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 | 169 |
gemini-3.8-flash | v2.1 | 104 |
gemini-3.7-flash | v2.1 | 96 |
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)
6.57 / 10
Analyzed papers: 634
Quality Stratification
🟢 High rigor (M ≥ 7.0): 322
🟡 Moderate rigor (4.0 ≤ M < 7.0): 294
🔴 Low rigor (M < 4.0): 18
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 245 |
other | 43 |
theoretical | 27 |
rct | 13 |
assessment_design | 168 |
natural_experiment | 5 |
quasi_experiment | 54 |
detector_evaluation | 42 |
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=336, 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 (103)
#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 (31)
#Automated Essay Scoring (30)
#Generative AI (28)
#construct validity (26)
#IRT (24)
#Construct Validity (23)
#LLM as rater (21)
#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 (16)
#prompt engineering (15)
#AI-resilient assessment (14)
#LLM as Rater (14)
#automated scoring (14)
#GPT-4 (13)
#assessment design (13)
#automated assessment (13)
#differential item functioning (13)
#validity (13)
#Assessment Redesign (12)
#ChatGPT (12)
#Prompt Engineering (12)
#academic_integrity (12)
#LLMs as raters (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#Automated Item Generation (10)
#LLM-as-a-judge (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 |
|---|---|---|---|---|
| 4501 | scored |
Knowledge, attitudes, and perceptions of registered medical technologists on the adoption of Artificial Intelligence among university-affiliated hospital clinical laboratories in Metro Manila
Clinica Chimica Acta (2026) · doi:10.1016/j.cca.2026.121534 · v1.0 |
0.0 (Flash Lite) | — |
| 4502 | scored |
From Training to Trash AI Compute Depreciation and E Waste Generation in Hyperscale Data Centres
International Journal of Versatile Research and Analysis (2026) · doi:10.56975/ijvra.v4i10.711718 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4503 | scored |
AI-ASSISTED DECISION-MAKING IN PUBLIC SECURITY
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.56082/annalsarscimilit.2026.3.72 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4504 | scored |
Determinants of utility perception of MahaVISTAAR-AI mobile app among the user farmers
International Journal of Agriculture and Food Science (2026) · doi:10.33545/2664844x.2026.v8.i10n.2131 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4505 | scored |
XAI for Psoriasis and Psoriatic Arthritis: A Systematic Survey of Diagnostic, Prognostic, and Therapeutic Applications
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.5935/jetia.v12i61.3445 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4506 | scored |
Artificial intelligence-based medical image segmentation in paediatric physeal injury and premature physeal closure: a narrative review
Advanced Orthopaedics (2026) · doi:10.1016/j.advop.2026.10.003 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4507 | Screened Out |
Generative Artificial Intelligence Use and Applied Higher-Order Cognitive Performance Among Health Informatics Students: A Multicenter Cross-Sectional Case-Based Assessment Study
Advances in Medical Education and Practice (2026) · doi:10.2147/amep.s650067 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 4508 | scored |
Artificial intelligence-assisted assessment of graft microcirculation in transplant anaesthesia
Indian Journal of Anaesthesia (2026) · doi:10.4103/ija.ija_1729_25 · v1.0 |
0.0 (Flash Lite) | — |
| 4509 | scored |
Artificial intelligence-powered visual simulation in basketball training and competitive performance among collegiate basketball students in China
International Journal of Multidisciplinary Trends (2026) · doi:10.22271/multi.2026.v8.i10a.1238 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4510 | scored |
AI Based Anxiety detection
JOURNAL OF ADVANCE AND FUTURE RESEARCH (2026) · doi:10.56975/jaafr.v4i10.515718 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4511 | scored |
Evaluation of an advanced large language model as an educational tool in understanding the impact of the exposome on airway epithelial barrier dysfunction and TSLP release in asthma
Clinica Chimica Acta (2026) · doi:10.1016/j.cca.2026.122523 · v1.0 |
0.0 (Flash Lite) | — |
| 4512 | No Fulltext |
Monitoring Online Tests through Data Visualization
IEEE Transactions on Knowledge and Data Engineering (2008) · doi:10.1109/tkde.2008.133 · v1.0 |
5.4 (gemini-3.5-flash-lite) | — |
| 4513 | No Fulltext |
Automated Online Exam Proctoring
IEEE Transactions on Multimedia (2017) · doi:10.1109/tmm.2017.2656064 · v1.0 |
6.0 (gemini-3.5-flash-lite) | — |
| 4514 | No Fulltext |
Massive Open Online Proctor
ACM Conference on Computer Supported Cooperative Work (CSCW) (2015) · doi:10.1145/2675133.2675245 · v1.0 |
5.6 (gemini-3.5-flash-lite) | — |
| 4515 | Screened Out |
When technological momentum overshadows pedagogical alignment: a systematic review of AI-generated formative feedback in higher education
Assessment & Evaluation in Higher Education (2026) · doi:10.1080/02602938.2026.2674230 · v1.0 |
4.4 (gemini-3.5-flash-lite) | — |
| 4516 | Screened Out |
AI feedback literacy in higher education: understanding, measuring, and predicting student feedback uptake
Assessment & Evaluation in Higher Education (2025) · doi:10.1080/02602938.2025.2587924 · v1.0 |
4.0 (gemini-3.5-flash-lite) | — |
| 4517 | Screened Out |
Developing generative AI literacies through self-regulated learning: A human-centered approach
Computers and Education Artificial Intelligence (2025) · doi:10.1016/j.caeai.2025.100482 · v1.0 |
4.2 (gemini-3.5-flash-lite) | — |
| 4518 | Screened Out |
Personality traits for self-regulated learning with generative artificial intelligence: The case of ChatGPT
Computers and Education Artificial Intelligence (2024) · doi:10.1016/j.caeai.2024.100315 · v1.0 |
2.1 (gemini-3.5-flash-lite) | — |
| 4519 | Screened Out |
University students’ engagement with generative AI-supported automated writing evaluation (AWE) feedback
Journal of Second Language Writing (2025) · doi:10.1016/j.jslw.2025.101203 · v1.0 |
2.8 (gemini-3.5-flash-lite) | — |
| 4520 | Screened Out |
Harnessing large language models to develop research-based learning assistants for formative feedback
Smart Learning Environments (2024) · doi:10.1186/s40561-024-00354-1 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 4521 | Screened Out |
A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: a case of ChatGPT
Educational Technology Research and Development (2024) · doi:10.1007/s11423-024-10366-w · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 4522 | In Buffer |
Zero-Shot Multimodal Question Answering for Assessment of Medical Student OSCE Physical Exam Videos
medRxiv (2024) · doi:10.1101/2024.06.05.24308467 · v1.0 |
4.7 (gemini-3.6-flash) | — |
| 4523 | Screened Out |
Development of a Virtual Patient Model for Kampo Medical Interview: New Approach for Enhancing Empathy and Understanding of Kampo Medicine Pathological Concepts
Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) (2024) · doi:10.1109/scisisis61014.2024.10759962 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 4524 | Screened Out |
Application of AI Communication Training Tools in Medical Undergraduate Education: Mixed Methods Feasibility Study Within a Primary Care Context
JMIR Medical Education (2025) · doi:10.2196/70766 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 4525 | Screened Out |
A large language model digital patient system enhances ophthalmology history taking skills
npj Digital Medicine (2025) · doi:10.1038/s41746-025-01841-6 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 4526 | Screened Out |
Enhancing Medical Interview Skills Through AI-Simulated Patient Interactions: Nonrandomized Controlled Trial
JMIR Medical Education (2024) · doi:10.2196/58753 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 4527 | No Fulltext |
A Language Model–Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study
JMIR Medical Education (2024) · doi:10.2196/59213 · v1.0 |
6.0 (gemini-3.1-flash-lite) | — |
| 4528 | Screened Out |
Reproducible Generative AI Evaluation for Healthcare: A Clinician-in-the-Loop Approach
medRxiv (2025) · doi:10.1101/2025.03.04.25323131 · v1.0 |
3.3 (gemini-3.1-flash-lite) | — |
| 4529 | Screened Out |
Examining the Performance of Artificial Intelligence in Scoring Students' Handwritten Responses to Open-Ended Items
TED EĞİTİM VE BİLİM (2025) · doi:10.15390/eb.2025.14119 · v1.0 |
4.7 (gemini-3.1-flash-lite) | — |
| 4530 | Screened Out |
Optimizing Implementation of Artificial‐Intelligence‐Based Automated Scoring: An Evidence Centered Design Approach for Designing Assessments for AI‐based Scoring
Journal of Educational Measurement (2022) · doi:10.1111/jedm.12332 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 4531 | No Fulltext |
Reliability of ChatGPT in automated essay scoring for dental undergraduate examinations
BMC Medical Education (2024) · doi:10.1186/s12909-024-05881-6 · v1.0 |
6.0 (gemini-3.1-flash-lite) | — |
| 4532 | Screened Out |
Students’ Perceptions of Generative Artificial Intelligence (GenAI) Use in Academic Writing in English as a Foreign Language
Education Sciences (2025) · doi:10.3390/educsci15050611 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 4533 | scored |
Human- versus Artificial Intelligence
Frontiers in Artificial Intelligence (2021) · doi:10.3389/frai.2021.622364 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4534 | scored |
Generative AI in Computer Science Education: Insights from Topic Modeling and Text Network Analysis
International Conference on Artificial Intelligence and Soft Computing (2025) · doi:10.1109/icaisc64594.2025.10959426 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4535 | scored |
The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork
Organization science (Providence, R.I.) (2026) · doi:10.1287/orsc.2025.20702 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4536 | Screened Out |
Evaluating the Accuracy and Educational Potential of Generative AI Models in Pharmacy Education: A Comparative Analysis of ChatGPT and Gemini Across Bloom’s Taxonomy
Pharmacy (2025) · doi:10.3390/pharmacy14010001 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 4537 | Screened Out |
Effects of generative artificial intelligence on cognitive effort and task performance: study protocol for a randomized controlled experiment among college students
Trials (2025) · doi:10.1186/s13063-025-08950-3 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 4538 | Screened Out |
Generative AI for ECG Interpretation Education: Impact on Nursing, Student Performance, and AI Model Accuracy.
Nurse Educator (2026) · doi:10.1097/nne.0000000000002178 · v1.0 |
3.3 (gemini-3.1-flash-lite) | — |
| 4539 | Screened Out |
DIALOGUE: A Generative AI-Based Pre–Post Simulation Study to Enhance Diagnostic Communication in Medical Students Through Virtual Type 2 Diabetes Scenarios
European Journal of Investigation in Health, Psychology and Education (2025) · doi:10.3390/ejihpe15080152 · v1.0 |
3.8 (gemini-3.1-flash-lite) | — |
| 4540 | Screened Out |
Investigating the Capabilities of Generative AI in Solving Data Structures, Algorithms, and Computability Problems
Technical Symposium on Computer Science Education (2025) · doi:10.1145/3641554.3701946 · v1.0 |
4.1 (gemini-3.1-flash-lite) | — |
| 4541 | scored |
Taming generative AI for interpreter education: using large language models in classroom-based assessment of English-Chinese consecutive interpreting
The Interpreter and Translator Trainer (2025) · doi:10.1080/1750399x.2025.2533606 · v1.0 |
5.3 (gemini-3.1-flash-lite) | — |
| 4542 | scored |
Evaluating the Use of Generative AI Travel Assistants in Smart Tourism through Student Feedback
Journal of Computers, Mechanical and Management (2025) · doi:10.57159/jcmm.4.5.25231 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4543 | Screened Out |
Blending Generative AI and Instructor-Led Learning: Empirical Insights on Student Motivation, Learning Experience, and Academic Performance in Higher Education
Education sciences (2025) · doi:10.3390/educsci15111480 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 4544 | Screened Out |
Exploring the Impact of Transparent Generative AI on Learning Outcomes Through a Human-Machine Trust Model
International Conference on Advanced Learning Technologies (2025) · doi:10.1109/icalt64023.2025.00072 · v1.0 |
3.0 (gemini-3.1-flash-lite) | — |
| 4545 | Screened Out |
Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support
British Journal of Educational Technology (2025) · doi:10.1111/bjet.13599 · v1.0 |
1.7 (gemini-3.1-flash-lite) | — |
| 4546 | Screened Out |
PENGEMBANGAN INSTRUMEN TEACHER PERCEPTION SCALE FOR FINANCIAL ACCOUNTABILITY (TPS-FA) MENGGUNAKAN MODEL RASCH PADA GURU SMK NEGERI DI KALBAR
Jurnal Visi Ilmu Pendidikan (2026) · doi:10.26418/jvip.v18i2.105437 · v1.0 |
3.7 (gemini-3.5-flash-lite) | — |
| 4547 | scored |
Anti-drifting Feature Selection via Deep Reinforcement Learning (Student Abstract)
Proceedings of the AAAI Conference on Artificial Intelligence (2023) · doi:10.1609/aaai.v37i13.27038 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4548 | scored |
Adaptive Hybrid Ensemble Method for Accelerate Adaptation of Concept Drift
Research Square (2023) · doi:10.21203/rs.3.rs-3101864/v1 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4549 | scored |
Sensitizing Rules for Change Point Detection in Phase I Analysis with Nonparametric Shewhart-type Control Charts
Portland International Conference on Management of Engineering and Technology (PICMET) (2023) · doi:10.23919/picmet59654.2023.10216915 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4550 | scored |
Trend assessment of rainfall, temperature and relative humidity using non-parametric tests in the national capital region, Delhi
MAUSAM (2023) · doi:10.54302/mausam.v74i3.4936 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |