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 | 181 |
gemini-3.8-flash | v2.1 | 108 |
gemini-3.7-flash | v2.1 | 101 |
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: 655
Quality Stratification
🟢 High rigor (M ≥ 7.0): 327
🟡 Moderate rigor (4.0 ≤ M < 7.0): 309
🔴 Low rigor (M < 4.0): 19
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 253 |
other | 45 |
theoretical | 27 |
rct | 13 |
assessment_design | 174 |
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 (49)
#Assessment Validity (40)
#assessment validity (39)
#item response theory (39)
#higher education (37)
#Automated Essay Scoring (33)
#automated essay scoring (32)
#Generative AI (29)
#Construct Validity (26)
#construct validity (26)
#IRT (24)
#LLM as rater (22)
#generative AI (20)
#Academic Integrity (19)
#Higher Education (19)
#Large Language Models (19)
#assessment redesign (18)
#Automated Scoring (17)
#LLM Evaluation (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)
#Medical Education (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 6201 | Screened Out |
Public Sentiment and Social Cognition toward Generative AI in Education: A Comparative Analysis of ChatGPT and DeepSeek in Sina Weibo Discourse
ASTRA MAN (2026) · doi:10.53941/astm.2026.100003 · v1.0 |
3.4 (gemini-3.5-flash-lite) | — |
| 6202 | Screened Out |
The Use of ChatGPT in ELT Practices for University Students
Conference on English Language Teaching (2026) · doi:10.24090/2t5nr802 · v1.0 |
1.8 (gemini-3.5-flash-lite) | — |
| 6203 | Screened Out |
कतरम बदधमतत और मनव-कदरत शकष: सभवनए, चनतय एव भवषय क शकषक दश
Adhigamy International Journal of Modern Research Perspectives (AIJMRP) (2026) · doi:10.67985/aijmrp.v1i2.03 · v1.0 |
1.1 (gemini-3.5-flash-lite) | — |
| 6204 | scored |
Innovations Beyond Boundaries: Interdisciplinary Research in Science, Engineering and Social Sciences
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21394382 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6205 | Screened Out |
Penggunaan ChatGPT dalam Pengerjaan Tugas Kuliah Mahasiswa Program Studi Komunikasi Digital dan Media
Educatoria Jurnal Ilmiah Ilmu Pendidikan (2026) · doi:10.36312/educatoria.v6i3.1500 · v1.0 |
1.8 (gemini-3.5-flash-lite) | — |
| 6206 | Screened Out |
Evaluating University Students’ Perceptions of Artificial Intelligence Integration in Islamic Civilization History Learning and Its Relation to Academic Adab
QALAMUNA Jurnal Pendidikan Sosial dan Agama (2026) · doi:10.37680/qalamuna.v18i2.9520 · v1.0 |
1.8 (gemini-3.5-flash-lite) | — |
| 6207 | Screened Out |
Detecting AI-Generated Text: Mechanisms, Robustness, and the Limits of Reliable Detection
International Journal of Research Publication and Reviews (2026) · doi:10.55248/gengpi.07.0726.2103 · v1.0 |
3.0 (gemini-3.5-flash-lite) | — |
| 6208 | scored |
Martial law as a factor for the modernization of Ukrainian law of higher education
Analytical and Comparative Jurisprudence (2026) · doi:10.24144/2788-6018.2026.03.2.63 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6209 | scored |
Legal discussion aspects of the Code of Academic Integrity in the context of key moral values of a teacher are highlighted
Analytical and Comparative Jurisprudence (2026) · doi:10.24144/2788-6018.2026.03.2.40 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6210 | Screened Out |
Ethical AI in Islamic Education: ChatGPT's Disruption to the Authority of Knowledge and the Future of Children's Education in the Perspective of the Book of Idhotun Nasyi'in
Proceeding of Annual International Conference on Islamic Education (AICIED) (2026) · doi:10.21154/aicied.v4i.1754 · v1.0 |
1.3 (gemini-3.5-flash-lite) | — |
| 6211 | Screened Out |
Ethical Integration of Artificial Intelligence in Islamic Teacher Education: An Islamic Perspective
Proceeding of Annual International Conference on Islamic Education (AICIED) (2026) · doi:10.21154/aicied.v4i.1752 · v1.0 |
1.3 (gemini-3.5-flash-lite) | — |
| 6212 | Screened Out |
An Empirical Study of Fine-Tuning Pre-Trained Code Models and Adapters for the Classification of Source Code Plagiarism Instances
Applied Sciences (2026) · doi:10.3390/app16147156 · v1.0 |
4.3 (gemini-3.5-flash-lite) | — |
| 6213 | scored |
Guest editorial: Design for belonging and engagement: designing inclusive higher education futures
Information and Learning Sciences (2026) · doi:10.1108/ils-07-2026-270 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6214 | scored |
Challenges and Ethical Concerns of Artificial Intelligence in Research of Teacher Education
International Journal of Advanced Research in Science Communication and Technology (2026) · doi:10.48175/ijarsct-37685 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6215 | Screened Out |
Acceptability Scale for the Use of Large Language Models (LLMs) by Project Teams: Development and Preliminary Validation
Systems (2026) · doi:10.3390/systems14040366 · v1.0 |
3.0 (gemini-3.5-flash-lite) | — |
| 6216 | abstract_only |
Elder-Sim: A Psychometrically Validated Platform for Personality-Stable Elderly Digital Twins
medRxiv (2026) · doi:10.64898/2026.03.25.26349036 · v1.0 |
6.7 (gemini-3.1-flash-lite) | — |
| 6217 | No Fulltext |
The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGENIE from its Bottle
arXiv/Crossref (2026) · doi:10.31234/osf.io/arfg3_v1 · v1.0 |
7.1 (gemini-3.1-flash-lite) | — |
| 6218 | Screened Out |
Advancing 21st-Century Professional Competencies with Learning Analytics in the Age of Generative AI
Journal of Learning Analytics (2026) · doi:10.18608/jla.2026.9743 · v1.0 |
4.6 (gemini-3.1-flash-lite) | — |
| 6219 | Screened Out |
Developing Critical Thinking Skills Through Active Learning: Strategies for Higher Secondary and Undergraduate Education
Social science review archives. (2026) · doi:10.70670/sra.v4i1.1910 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 6220 | Screened Out |
Emotional Responses to AI Use: Development of the SAG-AI Scale for Shame and Guilt
International Journal of Human-Computer Interaction (2026) · doi:10.1080/10447318.2026.2648803 · v1.0 |
3.8 (gemini-3.1-flash-lite) | — |
| 6221 | Screened Out |
Development of a STEAM–ESD-Based AI Chatbot to Enhance Students’ Scientific Literacy in Biotechnology Learning
Formatif Jurnal Ilmiah Pendidikan MIPA (2026) · doi:10.30998/formatif.v16i1.2197 · v1.0 |
3.5 (gemini-3.1-flash-lite) | — |
| 6222 | Screened Out |
Mapping the Landscape of Rubric-Based Assessment Studies: A Bibliometric Review
The Eastasouth Journal of Learning and Educations (2026) · doi:10.58812/esle.v4i01.966 · v1.0 |
2.5 (gemini-3.1-flash-lite) | — |
| 6223 | Screened Out |
An Inquiry-Based Experiment Teaching Intermediate Geometry Using Polygon Generating Polygons
International Journal for Technology in Mathematics Education (2026) · doi:10.1564/tme_v33.1.03 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 6224 | scored |
Predictive Analytics for Marital Counseling: AI-Based Early Divorce Detection and Intervention Model
World Journal of Research and Review (2026) · doi:10.31871/wjir.20.3.3 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6225 | Screened Out |
Establishing the Role of Korean Language Teachers in the Age of Artificial Intelligence : A Topic Modeling Analysis of Research Trends
arXiv/Crossref (2026) · doi:10.15652/ink.2026.23.1.231 · v1.0 |
3.1 (gemini-3.1-flash-lite) | — |
| 6226 | Screened Out |
Sensory Profile-2 Summary and Interpretation (SPSI) Chatbot Prototype Development and Utilization Prospects
arXiv/Crossref (2026) · doi:10.14519/kjot.2026.34.1.02 · v1.0 |
2.4 (gemini-3.1-flash-lite) | — |
| 6227 | scored |
Artificial Intelligence in Essay Scoring: A Comparative Study of Transformer-Based Models
Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) (2026) · doi:10.23887/janapati.v15i1.103823 · v1.0 |
5.0 (gemini-3.1-flash-lite) | — |
| 6228 | Screened Out |
INNOVATION WITHOUT RESISTANCE? A CRITICAL EXAMINATION OF APPLYING INNOVATION RESISTANCE THEORY TO THE CASE OF GENERATIVE AI CHATGPT
UCJC Business and Society Review (formerly known as Universia Business Review) (2026) · doi:10.57087/ucjcbsr.2026.23.88.4976 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 6229 | No Fulltext |
Beyond Generation to Reasoning: Psychometric Equivalence of JLPT Items Developed via Gemini 3.0 Pro's “Deep Think” Mode
The Korean Journal of Japanese Education (2026) · doi:10.21808/kjje.74.05 · v1.0 |
6.7 (gemini-3.1-flash-lite) | — |
| 6230 | Screened Out |
Open Data and AI Safety: Contributions of the Semantic Web to the Evaluation of Large Language Models
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.19348883 · v1.0 |
3.6 (gemini-3.1-flash-lite) | — |
| 6231 | scored |
From Text to Events: Turning Freedom House Reports into Evidence of Democratization and Democratic Backsliding
arXiv/Crossref (2026) · doi:10.33774/apsa-2026-hgrxk · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6232 | Not Article |
Dataset: From Legacy-Making to the Development of the Global Emotional Comfort Index
Mendeley Data (2026) · doi:10.17632/rg43z5hp5m · v1.0 |
- | — |
| 6233 | scored |
Cultural adaptation and validation of a ChatGPT literacy scale for university students in Chile
Current Psychology (2026) · doi:10.1007/s12144-026-09314-z · v1.0 |
0.0 (Flash Lite) | — |
| 6234 | Screened Out |
Teaching Strategy Linkages in University Entrepreneurship Education: A Human-AI Collaborative Learning Perspective
SAGE Open (2026) · doi:10.1177/21582440261495243 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 6235 | scored |
Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges
Cancer Reports (2026) · doi:10.1002/cnr2.70665 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 6236 | Not Article |
Generative agents as behavioural proxies for depression in language-driven agent-based models
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23212252 · v1.0 |
- | — |
| 6237 | Screened Out |
AI in the Classroom: Student Teachers’ Experiences and Practices in a Changing Learning Landscape
Asia-Pacific Journal of Futures in Education and Society (2026) · doi:10.58946/apjfes-5.3.p3 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 6238 | Screened Out |
LLM Hallucination of Citations in Economics Persists With Web‐Enabled Models
Southern Economic Journal (2026) · doi:10.1002/soej.70069 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 6239 | scored |
Internet Infrastructure Science: The Transversal Coupling Substrate of Information, Computation, Agency, Execution, and Cross-System Propagation
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23247083 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6240 | scored |
Artificial intelligence and machine learning in blepharoplasty procedure: a systematic review and meta-analysis
Annals of Middle Eastern Medicine (2026) · doi:10.24911/amem.15-2998 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6241 | scored |
Beyond Faster Learning: How GenAI Learning Efficiency Translates into Effective Learning Outcomes
International Journal of Social Science Research (IJSSR) (2026) · doi:10.70558/ijssr.2026.v3.i5.301338 · v1.0 |
5.4 (gemini-3.5-flash-lite) | — |
| 6242 | Screened Out |
The self-efficacy–SEL nexus in digital teacher education: A structural model of pre-service teachers’ readiness for future classrooms
International Journal of Humanities and Education Research (2026) · doi:10.33545/26649799.2026.v8.i10a.517 · v1.0 |
2.2 (gemini-3.5-flash-lite) | — |
| 6243 | scored |
The Role of Artificial Intelligence in Improving Healthcare Quality, Patient Safety, and Service Delivery: A Systematic Review of Real-World Applications
European Journal of Prosthodontics and Restorative Dentistry (2026) · doi:10.1922/ejprd.v34i13s.2192 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6244 | scored |
Artificial Intelligence in Diagnostic Pathology: A Narrative review on Current Applications, Emerging Challenges, and Future Perspectives
National Journal of Laboratory Medicine (2026) · doi:10.7860/njlm/2026/90223.3008 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6245 | Screened Out |
It takes a village: Feedback providers and multilingual graduate students’ writing self-perceptions
Assessing Writing (2026) · doi:10.1016/j.asw.2026.101128 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 6246 | scored |
Digitization and digital preservation of Ayurvedic manuscripts: Challenges and new trends
International Journal of Advanced Academic Studies (2026) · doi:10.33545/27068919.2026.v8.i10a.2199 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6247 | Screened Out |
AI Agents as Policy Instruments in Education: Reducing School Abandonment through Adaptive Governance Systems
European Journal of Sustainable Development (2026) · doi:10.14207/ejsd.2026.v15n4p384 · v1.0 |
2.5 (gemini-3.5-flash-lite) | — |
| 6248 | scored |
Table Olive Fermentation: Microbial Ecology, Metabolite Transformation, and Emerging Non‐Thermal and Omics‐Based Technologies
Journal of Food Science (2026) · doi:10.1111/1750-3841.71520 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6249 | scored |
A Holistic Framework for Cervical Cancer Screening and Risk Stratification: Integrating Etiology, Morphology, Epigenetics, and Microecology
Cancer Management and Research (2026) · doi:10.2147/cmar.s633709 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 6250 | scored |
Artificial Intelligence-Powered Contrast-Free Myocardial Scar Assessment
Journal of the American College of Cardiology (2026) · doi:10.1016/j.jacc.2026.07.055 · v1.0 |
0.0 (Flash Lite) | — |