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: 16898 →
Screen: 15889 →
Eligible: 2367 →
Included: 81
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 = 81 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 | 15111 | 2026-11-06 14:23 |
ref — reference lists of read works | 1760 | 2026-11-06 10:24 |
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 | 15111 | 2026-10-06 | 2026-11-06 |
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 | 7873 |
gemini-3.5-flash-lite | 7472 |
— | 610 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 162 |
gemini-3.8-flash | v2.1 | 101 |
gemini-3.7-flash | v2.1 | 95 |
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) | 15491 |
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): 13549 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 | 8764 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3658 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1127 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1285 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1028 | 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)
6.59 / 10
Analyzed papers: 623
Quality Stratification
🟢 High rigor (M ≥ 7.0): 320
🟡 Moderate rigor (4.0 ≤ M < 7.0): 286
🔴 Low rigor (M < 4.0): 17
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 239 |
other | 42 |
theoretical | 27 |
rct | 13 |
assessment_design | 165 |
natural_experiment | 5 |
quasi_experiment | 54 |
detector_evaluation | 41 |
survey | 7 |
review | 18 |
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=333, 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 (101)
#Psychometrics (57)
#Item Response Theory (47)
#academic integrity (46)
#item response theory (38)
#assessment validity (37)
#Assessment Validity (36)
#higher education (36)
#Automated Essay Scoring (28)
#Generative AI (28)
#automated essay scoring (27)
#construct validity (26)
#IRT (24)
#Construct Validity (23)
#generative AI (20)
#Academic Integrity (19)
#LLM as rater (19)
#Automated Scoring (17)
#Higher Education (17)
#Large Language Models (17)
#assessment redesign (16)
#assessment_design (16)
#LLM Evaluation (15)
#AI-resilient assessment (14)
#automated scoring (14)
#large language models (14)
#LLM as Rater (13)
#assessment design (13)
#automated assessment (13)
#prompt engineering (13)
#validity (13)
#GPT-4 (12)
#academic_integrity (12)
#differential item functioning (12)
#Assessment Redesign (11)
#ChatGPT (11)
#Prompt Engineering (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#LLM-as-a-judge (10)
#LLMs as raters (10)
#item_response_theory (10)
#Automated Item Generation (9)
#Computerized Adaptive Testing (9)
#DIF (9)
#Inter-Rater Reliability (9)
#LLM evaluation (9)
#LLMs as Test Takers (9)
#concept drift (9)
#inter-rater reliability (9)
#rater drift (9)
#AI-resistant assessment (8)
#Assessment Design (8)
#Differential Item Functioning (8)
#Knowledge Tracing (8)
#Validity (8)
#automated_essay_scoring (8)
#computerized adaptive testing (8)
#construct_validity (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 1651 | scored |
Feasibility of an AI-Simulated Patient Encounter Delivered in Group and Individual Learning Formats for First-Year Medical Students: A Two-Cohort Pilot Study
Research Square (2026) · doi:10.21203/rs.3.rs-10627241/v1 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 1652 | scored |
Artificial Intelligence and the Transformation of Collaborative Study Culture: Evidence from Graduate-Level Students in Pakistan
arXiv/Crossref (2026) · doi:10.66857/3101 · v1.0 |
0.0 (Flash Lite) | — |
| 1653 | scored |
Before the Decision: Power, Time, and the Making of Health Politics
Health Politics (2026) · doi:10.66534/hp.2026.0012 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 1654 | scored |
Investigating Generative AI for Personalized College Assignments Based on Student Interests
Research Square (2026) · doi:10.21203/rs.3.rs-10656941/v1 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 1655 | scored |
Student Perspectives on Artificial Intelligence in Sustainable Higher Agricultural Education: A Multidimensional Assessment
Sustainability (2026) · doi:10.3390/su18199979 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 1656 | scored |
How can AI Empower the Prosocial Behaviors of Children with Special Needs in Inclusive Education?
Journal of Education and Educational Research (2026) · doi:10.54097/ap9yff14 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 1657 | scored |
Machine learning for sustainable digital literacy ecosystems in Thailand’s society 5.0 era
Open Science Framework (2026) · doi:10.17605/osf.io/xz8qe · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 1658 | scored |
人工智能辅助PACS结合CBL在神经病学见习教学中的应用研究
现代教育与教学创新 (2026) · doi:10.68070/rd6vse15 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 1659 | Not Article |
Cognitive Trapeze — AI-Generated Comic: Tendani Ito (The Golden Canary) for Guided Reading in ZEP
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23065164 · v1.0 |
- | — |
| 1660 | scored |
A DIGITAL RE-EXPLORATION OF THEATRE TRANSLATION HISTORY IN TÜRKİYE (1923-1973): A BIBLIOMETRIC ANALYSIS
Uluslararası Dil Edebiyat ve Kültür Araştırmaları Dergisi (2026) · doi:10.37999/udekad.1989635 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 1661 | scored |
Bias in Standardized Testing? An Item Response Theory Analysis of Cultural and Religious Influences of a Multi-Denominational Educational Systems in Ghana and Botswana
Research Square (2025) · doi:10.21203/rs.3.rs-6180414/v1 · v1.0 |
5.2 (gemini-3.1-flash-lite) | — |
| 1662 | Not Article |
Correction to: Cultural sensitivity of early childhood assessments based on learning progressions: a Rasch person fit analysis
Educational Assessment Evaluation and Accountability (2025) · doi:10.1007/s11092-025-09462-z · v1.0 |
- | — |
| 1663 | Screened Out |
Construct validity of the Short Warwick-Edinburgh Mental Well-Being Scale in a diverse urban population
Social Psychiatry and Psychiatric Epidemiology (2026) · doi:10.1007/s00127-026-03109-0 · v1.0 |
3.1 (gemini-3.1-flash-lite) | — |
| 1664 | Screened Out |
Faculty and Student Perceptions of Generative AI Use, ChatGPT and Academic Integrity: Connecting Findings to Assessment Redesign
Intersection A Journal at the Intersection of Assessment and Learning (2026) · doi:10.61669/001c.162793 · v1.0 |
3.6 (gemini-3.1-flash-lite) | — |
| 1665 | scored |
CiteChain: A Blockchain-Based Scientific Citation and Reputation Network
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20509178 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 1666 | scored |
Haar Cascade Classifier-Based System for Student Attendance Through Face Recognition
International Journal of Latest Technology in Engineering Management & Applied Science (2026) · doi:10.51583/ijltemas.2026.150500072 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 1667 | Screened Out |
Students’ Perceptions of the Use of Artificial Intelligence Tools in Educational Activities
Systems (2026) · doi:10.3390/systems14060633 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 1668 | Screened Out |
Teaching microbiology with generative AI: a survey-style playbook to empower microbiology faculty to integrate AI into courses
Journal of Microbiology and Biology Education (2026) · doi:10.1128/jmbe.00040-26 · v1.0 |
1.6 (gemini-3.1-flash-lite) | — |
| 1669 | Screened Out |
The GenAI gender gap in higher education: why female caution is not the problem
Teaching in Higher Education (2026) · doi:10.1080/13562517.2026.2665702 · v1.0 |
2.0 (gemini-3.1-flash-lite) | — |
| 1670 | Screened Out |
The psychological mechanisms and behavioral determinants of academic integrity in the age of artificial intelligence
Frontiers in Psychology (2026) · doi:10.3389/fpsyg.2026.1853790 · v1.0 |
3.8 (gemini-3.1-flash-lite) | — |
| 1671 | Screened Out |
Educating What AI Cannot: Character and the Future of Higher Education
British Journal of Educational Studies (2026) · doi:10.1080/00071005.2026.2678972 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 1672 | Screened Out |
Toward ethical governance of generative artificial intelligence in higher education: a Delphi study of key dimensions and priorities
Cogent Education (2026) · doi:10.1080/2331186x.2026.2677267 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 1673 | Screened Out |
Prospects for the development of medical education and the professional development of scientific and pedagogical staff
Acta Medica Leopoliensia (2026) · doi:10.25040/aml2026.1-2.080 · v1.0 |
1.3 (gemini-3.1-flash-lite) | — |
| 1674 | Screened Out |
A Systematic Literature Review on The Use of Artificial Intelligence Tools In Enhancing University Students’ Academic Writing Skills
Journal of English Teaching Applied Linguistics and Literature Studies (2026) · doi:10.67548/jentalis.v1i1.244 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 1675 | Screened Out |
IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON STUDENTS’ CRITICAL THINKING AND ACHIEVEMENT IN SOCIAL AND CIVIC EDUCATION IN THE UNIVERSITY OF JOS
World Journal of Arts Education and Literature (2026) · doi:10.65336/wjael.2026.3604 · v1.0 |
2.4 (gemini-3.1-flash-lite) | — |
| 1676 | Screened Out |
Beyond Lecturing: A Qualitative Exploration of the Impact of Artificial Intelligence (AI) and Academic Integrity on Faculty Job Satisfaction in Christian Higher Education
Journal for the Scholarship of Teaching & Learning (2026) · doi:10.70623/ytui8353 · v1.0 |
1.1 (gemini-3.1-flash-lite) | — |
| 1677 | Screened Out |
INTELIGÊNCIA ARTIFICIAL GENERATIVA NO ENSINO SUPERIOR: POTENCIALIDADES E LIMITES PEDAGÓGICOS
INTERNATIONAL INTEGRALIZE SCIENTIFIC (2026) · doi:10.63391/dcmvqc48 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 1678 | scored |
STRATEGI GURU PENDIDIKAN AGAMA ISLAM DALAM MENGATASI AKHLAK MAZMUMAH DI KALANGAN PELAJAR SMA: KAJIAN LITERATUR
Pendas Jurnal Ilmiah Pendidikan Dasar (2026) · doi:10.23969/jp.v11i02.49489 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 1679 | Not Article |
Data from: MST in the wild: Optimizing the mnemonic similarity task for use in diverse environments
DRYAD (2026) · doi:10.5061/dryad.gqnk98t1j · v1.0 |
- | — |
| 1680 | Screened Out |
AI IN ENGLISH LANGUAGE LEARNING AND TEACHING IN PAKISTAN: A WORLD ENGLISHES PERSPECTIVE ON TRENDS, OPPORTUNITIES, CHALLENGES, AND ETHICAL IMPLICATIONS
Journal of Applied Linguistics and TESOL (JALT) (2026) · doi:10.63878/jalt2295 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 1681 | scored |
CRITICAL THINKING AS AN ASPECT OF THE PROFESSIONAL CULTURE OF FUTURE SPECIALISTS
Ukrainian professional education (2026) · doi:10.33989/2519-8254.2026.19.363699 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 1682 | Not Article |
Copyright Law And Predatory Publishing An International Legal Analysis
Figshare (2026) · doi:10.6084/m9.figshare.32569119 · v1.0 |
- | — |
| 1683 | In Buffer |
BLOCKCHAIN AND AI-ENABLED EXAMINATION INTEGRITY VERIFICATION: A DECENTRALIZED FORENSIC READINESS FRAMEWORK USING DEEP LEARNING
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20530982 · v1.0 |
3.0 (gemini-3.6-flash) | — |
| 1684 | scored |
Cracking the Code: Smart Techniques for Effective Interviews
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2025) · doi:10.55041/ijsrem55125 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 1685 | scored |
Artificial Intelligence for Suturing and Knot-tying Skills Assessment: A Systematic Review
Journal of surgical education (2025) · doi:10.1016/j.jsurg.2025.103817 · v1.0 |
0.0 (Flash Lite) | — |
| 1686 | scored |
Evaluating the Efficacy of AI-Based Interactive Assessments Using Large Language Models for Depression Screening: Development and Usability Study
JMIR Formative Research (2025) · doi:10.2196/78401 · v1.0 |
5.0 (gemini-3.1-flash-lite) | — |
| 1687 | scored |
AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology
Underline Science Inc. (2025) · doi:10.48448/f7v0-bk19 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 1688 | scored |
DEEP LEARNING FOR PERFORMANCE ASSESSMENT IN DANCE AND MUSIC
ShodhKosh Journal of Visual and Performing Arts (2025) · doi:10.29121/shodhkosh.v6.i2s.2025.6751 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 1689 | Screened Out |
Artificial intelligence and academic writing questionnaire (AI-AWQ): development and validation among medical students’ experiences using exploratory factor analysis
BMC Medical Education (2025) · doi:10.1186/s12909-025-08288-z · v1.0 |
1.7 (gemini-3.1-flash-lite) | — |
| 1690 | Screened Out |
Adoption intention of generative artificial intelligence among Chinese college students: an extended TAM-UTAUT2 model from the four-helix perspective
Frontiers in Education (2025) · doi:10.3389/feduc.2025.1686408 · v1.0 |
1.7 (gemini-3.1-flash-lite) | — |
| 1691 | scored |
Longitudinal Evaluation of Student Writing Improvement Using Human-in-the-Loop Open-SLLMs for Automated Scoring and Revision
International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (2025) · doi:10.1109/icaaic64647.2025.11330583 · v1.0 |
5.3 (gemini-3.1-flash-lite) | — |
| 1692 | Screened Out |
Pioneering Smart Leadership: Integrating AI Analytics to Transform STEM Education Management for the Future
Al-Qanṭara (2025) · doi:10.5281/zenodo.17958429 · v1.0 |
1.7 (gemini-3.1-flash-lite) | — |
| 1693 | Not Article |
EvaluadorTRI
Zenodo (CERN European Organization for Nuclear Research) (2025) · doi:10.5281/zenodo.17969697 · v1.0 |
- | — |
| 1694 | scored |
Mapping the landscape of predictive biomarkers for immune checkpoint inhibitors a bibliometric analysis
Discover Oncology (2025) · doi:10.1007/s12672-025-04137-z · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 1695 | scored |
Editorial: Current research and future development of neuropsychology
Frontiers in Neuroscience (2025) · doi:10.3389/fnins.2025.1754906 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 1696 | Screened Out |
Mathematics teachers’ perspectives on utilizing AI in their teaching practices: Examining regional and teaching experience factors
Journal of Education and e-Learning Research (2025) · doi:10.20448/jeelr.v12i4.7884 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 1697 | scored |
Trust in artificial intelligence-based follow-up in hospital information systems: development and validation of a new scale
BMC Psychology (2025) · doi:10.1186/s40359-025-03855-x · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 1698 | Not Article |
Review for "Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agents"
Publons reviews and discussion (2025) · doi:10.1098/rsos.251802/v1/review1 · v1.0 |
- | — |
| 1699 | scored |
Can LLMs Construct Valid Syllabi? Vietnamese High School Math Case Study
RIVF International Conference on Computing and Communication Technologies (RIVF) (2025) · doi:10.1109/rivf68649.2025.11365100 · v1.0 |
5.0 (gemini-3.1-flash-lite) | — |
| 1700 | Screened Out |
Exploring the Relationship between Automated Essay Scoring System Functionality, Student Innovativeness, Reflective Thinking, and Exploratory Usage: A PLS-SEM Approach
International Conference on Automation, Robotics and Computer Engineering (ICARCE) (2025) · doi:10.1109/icarce67182.2025.11361713 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |