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: 17506 →
Screen: 16486 →
Eligible: 2465 →
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 | 15624 | 2026-11-07 16:04 |
ref — reference lists of read works | 1855 | 2026-11-07 16:04 |
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 | 15624 | 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 | 8167 |
gemini-3.5-flash-lite | 7760 |
— | 625 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 178 |
gemini-3.8-flash | v2.1 | 108 |
gemini-3.7-flash | v2.1 | 100 |
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) | 16088 |
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): 14048 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 | 9079 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3793 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1176 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1349 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1061 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 31 | 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)
6.54 / 10
Analyzed papers: 651
Quality Stratification
🟢 High rigor (M ≥ 7.0): 324
🟡 Moderate rigor (4.0 ≤ M < 7.0): 308
🔴 Low rigor (M < 4.0): 19
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 252 |
other | 45 |
theoretical | 27 |
rct | 13 |
assessment_design | 171 |
natural_experiment | 5 |
quasi_experiment | 56 |
detector_evaluation | 43 |
survey | 7 |
review | 20 |
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=347, 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 (106)
#Psychometrics (62)
#Item Response Theory (50)
#academic integrity (48)
#Assessment Validity (39)
#assessment validity (39)
#item response theory (39)
#higher education (37)
#automated essay scoring (32)
#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)
#LLM Evaluation (17)
#assessment redesign (17)
#large language models (17)
#assessment_design (16)
#prompt engineering (16)
#automated scoring (15)
#AI-resilient assessment (14)
#GPT-4 (14)
#LLM as Rater (14)
#validity (14)
#Assessment Redesign (13)
#ChatGPT (13)
#assessment design (13)
#automated assessment (13)
#differential item functioning (13)
#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)
#automated_essay_scoring (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)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 6101 | Screened Out |
AI Literacy as a Predictor of Perceived English Language Development in Generative AI-Supported EFL Contexts
Arab World English Journal (2026) · doi:10.24093/awej/call12.25 · v1.0 |
3.5 (gemini-3.5-flash-lite) | — |
| 6102 | Screened Out |
Artificial Intelligence in Higher Education: Student Use Practices
THE KAZAN SOCIALLY-HUMANITARIAN BULLETIN (2026) · doi:10.26907/2079-5912.2026.2.4-12 · v1.0 |
4.1 (gemini-3.5-flash-lite) | — |
| 6103 | Screened Out |
Revitalisasi Pendidikan Islam di Sekolah dalam Menghadapi Krisis Moral Peserta Didik
Journal of Humanities Social Sciences and Education (2026) · doi:10.64690/jhuse.v2i5.888 · v1.0 |
1.3 (gemini-3.5-flash-lite) | — |
| 6104 | No Fulltext |
mplementing an AI Teaching Assistant for a Class in Motion Picture Engineering
SMPTE Motion Imaging Journal (2026) · doi:10.5594/jmi.2026/bgcm8723 · v1.0 |
6.2 (gemini-3.5-flash-lite) | — |
| 6105 | Screened Out |
Artificial Intelligence Adoption in Journalism and Mass Communication Education: Exploring Perceived Benefits, Barriers, and Future Readiness
RESEARCH HUB International Multidisciplinary Research Journal (2026) · doi:10.53573/rhimrj.2026.v13n07.008 · v1.0 |
1.4 (gemini-3.5-flash-lite) | — |
| 6106 | Screened Out |
SELF-CONCEPT AND PEER PRESSURE AS DETERMINANTS OF EXAMINATION CHEATING BEHAVIOUR AMONG STUDENTS IN PUBLIC UNIVERSITIES IN LAGOS STATE (IMPLICATIONS FOR EVALUATORS).
Lagos Education Review (2026) · doi:10.52968/15069712 · v1.0 |
4.1 (gemini-3.5-flash-lite) | — |
| 6107 | Screened Out |
Hubungan Intensitas Penggunaan Generative Artificial Intelligence dengan Tingkat Etika Akademik Mahasiswa PPKn FKIP Universitas Lambung Mangkurat
Jurnal Penelitian dan Pengabdian Masyarakat (2026) · doi:10.61231/ka9m8778 · v1.0 |
3.6 (gemini-3.5-flash-lite) | — |
| 6108 | No Fulltext |
The Gravity of Generative AI in Education: A Systematic Review of Pedagogical Offloading, Platform Control and Digital Equity
arXiv/Crossref (2026) · doi:10.17605/osf.io/h8c65 · v1.0 |
5.5 (gemini-3.5-flash-lite) | — |
| 6109 | Screened Out |
Developing learning-strategic competence in pre-service Еnglish teachers through artificial intelligence tools
The scientific and methodological journal Foreign Languages (2026) · doi:10.32589/1817-8510.2026.1.367037 · v1.0 |
3.9 (gemini-3.5-flash-lite) | — |
| 6110 | No Fulltext |
How Grades Changed Before, During and After Covid-19 in a Business College
International journal for business education (2026) · doi:10.61403/2164-2885.1175 · v1.0 |
5.9 (gemini-3.5-flash-lite) | — |
| 6111 | Screened Out |
The reliability of AI-generated responses to prompts on the development of foreign language communicative speech competences: trust, but verify critically
The scientific and methodological journal Foreign Languages (2026) · doi:10.32589/1817-8510.2026.1.367038 · v1.0 |
3.2 (gemini-3.5-flash-lite) | — |
| 6112 | Screened Out |
Academic Integrity in the Era of Generative AI: Students’ Perceptions, Ethical Boundaries, and Risk-Taking Behavior in Nigerian Universities
Systems and Computing (2026) · doi:10.64409/sycom.v2.i2.44 · v1.0 |
3.9 (gemini-3.5-flash-lite) | — |
| 6113 | Screened Out |
Generative Artificial Intelligence Research: A case study on the implications of Generative Artificial Intelligence (GAI) on teaching within an Irish Technological University.
Sword (2026) · doi:10.34719/bsj0-bw31 · v1.0 |
3.3 (gemini-3.5-flash-lite) | — |
| 6114 | Screened Out |
The Integration of Emerging Technologies and Digital Platforms in Sanskrit Language Pedagogy: A Systematic Literature Review
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21381696 · v1.0 |
2.9 (gemini-3.5-flash-lite) | — |
| 6115 | scored |
Exploring the Effects of ChatGPT in Academic Writing and Learners’ Perceptions among Marine Technology Students
SpringerBriefs in applied sciences and technology (2026) · doi:10.1007/978-3-032-26565-4_1 · v1.0 |
0.0 (Flash Lite) | — |
| 6116 | Screened Out |
Artificial Intelligence in Higher Education Opportunities, Challenges, and Ethical
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21379138 · v1.0 |
1.0 (gemini-3.5-flash-lite) | — |
| 6117 | Screened Out |
Navigating Ethical Complexities in Educational AI: A Systematic Review of Generative Chatbot Integration in Teaching and Learning
F1000Research (2026) · doi:10.12688/f1000research.180291.1 · v1.0 |
3.2 (gemini-3.5-flash-lite) | — |
| 6118 | Screened Out |
Digitizing Examination Misconduct Records for Enhanced Accountability and Future Reference: A Case Study of Auchi Polytechnic
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21380519 · v1.0 |
1.5 (gemini-3.5-flash-lite) | — |
| 6119 | scored |
Human Intelligence Labs in the Age of Generative AI: Rethinking Learning and Integrity
arXiv/Crossref (2026) · doi:10.66246/1bvhrj35 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6120 | Screened Out |
Automated Essay-Answer Grading using Transformer and GenAI-Based Error Analysis on Answer Sentence
Scientific Journal of Informatics (2026) · doi:10.15294/sji.v13i1.39859 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 6121 | Screened Out |
The JUDGE Model of AI-Mediated Teacher Agency: A Theoretical Framework for English Language Teaching
arXiv/Crossref (2026) · doi:10.35542/osf.io/prtjm_v2 · v1.0 |
2.3 (gemini-3.5-flash-lite) | — |
| 6122 | Screened Out |
Design and Feasibility Study of Dual-Graph Framework for Personalized Learning Based on Artificial Intelligence
International Conference on Educational and Information Technology (2026) · doi:10.1109/iceit68991.2026.11521359 · v1.0 |
3.4 (gemini-3.5-flash-lite) | — |
| 6123 | No Fulltext |
Enhancing IELTS writing automated scoring with M-LoRA fine-tuned LLAMA-3 and human feedback-driven PPO reinforcement learning
Scientific Reports (2026) · doi:10.1038/s41598-026-43318-w · v1.0 |
5.5 (gemini-3.5-flash-lite) | — |
| 6124 | scored |
Interfacing with the Future: Wearable AI and Academic Integrity for K-12 and Higher Ed
arXiv/Crossref (2026) · doi:10.11575/prism/51213 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6125 | scored |
The Kshetra-Astra Corpus: Empirical Validation and Extension of a Predictive Disruption Framework Across 83 Civilisational Cases
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.19281839 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6126 | scored |
Editorial
Dibon Journal of Education (2026) · doi:10.64169/dje.212 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6127 | scored |
Contrast sensitivity in multimodal large language models: A psychophysics-inspired evaluation
Neural Networks (2026) · doi:10.1016/j.neunet.2026.108903 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6128 | Screened Out |
AI-Assisted Assessment and Instruction in Higher Education: Foundations, Applications, and Implications for Exam Design
arXiv/Crossref (2026) · doi:10.31234/osf.io/uwpfq_v1 · v1.0 |
3.9 (gemini-3.1-flash-lite) | — |
| 6129 | scored |
Development of a Computerized Adaptive Item Bank for the Big Five Personality Based on Large Language Models
Assessment (2026) · doi:10.1177/10731911261427877 · v1.0 |
5.5 (gemini-3.1-flash-lite) | — |
| 6130 | Screened Out |
How to measure AI literacy (Fast): Development and norming of the AI competence objective scale short version (AICOS-S)
Computers in Human Behavior Artificial Humans (2026) · doi:10.1016/j.chbah.2026.100298 · v1.0 |
3.5 (gemini-3.1-flash-lite) | — |
| 6131 | Screened Out |
Drivers and Barriers of ChatGPT Adoption in Higher Education: Insights from Multiple Theories
Pakistan Journal of Commerce and Social Sciences (2026) · doi:10.64534/commer.2026.672 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 6132 | Not Article |
Decision letter for "Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agents"
arXiv/Crossref (2026) · doi:10.1098/rsos.251802/v1/decision1 · v1.0 |
- | — |
| 6133 | scored |
АБИТУРИЕНТТЕРДІҢ КӘСІБИ ТАҢДАУЫН БОЛЖАУҒА АРНАЛҒАН ИНТЕРПРЕТАЦИЯЛАНАТЫН МАШИНАЛЫҚ ОҚЫТУ ЖӘНЕ ТҮСІНДІРМЕЛІ ЖАСАНДЫ ИНТЕЛЛЕКТ МОДЕЛЬДЕРІН ЗЕРТТЕУ
Yessenov Science Journal (2026) · doi:10.56525/kmqs0051 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6134 | No Fulltext |
Методика внедрения искусственного интеллекта в практико-ориентированные дисциплины для моделирования профессиональных кейсов и развития компетенций принятия решений
Management of Education (2026) · doi:10.25726/r8400-3630-6308-r · v1.0 |
5.6 (gemini-3.1-flash-lite) | — |
| 6135 | scored |
From Measurement to Understanding: An Architecture of Emulated Expertise for LLM-Diagnostics in Education
Психология Журнал Высшей школы экономики (2026) · doi:10.17323/1813-8918-2026-2-263-274 · v1.0 |
5.2 (gemini-3.1-flash-lite) | — |
| 6136 | Screened Out |
DEVELOPMENT AND PSYCHOMETRIC VALIDATION OF THE SCREENING SCALE FOR ARTIFICIAL INTELLIGENCE ADDICTION DIAGNOSIS (AIAS-10+1 V4.0)
PSYCHOLOGICAL JOURNAL (2026) · doi:10.31108/1.2026.12.3.1 · v1.0 |
3.1 (gemini-3.1-flash-lite) | — |
| 6137 | Screened Out |
Innovating Reading Materials Development Based on Generative AI Within the Backward Design Framework
Journal of Language (2026) · doi:10.64699/26gxpj1206 · v1.0 |
4.9 (gemini-3.1-flash-lite) | — |
| 6138 | Screened Out |
Review of Design Principles for AI Moral Ontology Based on Universal Moral Grammar Theory and Implications for Moral Education
The SNU Journal of Education Research (2026) · doi:10.54346/sjer.2026.35.1.175 · v1.0 |
3.9 (gemini-3.1-flash-lite) | — |
| 6139 | scored |
Artificial Intelligence-Driven Recruitment as Business Capability: Evidence on Hiring Accuracy from Firms Operating in Dar es Salaam
Journal of Economics Management and Trade (2026) · doi:10.9734/jemt/2026/v32i41407 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6140 | Screened Out |
The Analysis Needs Mapping for the Development of an Artificial Intelligence-Item Response Theory (AI-IRT)-Based CAT Web System for Adaptive Science Assessment
Journal of Mathematics Instruction Social Research and Opinion (2026) · doi:10.58421/misro.v5i1.1256 · v1.0 |
3.8 (gemini-3.1-flash-lite) | — |
| 6141 | scored |
Geospatial and hydrological assessment of water resources under climate change: A comprehensive review
International Journal of Agriculture and Food Science (2026) · doi:10.33545/2664844x.2026.v8.i10c.2020 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6142 | scored |
Comparison of In-Person, Virtual, and Artificial Intelligence-Augmented Assessments of Psoriasis Disease Severity
Journal of Investigative Dermatology (2026) · doi:10.1016/j.jid.2026.09.855 · v1.0 |
0.0 (Flash Lite) | — |
| 6143 | scored |
Obesity in CKD: The Double-Edged Sword
Journal of Nephrology (2026) · doi:10.1093/joneph/aajag256 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6144 | Screened Out |
The Application of AI in Teaching Chinese as a Foreign Language: Improving Classroom Interaction and Student Engagement
Asia-Pacific Journal of Futures in Education and Society (2026) · doi:10.58946/apjfes-5.3.p6 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 6145 | Screened Out |
Determinants and Impact of AI-Based Chatbot Adoption in Higher Education: A TAM–ISSM Perspective
SAGE Open (2026) · doi:10.1177/21582440261495170 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 6146 | scored |
APPLICATION OF DATA ANALYTICS IN FINANCIAL RISK MANAGEMENT AND DECISION-MAKING
arXiv/Crossref (2026) · doi:10.48047/ijss.5.9.2026.2 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6147 | scored |
123 Large language models in acute neuro-oncology clinics to rapidly produce clinical documentation: are they accurate and do they improve efficiency?
Neuro-Oncology (2026) · doi:10.1093/neuonc/noag233.023 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6148 | Screened Out |
Cultivating a Strong Field: Reengineering Education R&D for Speed, Scale, and Impact
arXiv/Crossref (2026) · doi:10.51388/20.500.12265/321 · v1.0 |
2.4 (gemini-3.1-flash-lite) | — |
| 6149 | scored |
Competencias digitales en estudiantes de educación básica: revisión sistemática de la evidencia científica 2023-2026
Revista Tribunal (2026) · doi:10.59659/revistatribunal.v6i17.541 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6150 | Screened Out |
SonicLens: Visual Metaphors and Explainable AI for Formative Feedback in Sound Design Education
Asia-Pacific Journal of Futures in Education and Society (2026) · doi:10.58946/apjfes-5.3.p7 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |