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: 16337 →
Screen: 15417 →
Eligible: 2301 →
Included: 75
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 = 75 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 | 14577 | 2026-11-05 04:05 |
ref — reference lists of read works | 1733 | 2026-11-04 10:23 |
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 | 14577 | 2026-10-06 | 2026-11-05 |
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 | 7648 |
gemini-3.5-flash-lite | 7240 |
— | 595 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 150 |
gemini-3.8-flash | v2.1 | 101 |
gemini-3.7-flash | v2.1 | 94 |
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) | 15019 |
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): 13143 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 | 8497 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3550 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1096 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1251 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 996 | 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)
6.60 / 10
Analyzed papers: 610
Quality Stratification
🟢 High rigor (M ≥ 7.0): 316
🟡 Moderate rigor (4.0 ≤ M < 7.0): 277
🔴 Low rigor (M < 4.0): 17
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 236 |
other | 42 |
theoretical | 27 |
rct | 13 |
assessment_design | 157 |
natural_experiment | 4 |
quasi_experiment | 54 |
detector_evaluation | 41 |
survey | 7 |
review | 17 |
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=320, 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 (99)
#Psychometrics (56)
#Item Response Theory (46)
#academic integrity (46)
#item response theory (37)
#Assessment Validity (34)
#assessment validity (34)
#higher education (34)
#Automated Essay Scoring (27)
#Generative AI (27)
#automated essay scoring (25)
#construct validity (25)
#IRT (24)
#Construct Validity (23)
#Academic Integrity (19)
#LLM as rater (19)
#generative AI (19)
#Automated Scoring (17)
#Large Language Models (17)
#Higher Education (16)
#assessment redesign (16)
#assessment_design (16)
#LLM Evaluation (15)
#large language models (14)
#AI-resilient assessment (13)
#LLM as Rater (13)
#assessment design (13)
#automated assessment (13)
#automated scoring (13)
#validity (13)
#academic_integrity (12)
#Assessment Redesign (11)
#ChatGPT (11)
#Prompt Engineering (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#differential item functioning (11)
#prompt engineering (11)
#GPT-4 (10)
#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)
#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)
#higher_education (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 901 | scored |
Online Plagiarism Checking Using Data Mining Techniques
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2026) · doi:10.55041/ijsrem65077 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 902 | scored |
AI in Healthcare: Promise, Pitfalls, and Future Directions
TTU Journal of Science (2026) · doi:10.53901/tjs.2026.v01.issue02.art02 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 903 | scored |
LEGAL FOUNDATIONS AND PROSPECTS FOR THE USE OF ARTIFICIAL INTELLIGENCE IN TRAINING OF THE PERSONNEL OF THE SCES OF UKRAINE
Scientific Herald of Sivershchyna Series Law (2026) · doi:10.32755/sjlaw.2026.02.255 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 904 | scored |
ID:92 FC-NP3-3 Shortening the Learning Curve: Improving ECMO Training Program of a Nurse-Led ECMO Centre
ASAIO Journal (2026) · doi:10.1097/01.mat.0001204764.16302.b1 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 905 | Screened Out |
Fast and accurate cheat detection using deep learning algorithms
Bulletin of Electrical Engineering and Informatics (2026) · doi:10.11591/eei.v15i3.11207 · v1.0 |
5.0 (gemini-3.1-flash-lite) | — |
| 906 | scored |
MITIGATING AI OVERUSE IN GRAMMAR COURSE ASSIGNMENTS THROUGH PROCESS-BASED TASKS: AN ACTION RESEARCH STUDY OF ENGLISH-MAJORED STUDENTS AT THE ACADEMY OF PUBLIC ADMINISTRATION AND GOVERNANCE
Tạp chí Khoa học Trường Đại học Mở Hà Nội (2026) · doi:10.59266/houjs.2026.1190 · v1.0 |
5.6 (gemini-3.1-flash-lite) | — |
| 907 | Screened Out |
"EXAM SHILED” USE FOR PROCTORED EXAM INVIGILATION…
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2026) · doi:10.55041/ijsrem64679 · v1.0 |
3.5 (gemini-3.1-flash-lite) | — |
| 908 | Screened Out |
Exploring human-ai interaction in academic writing: insights from translation students
Traducerea în era inteligenţei artificiale: provocări şi oportunităţi (2026) · doi:10.59295/teia2025.02 · v1.0 |
2.4 (gemini-3.1-flash-lite) | — |
| 909 | scored |
Ethics and deontology of scientific research in translation studies in the context of ict
Traducerea în era inteligenţei artificiale: provocări şi oportunităţi (2026) · doi:10.59295/teia2025.01 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 910 | Screened Out |
AI tools in teaching: EFL teachers’ perceptions of usefulness, challenges, and the role of designation and AI-training
IAES International Journal of Artificial Intelligence (2026) · doi:10.11591/ijai.v15.i3.pp2092-2102 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 911 | Screened Out |
Human-in-the-Loop Artificial Intelligence in University Teaching in Vietnam: Implications for Teacher Professionalism, Quality Assurance, and Pedagogical Practice
European Modern Studies Journal (2026) · doi:10.59573/emsj.10(2).2026.35 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 912 | Screened Out |
AI Benefits in Education and Peer Recommendation: The Role of Student Demographics
Association of Marketing Theory and Practice Conference (AMTP) (2026) · doi:10.20429/amtp.2026.16 · v1.0 |
2.4 (gemini-3.1-flash-lite) | — |
| 913 | scored |
THE INFLUENCE OF ARTIFICIAL INTELLIGENCE ON ACADEMIC INTEGRITY AMONG FINAL YEAR STUDENTS OF LIBRARY AND INFORMATION SCIENCE OF BENUE STATE UNIVERSITY, MAKURDI
Nigerian Academic and Research Libraries Journal (2026) · doi:10.61955/uqzckq · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 914 | scored |
SOFTWARE ARCHITECTURE AND IMPLEMENTATION OF A DATA-DRIVEN ADAPTIVE AІ SYSTEM FOR 2D TACTICAL GAMES
arXiv/Crossref (2026) · doi:10.70286/eoss-01.06.2026.004.217-231 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 915 | scored |
Enhancing proactivity or encouraging cheating? The contrasting effects of leader negative feedback on employees' workplace behaviour
Journal of Occupational and Organizational Psychology (2026) · doi:10.1111/joop.70126 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 916 | scored |
Digital Competence and Language Teaching Activities of Junior High School English Teachers in Congressional Teacher II, SDO Batangas Province
Aloysian Interdisciplinary Journal of Social Sciences, Education, and Allied Fields (2026) · doi:10.5281/zenodo.20491087 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 917 | Screened Out |
Is Generative Artificial Intelligence Sculpting or Stunting University Undergraduate Students’ Intellect? Perceptions of Lecturers in a Zimbabwean University
Journal of Education and Training Studies (2026) · doi:10.11114/jets.v14i3.8892 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 918 | Screened Out |
Towards a New Learning Ecology: The Mediator Role of Teachers Facing Generative AI in the University Context of Peru, Chile, and Colombia
F1000Research (2026) · doi:10.12688/f1000research.182491.1 · v1.0 |
3.1 (gemini-3.1-flash-lite) | — |
| 919 | Screened Out |
Artificial Intelligence in Education: Emerging Trends, Challenges, and Future Directions
Asian Journal of Education and Social Studies (2026) · doi:10.9734/ajess/2026/v52i63112 · v1.0 |
1.3 (gemini-3.1-flash-lite) | — |
| 920 | scored |
The AI Educator
CRC Press eBooks (2026) · doi:10.1201/9781003624981 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 921 | Screened Out |
Impact of Artificial Intelligence on Assessment, Engagement and Motivation among Secondary School Students in Kaduna State, Nigeria
International Journal of Research and Innovation in Social Science (2025) · doi:10.47772/ijriss.2025.903sedu0710 · v1.0 |
1.3 (gemini-3.1-flash-lite) | — |
| 922 | Screened Out |
Exploring the Link Between the Emotional Recall Task and Mental Health in Humans and LLMs
Information (2025) · doi:10.3390/info16121057 · v1.0 |
3.9 (gemini-3.1-flash-lite) | — |
| 923 | Screened Out |
Chatbot Rating Scale (CHARS): Development and Validation of a New Tool for Assessing the Quality of Health and Social Care Chatbots
Research Square (2025) · doi:10.21203/rs.3.rs-8228980/v1 · v1.0 |
3.3 (gemini-3.1-flash-lite) | — |
| 924 | scored |
Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
Journal of the American Statistical Association (2025) · doi:10.1080/01621459.2025.2587922 · v1.0 |
5.5 (gemini-3.1-flash-lite) | — |
| 925 | Screened Out |
Generative AI in Mechanical Engineering Education: Enablers, Challenges, and Implementation Pathways
Sustainability (2025) · doi:10.3390/su172310817 · v1.0 |
4.1 (gemini-3.5-flash-lite) | — |
| 926 | Screened Out |
GAAIS-J: Translation and Validation of the Japanese Version of the General Attitudes Toward Artificial Intelligence Scale
Behavioral Sciences (2025) · doi:10.3390/bs15121668 · v1.0 |
2.3 (gemini-3.1-flash-lite) | — |
| 927 | scored |
Customer Responses to Emerging Technologies in Online Retail : Evidence from Generative AI Chatbot and Augmented Reality
arXiv/Crossref (2025) · doi:10.70675/82c82ea7zf612z41bfzb733z229436cc1276 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 928 | scored |
Architects of Attention: Mapping the Hidden Infrastructures of Machine-Mediated Learning
arXiv/Crossref (2025) · doi:10.22541/au.176487090.09185174/v1 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 929 | scored |
Cultural Adaptation and Validation of the Medical Artificial Intelligence Readiness Scale for Medical Students Questionnaire in Indian Undergraduate Medical Education: A Mixed-methods Study
National Journal of Clinical Anatomy (2025) · doi:10.4103/njca.njca_75_25 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 930 | scored |
Building Trust in and through Big Data Technologies? A Psychometric Exploration of Artificial Intelligence Adoption at a Research University
Michigan State University Libraries (2025) · doi:10.25335/gphj-c431 · v1.0 |
0.0 (Flash Lite) | — |
| 931 | In Buffer |
Campbell's Law and Goodhart's Law in LLM Evaluation: A Theory of Benchmark Decay
Zenodo (CERN European Organization for Nuclear Research) (2025) · doi:10.5281/zenodo.17826619 · v1.0 |
4.9 (gemini-3.6-flash) | — |
| 932 | scored |
LARGE LANGUAGE MODELS IN EDUCATIONAL MEASUREMENT OF KAZAKH LANGUAGE PROFICIENCY
«Педагогикалық өлшемдер» ғылыми–практикалық журналы (2025) · doi:10.63597/uto3105-4161.2025.3.3.008 · v1.0 |
6.3 (gemini-3.5-flash-lite) | — |
| 933 | scored |
Infrared Thermography for Social Overhead Capital (SOC) Component Inspection: A Comprehensive Review of Principles, Advances, and Trends
IEEE Sensors Journal (2025) · doi:10.1109/jsen.2025.3625540 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 934 | scored |
Visual Translator: Bridging Students’ Handwritten Solutions and Automatic Diagnosis of Students’ Use of Number Lines to Represent Fractions
Education Sciences (2025) · doi:10.3390/educsci15121638 · v1.0 |
5.8 (gemini-3.5-flash-lite) | — |
| 935 | scored |
CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer
Zenodo (CERN European Organization for Nuclear Research) (2025) · doi:10.5281/zenodo.17832626 · v1.0 |
5.8 (gemini-3.5-flash-lite) | — |
| 936 | scored |
Infrared thermography for the diagnosis and monitoring of Raynaud’s phenomenon: current evidence and future directions
Reumatologia/Rheumatology (2025) · doi:10.5114/reum/211627 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 937 | Not Article |
Mystical and Affective Features of Naturalistic Psychedelic Experiences: Dataset and Supplementary Materials
Zenodo (CERN European Organization for Nuclear Research) (2025) · doi:10.5281/zenodo.21909882 · v1.0 |
- | — |
| 938 | scored |
Transforming Teaching Practice Evaluation Through a Cloud-Based Learning System Within the Pulami Integrated Management System (PIMS): Towards Smart and Evidence-Based Assessment in Teacher Education
International Conference on Artificial Intelligence and Teacher Education (ICAITE) (2025) · doi:10.1109/icaite68636.2025.11442424 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 939 | scored |
Verification, Trust, and Traceability: Epistemic Challenges in LLM-Augmented Software Engineering
Zenodo (CERN European Organization for Nuclear Research) (2025) · doi:10.5281/zenodo.17851057 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 940 | scored |
Translation as Infrastructure for Global Psychological Science
arXiv/Crossref (2025) · doi:10.31234/osf.io/bd7gk_v1 · v1.0 |
5.0 (gemini-3.5-flash-lite) | — |
| 941 | scored |
Framework Artificial Intelligence Ecosystem STIT Bustanul Ulum
Jurnal Riset Sosial Humaniora dan Pendidikan (2026) · doi:10.62383/risoma.v4i5.1917 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 942 | scored |
Effectiveness of Using AI Chatbots as Interactive Tutors to Improve Personalized Learning and Learning Outcomes of Eighth-Grade Informatics Students
Jurnal Penelitian Pendidikan IPA (2026) · doi:10.29303/jppipa.v12i9.16076 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 943 | scored |
School Management in Adapting the Merdeka Curriculum for the Software Engineering (RPL) Concentration in the Era of Artificial Intelligence
Jurnal Perspektif Ilmu Pendidikan dan Keguruan (2026) · doi:10.66618/jpipk.v1i3.1561 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 944 | scored |
Higher education models and capacity building for collaboration with human-centred AI in global higher education networks
Ubiquity Proceedings (2026) · doi:10.5334/uproc.263 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 945 | scored |
From Artificial Creativity to Consumer Trust: Examining AI-Generated Content in Social Media Marketing
Journal of Economics Finance and Management Studies (2026) · doi:10.47191/jefms/v9-i9-50 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 946 | scored |
SRCE Academy – support for the systematic development of teachers' digital competences
Ubiquity Proceedings (2026) · doi:10.5334/uproc.297 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 947 | scored |
Artificial Intelligence-Assisted Diagnosis of Fungal Infections: Advances in Imaging and Beyond
Journal of Mycology and Infection (2026) · doi:10.17966/jmi.2026.31.3.93 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 948 | scored |
Artificial Intelligence, Climate Change, and Human Systems Behaviour: A Behavioural Systems Approach
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23084872 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 949 | Screened Out |
Beyond the Framework: A Whole Institution Approach to AI Literacy in Distance Education
Ubiquity Proceedings (2026) · doi:10.5334/uproc.262 · v1.0 |
2.7 (gemini-3.1-flash-lite) | — |
| 950 | Screened Out |
Preparing Graduates for AI-Augmented Professional Practice: Practitioner Insights and a Framework for Curriculum Design
Ubiquity Proceedings (2026) · doi:10.5334/uproc.265 · v1.0 |
2.2 (gemini-3.1-flash-lite) | — |