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: 17222 →
Screen: 16208 →
Eligible: 2414 →
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 | 15429 | 2026-11-07 04:07 |
ref — reference lists of read works | 1766 | 2026-11-07 02:24 |
manual — added manually | 27 | 2026-10-07 12:05 |
Citation candidates take metadata from OpenAlex; Crossref/Unpaywall supply DOIs, versions and open PDFs.
Search strategy (7)
Current search version: v1.0. A work matches if its title/abstract hits a term from EVERY group of at least one query (OR within a group; case-insensitive, word boundaries).
Queries (groups):
education × ai; psychometrics × ai; integrity × education; drift × education; drift × psychometrics
education: education, educational, student, learner, teacher, lecturer, classroom, university, higher education, school, curriculum, assessment, exam, examination, examinee, test taker, test score, grading, homework, coursework, online course, essay, academic integrity, academic misconduct, plagiarism, cheating, e-learning, MOOC
ai: generative AI, GenAI, large language model, LLM, ChatGPT, GPT-4, chatbot, AI-generated, AI-assisted, AI-resistant, AI-resilient, AI tool, artificial intelligence
integrity: proctoring, proctored, unproctored, non-proctored, remote proctoring, online exam, online test, online testing, exam integrity, testing integrity, academic integrity, academic dishonesty, cheating, contract cheating, grade inflation
psychometrics: item response theory, IRT, Rasch, psychometric, educational measurement, educational testing, differential item functioning, computerized adaptive testing, adaptive testing, automated test assembly, knowledge tracing, automated scoring, automated essay scoring, validity argument, construct validity, assessment validity, predictive validity
drift: parameter drift, dynamic item response, item parameter drift, rater drift, severity drift, concept drift, change point, change-point detection, item compromise, preknowledge, item exposure, test security, aberrant response, person-fit, differential item functioning
Semantic Scholar phrases: assessment validity generative AI; AI-resilient assessment higher education; AI-resistant assessment design; generative AI exam performance natural experiment; differential item functioning large language models humans; LLM as test taker item response theory; proctored versus unproctored online exam scores; generative AI grade inflation; item parameter drift detection; item compromise preknowledge detection; concept drift knowledge tracing; change point detection educational testing
arXiv: cs.CY, cs.CL, cs.HC, cs.AI, cs.LG, stat.AP, stat.ME. Result caps per query: OpenAlex 50, Semantic Scholar 25, ERIC 25. Citation searching: depth 1 from read and 👍 works, depth 2 from 👍 only; neighbours of 👍 works bypass the lexical filter.
| Search version | Records (search) | First | Last |
|---|---|---|---|
v1.0 | 15429 | 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 | 8020 |
gemini-3.5-flash-lite | 7635 |
— | 619 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 168 |
gemini-3.8-flash | v2.1 | 102 |
gemini-3.7-flash | v2.1 | 95 |
Scoring rubric v2.3 (0–10 anchors)
The model scores 0–10 per criterion; the anchors below are embedded in the prompt. Formula and weights are in the footnote above. Changing anchors or weights without a new version is blocked by a hash check.
| Criterion | Weight | 2 | 5 | 8 |
|---|---|---|---|---|
| Topic relevance (R) | × min(1, R/7.0) | No link to education or assessment: image, audio or model watermarking, model IP protection, general NLP benchmarks, AI in industry. | Adjacent: generative AI in education without an assessment focus; academic integrity with no evidence on assessment outcomes; detectors of AI-generated text or of student AI use; general psychometrics (adaptive testing, test assembly, IRT) with no drift, validity or AI angle. | Core: validity of assessment under generative AI (what grades and scores mean when AI is available); how grades shift under unsupervised conditions or cheating (proctored vs unproctored exams, grade inflation); AI-resistant or AI-resilient assessment design; quantitative methods that detect or model distorted assessment or learning data (DIF between humans and LLMs, item parameter or rater drift, item compromise/preknowledge, change-point or concept-drift detection, LLMs as test takers or raters). |
| Methodological rigor (M) | 0.32 | Vague evaluation, convenience sample of a few dozen, self-report only, no control group or baseline. | Clear dataset or sample (hundreds), standard metrics or validated instruments, comparison with baselines or a control group. | Randomized/controlled or preregistered, large or multi-site/multilingual data, adversarial or robustness checks, effect sizes with uncertainty. |
| Engineering countermeasures (E) | 0.25 | No method or countermeasure: describes impact, attitudes or policy only. | A concrete method or countermeasure with an evaluation: statistical test or model, detector, scoring procedure, or an assessment design tried on real cohorts. | A rigorous, reusable method: formal model with guarantees or error control, open code, validated on both real and simulated data. |
| Consensus challenge (C) | 0.43 | Reconfirms standard wisdom. | Nuances the consensus (e.g. detectors fail under mild paraphrasing; AI use does not harm an outcome it is assumed to harm). | Directly refutes high-profile claims with rigorous evidence. |
| Novelty (N) | 0.00 | Replicates well-known findings (another survey of student attitudes to ChatGPT, another test of one detector on a handful of prompts). | New dataset, benchmark, population or setting, or a previously untried technique. | Fundamentally new attack or defence, a theoretical/impossibility result, or a finding that changes how the field frames the problem. |
The model interpolates between anchors. The deep-analysis score is computed by code with the same formula from criteria_breakdown.
| Rubric version | Papers scored |
|---|---|
v2.0 | 261 |
v2.1 | 183 |
v2.2 | 20 |
v2.3 (current) | 15810 |
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): 13821 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 | 8944 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3725 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1152 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1316 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1044 | 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)
6.58 / 10
Analyzed papers: 630
Quality Stratification
🟢 High rigor (M ≥ 7.0): 322
🟡 Moderate rigor (4.0 ≤ M < 7.0): 291
🔴 Low rigor (M < 4.0): 17
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 243 |
other | 43 |
theoretical | 27 |
rct | 13 |
assessment_design | 167 |
natural_experiment | 5 |
quasi_experiment | 54 |
detector_evaluation | 41 |
survey | 7 |
review | 18 |
experimental | 1 |
qualitative | 11 |
PRISMA 2020 27-Item Compliance Checklist
Full audit of compliance with PRISMA 2020 checklist (Page et al., 2021) for automated systematic reviews.
| № | Section / Item | Checklist Description | Status | Implementation |
|---|---|---|---|---|
| 1 | Title | Identify as living systematic review | ✓ Впроваджено | Page title, report metadata |
| 2 | Abstract | Structured summary (background, objectives, methods, results) | ✓ Впроваджено | #prisma-abstract block above |
| 3 | Rationale | Rationale in context of existing knowledge | ✓ Впроваджено | Topic description and rationale |
| 4 | Objectives | Explicit objectives using PICO / PECO | ✓ Впроваджено | #pico-framework table above |
| 5 | Eligibility criteria | Inclusion/exclusion criteria, language, dates | ✓ Впроваджено | Thresholds R, Score, Open Access |
| 6 | Information sources | Databases, registers, search dates | ✓ Впроваджено | arXiv, Crossref, OpenAlex, Semantic Scholar, ERIC, citations |
| 7 | Search strategy | Full search strategy, boolean filters, versions | ✓ Впроваджено | search_version, term groups, API limits |
| 8 | Selection process | Selection process, automation, independent screening | ✓ Впроваджено | LLM screening rotation + editorial taste loop |
| 9 | Data collection | Data collection from full-text reports | ✓ Впроваджено | PDF → MD parser, deep prompt v2.1 |
| 10a-b | Data items | List of outcomes and study characteristics | ✓ Впроваджено | Summary, sample, design, claims, countermeasures |
| 11 | Study risk of bias | Assessment of study methodological quality | ✓ Впроваджено | Rigor criterion M (0–10), quality distribution |
| 12 | Effect measures | Effect measures for outcomes | — Обґрунтовано N/A | Qualitative synthesis without meta-analytic pooled effects |
| 13a-f | Synthesis methods | Synthesis criteria and grouping methods | ✓ Впроваджено | Tag-based clustering, comparative study cards |
| 14 | Reporting bias | Assessment of reporting/publication bias | ✓ Впроваджено | Analysis of paywall exclusions & open access skew |
| 15 | Certainty assessment | Certainty assessment of body of evidence | ✓ Впроваджено | Automated quote grounding against full-text Markdown |
| 16 | Study selection | Selection results (flow diagram, counts) | ✓ Впроваджено | 100% stage conservation closure verified |
| 17 | Study characteristics | Characteristics of included studies table | ✓ Впроваджено | Interactive table with DOI, venue, year, tags |
| 18 | Risk of bias in studies | Risk of bias findings across studies | ✓ Впроваджено | Methodological rigor M & study designs table |
| 19 | Results of individual studies | Results for each individual study | ✓ Впроваджено | Public paper pages /doi/{doi} with full analysis |
| 20a-d | Results of syntheses | Results of syntheses & sub-groups | ✓ Впроваджено | Daily living editions, RSS feed, tag filters |
| 21 | Reporting biases | Results of reporting biases assessment | ✓ Впроваджено | Paywall n=335, retracted n=0 |
| 22 | Certainty of evidence | Summary of evidence certainty | ✓ Впроваджено | Gwet's AC₁, Cohen's κ, stratified recall, ROC AUC |
| 23a-d | Discussion | Discussion, limitations, implications | ✓ Впроваджено | Limitations section (23b, 23c) in methods |
| 24a-c | Registration & protocol | Registration, protocol availability, amendments | ✓ Впроваджено | Public repo, version history of search & rubric |
| 25 | Support | Financial & non-financial support | ✓ Впроваджено | Free API tiers declared in methods |
| 26 | Competing interests | Competing interests statement | ✓ Впроваджено | Declared in methods section |
| 27 | Data & code availability | Availability of data, analytical code, materials | ✓ Впроваджено | Export to CSV / JSON buttons, open-source code |
Limitations (23b, 23c)
- Open access only: paywalled papers never reach the synthesis, so the sample skews to open and preprint work.
- Screening ranks by quality and novelty rather than pure eligibility, so the sample skews towards counter-consensus work. Validity is measured against one expert (see above); a single LLM reviewer, model bias and version differences remain possible.
- The lexical filter and result caps can miss works with other terminology; sources skew English-language.
- Records scored under an older rubric version are not rescored automatically (see version table).
- Free Gemini quota: daily limits slow analysis; some selected works wait in the queue.
Registration, protocol, amendments, support, data (24–27)
- Registration: not registered. The protocol is the configuration (topic, queries, rubric, thresholds) in the project repository; it is reproduced in this block.
- Amendments: search versions (search_version) and rubric versions (rubric_version), tables above.
- Funding: none declared, free API tiers are used. Competing interests: none declared.
- Data: analyses and metadata are on article pages, RSS and sitemap; full texts are private (copyright) and not published.
Filter by Tags
Select one or more tags to refine the flow numbers and article corpus.
#psychometrics (101)
#Psychometrics (59)
#Item Response Theory (48)
#academic integrity (46)
#assessment validity (38)
#item response theory (38)
#Assessment Validity (37)
#higher education (36)
#Automated Essay Scoring (30)
#automated essay scoring (29)
#Generative AI (28)
#construct validity (26)
#IRT (24)
#Construct Validity (23)
#LLM as rater (20)
#generative AI (20)
#Academic Integrity (19)
#Automated Scoring (17)
#Higher Education (17)
#Large Language Models (17)
#LLM Evaluation (16)
#assessment redesign (16)
#assessment_design (16)
#large language models (15)
#AI-resilient assessment (14)
#LLM as Rater (14)
#automated scoring (14)
#prompt engineering (14)
#assessment design (13)
#automated assessment (13)
#validity (13)
#Assessment Redesign (12)
#GPT-4 (12)
#Prompt Engineering (12)
#academic_integrity (12)
#differential item functioning (12)
#ChatGPT (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#Automated Item Generation (10)
#LLM-as-a-judge (10)
#LLMs as raters (10)
#inter-rater reliability (10)
#item_response_theory (10)
#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)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 4101 | scored |
How to Assess? II
Routledge eBooks (2026) · doi:10.4324/9781003476832-5 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4102 | scored |
Digital health policy to innovation: health system maturity and AI readiness in Serbia
European Journal of Public Health (2026) · doi:10.1093/eurpub/ckag169.1221 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4103 | Screened Out |
The Effects of AI-Assisted Writing Instructions on Creativity and Technical Skills in Art Majors
SAGE Open (2026) · doi:10.1177/21582440261501842 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 4104 | scored |
F.9. Scientific session: Implementing digital and AI skills in public health education and the health workforce
European Journal of Public Health (2026) · doi:10.1093/eurpub/ckag169.499 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4105 | scored |
PSY-ICD study: AI phenotyping identifies device-focused distress overlooked by conventional mood assessment in ICD recipients
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.4031 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4106 | scored |
Analyzing Students’ Experimental Design Skills: Insights from a Rasch Model Study
Jurnal Penelitian dan Pembelajaran IPA (2026) · doi:10.62870/jppi.v12i1.37109 · v1.0 |
5.3 (gemini-3.5-flash-lite) | — |
| 4107 | Screened Out |
Course of human geography with climate change point of view
arXiv/Crossref (2023) · doi:10.5194/egusphere-egu23-7278 · v1.0 |
1.0 (gemini-3.5-flash-lite) | — |
| 4108 | scored |
DEVELOPMENT OF A SUPPORT SYSTEM OF REFLECTION ON LEARNING IN METAVERSE USING CHANGE-POINT DETECTION
INTED proceedings (2023) · doi:10.21125/inted.2023.2113 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4109 | scored |
Screening of thermal characteristics and assessment of comparative energy efficiency potential in a residential district
Advances in Building Energy Research (2023) · doi:10.1080/17512549.2023.2183522 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4110 | scored |
Unfolding and modeling the recovery process after COVID lockdowns
Scientific Reports (2023) · doi:10.1038/s41598-023-30100-5 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4111 | scored |
Thinking with Robots
American Literature (2023) · doi:10.1215/00029831-10575162 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4112 | scored |
The change point toolkit for teaching
Routledge eBooks (2023) · doi:10.4324/9781003018537-24 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4113 | Screened Out |
THE MEDIATING ROLE OF ACADEMIC INTEGRITY IN THE RELATIONSHIP BETWEEN AWARENESS AND ATTITUDES TOWARDS PLAGIARISM
International Journal of Legal Studies ( IJOLS ) (2026) · doi:10.5604/01.3001.0055.9362 · v1.0 |
1.9 (gemini-3.5-flash-lite) | — |
| 4114 | scored |
Academic integrity in the age of generative artificial intelligence: from detection to institutional policy
Education Quality Assurance (2026) · doi:10.58319/26170493_2026_2_42 · v1.0 |
5.0 (gemini-3.5-flash-lite) | — |
| 4115 | Screened Out |
The Responsible Use of Artificial Intelligence in EFL Learning
TRANSFORM Journal of English Language Teaching and Learning (2026) · doi:10.24114/tj.v15i2.75405 · v1.0 |
1.0 (gemini-3.5-flash-lite) | — |
| 4116 | Screened Out |
English Faculty Members’ Perception and Attitude Toward the Use of ChatGPT by English Language Teaching in Higher Education in Saudi Arabia
University of Tabuk Journal for Humanities and Social Sciences (2026) · doi:10.68114/1658-8797.1020 · v1.0 |
1.8 (gemini-3.5-flash-lite) | — |
| 4117 | scored |
Information Technology-Enhanced Vocational Education: A Student-Centered Instructional Framework and Descriptive Teacher Survey
Archives des sciences a multidisciplinary journal (2026) · doi:10.68304/as/75309 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4118 | Screened Out |
Artificial Intelligence, Creative Authorship, and Education in Algorithmic Environments
Archives des sciences a multidisciplinary journal (2026) · doi:10.68304/as/76104 · v1.0 |
3.5 (gemini-3.1-flash-lite) | — |
| 4119 | Screened Out |
Navigating Academic Dishonesty and Integrity in the Age of Generative AI: Causes, Challenges and Solutions for Higher Education
Asian Journal of University Education (2026) · doi:10.24191/ajue.v22i2.018 · v1.0 |
3.2 (gemini-3.1-flash-lite) | — |
| 4120 | Screened Out |
THE EMPLOYMENT OF GENERATIVE ARTIFICIAL INTELLIGENCE BY UNIVERSITY STUDENTS AS A FORM OF ACADEMIC DISHONESTY (BASED ON FOREIGN LANGUAGE CLASSES EXPERIENCE)
Профессиональное образование в России и за рубежом (2026) · doi:10.54509/22203036_2026_2_26 · v1.0 |
4.1 (gemini-3.1-flash-lite) | — |
| 4121 | scored |
INSTITUTIONALIZATION AS AN ELEMENT OF UKRAINE'S HIGHER EDUCATION POLICY HARMONIZATION IN THE PROCESS OF EUROPEAN INTEGRATION
ACTUAL PROBLEMS OF INTERNATIONAL RELATIONS (2026) · doi:10.17721/apmv.2026.167.1.14-20 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4122 | Screened Out |
Adoption of Artificial Intelligence in Facilitating the Professional Development of Lecturers
Education Science and Management (2026) · doi:10.56578/esm040201 · v1.0 |
3.2 (gemini-3.1-flash-lite) | — |
| 4123 | scored |
Fostering Honesty in Generation Z: Islamic Education Teachers' Strategies in the Digital Age
GHAITSA Islamic Education Journal (2026) · doi:10.62159/ghaitsa.v7i2.2759 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4124 | scored |
Ethics of Using Artificial Intelligence (AI) in Islamic Education: A Philosophical Inquiry into Responsibility, Academic Honesty, and Epistemic Authority
Perspektif Jurnal Pendidikan dan Ilmu Bahasa (2026) · doi:10.59059/perspektif.v4i2.3339 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4125 | No Fulltext |
A Context-Aware Evaluation Framework for Next-Generation Online Examination Systems: Formal Process Modeling and Architecture
VAWKUM Transactions on Computer Sciences (2026) · doi:10.21015/vtcs.v14i1.2148 · v1.0 |
7.4 (gemini-3.1-flash-lite) | — |
| 4126 | Screened Out |
Transforming Nursing Education Through ChatGPT and Generative AI: A Systematic Review of Learning Opportunities, Educational Challenges, and Learning Outcomes
JENDELA NURSING JOURNAL (2026) · doi:10.31983/jnj.v10i1.15157 · v1.0 |
2.5 (gemini-3.1-flash-lite) | — |
| 4127 | Not Article |
JENDELA NURSING JOURNAL (2026) · doi:10.31983/jnj.v10i1 · v1.0 |
- | — |
| 4128 | Screened Out |
Safe Exam Browser (SEB) in Digital Examinations: Supporting Students’ Academic Integrity in Secondary Education
Digital Learning Social Science and Life-course Studies (2026) · doi:10.70211/disolife.v2i1.736 · v1.0 |
4.1 (gemini-3.1-flash-lite) | — |
| 4129 | scored |
Utilizing Generative AI in Islamic Religious Education Learning: Opportunities and Challenges in the Digital Era
Assoeltan Indonesian Journal of Community Research and Engagement (2026) · doi:10.70610/edujavare.v4i01.1947 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4130 | Screened Out |
Educational AI Companions as Anticipatory Design Objects: Futures Literacy, Peer-Ethics, and Responsibility Architecture
International Journal of Academic Studies in Technology and Education (2026) · doi:10.55549/ijaste.101 · v1.0 |
3.5 (gemini-3.1-flash-lite) | — |
| 4131 | scored |
Examining the Impact of ChatGPT-Assisted Academic Writing on the Writing Performance and Critical Thinking Skills of Undergraduate Students in Pakistan
Research Journal of Human and Social Aspects (2026) · doi:10.71085/rjhsa.04.02.73 · v1.0 |
5.1 (gemini-3.1-flash-lite) | — |
| 4132 | Screened Out |
Academic Staff Experiences Regarding the Use of Generative Artificial Intelligence in Teacher Training: A Phenomenological Study
The Eurasia Proceedings of Educational and Social Sciences (2026) · doi:10.55549/epess.1033 · v1.0 |
2.4 (gemini-3.1-flash-lite) | — |
| 4133 | Screened Out |
From Generation to Autonomy: A Framework for Understanding Generative, Agent, and Agentic AI in Higher Education
The Eurasia Proceedings of Educational and Social Sciences (2026) · doi:10.55549/epess.1027 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 4134 | Screened Out |
The Role of Artificial Intelligence in Transforming Medical Education in Bangladesh: A Systematic Review of Applications, Opportunities, and Challenges
Pacific Journal of Medical Research (2026) · doi:10.70818/pjmr.v03i02.0234 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 4135 | scored |
The role of students as active participants of the hybrid learning environment in vocational education
Journal of Education Health and Sport (2026) · doi:10.12775/jehs.2026.93.75304 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4136 | scored |
Pelatihan Penggunaan Mendeley sebagai Upaya Meningkatkan Kompetensi Manajemen Referensi Ilmiah bagi Mahasiswa
NuCSJo Nusantara Community Service Journal (2026) · doi:10.70437/nucsjo.v2i4.435 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4137 | scored |
Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI‑Generated and Human Data
Journal of Educational Data Mining (2026) · doi:10.5281/zenodo.18247289 · v1.0 |
5.3 (gemini-3.1-flash-lite) | — |
| 4138 | Not Article |
Distractors for Multiple‐Choice Questions in the New Generations of L2 Assessment
The TESOL Encyclopedia of English Language Teaching (2026) · doi:10.1002/9781118784235.eelt1107 · v1.0 |
- | — |
| 4139 | Not Article |
L2 Dynamic Assessment in the Age of Artificial Intelligence
The TESOL Encyclopedia of English Language Teaching (2026) · doi:10.1002/9781118784235.eelt1112 · v1.0 |
- | — |
| 4140 | Screened Out |
Automated versus human scoring of the Rey-Osterrieth Complex Figure Test: a rapid review
Frontiers in Psychiatry (2026) · doi:10.3389/fpsyt.2025.1746720 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 4141 | scored |
Treatment-resistant depression: time to rethink current definitions and clinical practice
Frontiers in Psychiatry (2026) · doi:10.3389/fpsyt.2025.1733678 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4142 | scored |
Adaptation of the Japanese Version of the 12-Item Attitudes Towards Artificial Intelligence Scale for Medical Trainees: Multicenter Development and Validation Study
JMIR Medical Education (2026) · doi:10.2196/81986 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4143 | scored |
Machine learning in the development and application of patient-reported outcome measures (PROMs) for surgical patients: a systematic review
Journal of Patient-Reported Outcomes (2026) · doi:10.1186/s41687-026-00992-8 · v1.0 |
5.0 (gemini-3.5-flash-lite) | — |
| 4144 | scored |
Creation and Validity and Reliability Testing of an AI Literacy Assessment Scale for Teachers - Based on a Dual-track Empirical Study of Zhejiang Province’s Primary and Secondary Schools and Higher Education Institutions
Global Education Bulletin (2026) · doi:10.71052/grb2025/nxgj6555 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4145 | scored |
The Age of Human Comparison With AI: Development of AI Competence Anxiety Scale and AI Optimism Scale and Their Relationship With Generative AI Literacy and AI Self-Efficacy
International Journal of Human-Computer Interaction (2026) · doi:10.1080/10447318.2025.2609913 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4146 | scored |
Adoption of Explainable Artificial Intelligence in Retail Investors’ Decision-Making : Evidence from India
Indian Journal of Finance (2026) · doi:10.17010/ijf/2026/v20i1/175903 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4147 | scored |
Developing a Multi-Dimensional Evaluation Framework for AI-Generated Reading Questions: A Case Study of Doubao
International Conference on Informatics Education and Computer Technology Applications (IECA) (2026) · doi:10.1145/3802133.3802285 · v1.0 |
5.3 (gemini-3.5-flash-lite) | — |
| 4148 | scored |
Large Language Models And Examination Performance In Healthcare Education: A Bibliometric Analysis (Preprint)
arXiv/Crossref (2026) · doi:10.2196/preprints.91571 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4149 | abstract_only |
Reducing Decision Uncertainty in AI-Based Student Career Guidance Using a Hybrid Machine Learning and Large Language Model Framework
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.18271324 · v1.0 |
5.6 (gemini-3.5-flash-lite) | — |
| 4150 | Screened Out |
Functional Validation of a Generative AI-Based Mobile App for Assessing Speech Difficulties in Children
International Journal of Interactive Mobile Technologies (iJIM) (2026) · doi:10.3991/ijim.v20i01.57991 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |