL INTELLIGENCE ON CLINICAL DECISION-MAKING IN HEALTHCARE: A KAP STUDY AMONG FUTURE PHARMACISTS
Value in Health Regional Issues (2026) · doi:10.1016/j.vhri.2026.102002 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5142 | scored |
Choroid plexus pathophysiology and artificial intelligence in hydrocephalus: Toward mechanism-informed diagnosis and therapy
Genes & Diseases (2026) · doi:10.1016/j.gendis.2026.102542 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5143 | scored |
Generative artificial intelligence and automated scoring in curriculum-based measurement for elementary students with early writing needs
Assessing Writing (2026) · doi:10.1016/j.asw.2026.101134 · v1.0 |
0.0 (Flash Lite) | — |
| 5144 | Screened Out |
From Breaking Point to Breakthrough: How AI Is Reinventing Assessment in Higher Education and Corporate Learning
Human capital leadership. (2026) · doi:10.70175/hclreview.2020.39.4.1 · v1.0 |
4.2 (gemini-3.5-flash-lite) | — |
| 5145 | scored |
Mathematical analysis of advanced thermal assessment of nanofluids using a Levenberg–Marquardt-based feed forward artificial neural network
Results in Surfaces and Interfaces (2026) · doi:10.1016/j.rsurfi.2026.101021 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5146 | scored |
Beyond "AI Can Make Mistakes": An Evidence, Expertise and Outcome Framework for Assessing AI Recommendations
OSF Preprints (OSF Preprints) (2026) · doi:10.5281/zenodo.23439358 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5147 | scored |
HTA39 ARTIFICIAL INTELLIGENCE APPLICATIONS IN HEALTH TECHNOLOGY ASSESSMENT ACROSS ASIA-PACIFIC: A TARGETED REVIEW OF USE CASES, ADOPTION BARRIERS, AND OPPORTUNITIES
Value in Health Regional Issues (2026) · doi:10.1016/j.vhri.2026.101993 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5148 | scored |
Ethical dimensions of artificial intelligence in nursing and midwifery: A systematic review and online ethics toolkit
International Journal of Nursing Studies (2026) · doi:10.1016/j.ijnurstu.2026.105722 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5149 | scored |
AI ‐Based Wound Measurement in a Telemedicine‐Style Workflow: Agreement With In‐Person Reference Measurements Compared With Individual Physicians and Expert Consensus
International Wound Journal (2026) · doi:10.1111/iwj.71043 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5150 | scored |
A Web-Based Multi-Modal AI Framework for Intelligent Content Moderation with Constructive Feedback
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.33093/jiwe.20256.5.3.10 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |