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: 17447 →
Screen: 16339 →
Eligible: 2438 →
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 | 15566 | 2026-11-07 14:23 |
ref — reference lists of read works | 1854 | 2026-11-07 14:28 |
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 | 15566 | 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 | 8091 |
gemini-3.5-flash-lite | 7695 |
— | 619 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 178 |
gemini-3.8-flash | v2.1 | 108 |
gemini-3.7-flash | v2.1 | 100 |
Scoring rubric v2.3 (0–10 anchors)
The model scores 0–10 per criterion; the anchors below are embedded in the prompt. Formula and weights are in the footnote above. Changing anchors or weights without a new version is blocked by a hash check.
| Criterion | Weight | 2 | 5 | 8 |
|---|---|---|---|---|
| Topic relevance (R) | × min(1, R/7.0) | No link to education or assessment: image, audio or model watermarking, model IP protection, general NLP benchmarks, AI in industry. | Adjacent: generative AI in education without an assessment focus; academic integrity with no evidence on assessment outcomes; detectors of AI-generated text or of student AI use; general psychometrics (adaptive testing, test assembly, IRT) with no drift, validity or AI angle. | Core: validity of assessment under generative AI (what grades and scores mean when AI is available); how grades shift under unsupervised conditions or cheating (proctored vs unproctored exams, grade inflation); AI-resistant or AI-resilient assessment design; quantitative methods that detect or model distorted assessment or learning data (DIF between humans and LLMs, item parameter or rater drift, item compromise/preknowledge, change-point or concept-drift detection, LLMs as test takers or raters). |
| Methodological rigor (M) | 0.32 | Vague evaluation, convenience sample of a few dozen, self-report only, no control group or baseline. | Clear dataset or sample (hundreds), standard metrics or validated instruments, comparison with baselines or a control group. | Randomized/controlled or preregistered, large or multi-site/multilingual data, adversarial or robustness checks, effect sizes with uncertainty. |
| Engineering countermeasures (E) | 0.25 | No method or countermeasure: describes impact, attitudes or policy only. | A concrete method or countermeasure with an evaluation: statistical test or model, detector, scoring procedure, or an assessment design tried on real cohorts. | A rigorous, reusable method: formal model with guarantees or error control, open code, validated on both real and simulated data. |
| Consensus challenge (C) | 0.43 | Reconfirms standard wisdom. | Nuances the consensus (e.g. detectors fail under mild paraphrasing; AI use does not harm an outcome it is assumed to harm). | Directly refutes high-profile claims with rigorous evidence. |
| Novelty (N) | 0.00 | Replicates well-known findings (another survey of student attitudes to ChatGPT, another test of one detector on a handful of prompts). | New dataset, benchmark, population or setting, or a previously untried technique. | Fundamentally new attack or defence, a theoretical/impossibility result, or a finding that changes how the field frames the problem. |
The model interpolates between anchors. The deep-analysis score is computed by code with the same formula from criteria_breakdown.
| Rubric version | Papers scored |
|---|---|
v2.0 | 261 |
v2.1 | 183 |
v2.2 | 20 |
v2.3 (current) | 15941 |
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): 13928 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 | 9007 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3755 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1166 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1332 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1052 | 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)
6.54 / 10
Analyzed papers: 651
Quality Stratification
🟢 High rigor (M ≥ 7.0): 324
🟡 Moderate rigor (4.0 ≤ M < 7.0): 308
🔴 Low rigor (M < 4.0): 19
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 252 |
other | 45 |
theoretical | 27 |
rct | 13 |
assessment_design | 171 |
natural_experiment | 5 |
quasi_experiment | 56 |
detector_evaluation | 43 |
survey | 7 |
review | 20 |
experimental | 1 |
qualitative | 11 |
PRISMA 2020 27-Item Compliance Checklist
Full audit of compliance with PRISMA 2020 checklist (Page et al., 2021) for automated systematic reviews.
| № | Section / Item | Checklist Description | Status | Implementation |
|---|---|---|---|---|
| 1 | Title | Identify as living systematic review | ✓ Впроваджено | Page title, report metadata |
| 2 | Abstract | Structured summary (background, objectives, methods, results) | ✓ Впроваджено | #prisma-abstract block above |
| 3 | Rationale | Rationale in context of existing knowledge | ✓ Впроваджено | Topic description and rationale |
| 4 | Objectives | Explicit objectives using PICO / PECO | ✓ Впроваджено | #pico-framework table above |
| 5 | Eligibility criteria | Inclusion/exclusion criteria, language, dates | ✓ Впроваджено | Thresholds R, Score, Open Access |
| 6 | Information sources | Databases, registers, search dates | ✓ Впроваджено | arXiv, Crossref, OpenAlex, Semantic Scholar, ERIC, citations |
| 7 | Search strategy | Full search strategy, boolean filters, versions | ✓ Впроваджено | search_version, term groups, API limits |
| 8 | Selection process | Selection process, automation, independent screening | ✓ Впроваджено | LLM screening rotation + editorial taste loop |
| 9 | Data collection | Data collection from full-text reports | ✓ Впроваджено | PDF → MD parser, deep prompt v2.1 |
| 10a-b | Data items | List of outcomes and study characteristics | ✓ Впроваджено | Summary, sample, design, claims, countermeasures |
| 11 | Study risk of bias | Assessment of study methodological quality | ✓ Впроваджено | Rigor criterion M (0–10), quality distribution |
| 12 | Effect measures | Effect measures for outcomes | — Обґрунтовано N/A | Qualitative synthesis without meta-analytic pooled effects |
| 13a-f | Synthesis methods | Synthesis criteria and grouping methods | ✓ Впроваджено | Tag-based clustering, comparative study cards |
| 14 | Reporting bias | Assessment of reporting/publication bias | ✓ Впроваджено | Analysis of paywall exclusions & open access skew |
| 15 | Certainty assessment | Certainty assessment of body of evidence | ✓ Впроваджено | Automated quote grounding against full-text Markdown |
| 16 | Study selection | Selection results (flow diagram, counts) | ✓ Впроваджено | 100% stage conservation closure verified |
| 17 | Study characteristics | Characteristics of included studies table | ✓ Впроваджено | Interactive table with DOI, venue, year, tags |
| 18 | Risk of bias in studies | Risk of bias findings across studies | ✓ Впроваджено | Methodological rigor M & study designs table |
| 19 | Results of individual studies | Results for each individual study | ✓ Впроваджено | Public paper pages /doi/{doi} with full analysis |
| 20a-d | Results of syntheses | Results of syntheses & sub-groups | ✓ Впроваджено | Daily living editions, RSS feed, tag filters |
| 21 | Reporting biases | Results of reporting biases assessment | ✓ Впроваджено | Paywall n=347, retracted n=0 |
| 22 | Certainty of evidence | Summary of evidence certainty | ✓ Впроваджено | Gwet's AC₁, Cohen's κ, stratified recall, ROC AUC |
| 23a-d | Discussion | Discussion, limitations, implications | ✓ Впроваджено | Limitations section (23b, 23c) in methods |
| 24a-c | Registration & protocol | Registration, protocol availability, amendments | ✓ Впроваджено | Public repo, version history of search & rubric |
| 25 | Support | Financial & non-financial support | ✓ Впроваджено | Free API tiers declared in methods |
| 26 | Competing interests | Competing interests statement | ✓ Впроваджено | Declared in methods section |
| 27 | Data & code availability | Availability of data, analytical code, materials | ✓ Впроваджено | Export to CSV / JSON buttons, open-source code |
Limitations (23b, 23c)
- Open access only: paywalled papers never reach the synthesis, so the sample skews to open and preprint work.
- Screening ranks by quality and novelty rather than pure eligibility, so the sample skews towards counter-consensus work. Validity is measured against one expert (see above); a single LLM reviewer, model bias and version differences remain possible.
- The lexical filter and result caps can miss works with other terminology; sources skew English-language.
- Records scored under an older rubric version are not rescored automatically (see version table).
- Free Gemini quota: daily limits slow analysis; some selected works wait in the queue.
Registration, protocol, amendments, support, data (24–27)
- Registration: not registered. The protocol is the configuration (topic, queries, rubric, thresholds) in the project repository; it is reproduced in this block.
- Amendments: search versions (search_version) and rubric versions (rubric_version), tables above.
- Funding: none declared, free API tiers are used. Competing interests: none declared.
- Data: analyses and metadata are on article pages, RSS and sitemap; full texts are private (copyright) and not published.
Filter by Tags
Select one or more tags to refine the flow numbers and article corpus.
#psychometrics (106)
#Psychometrics (62)
#Item Response Theory (50)
#academic integrity (48)
#Assessment Validity (39)
#assessment validity (39)
#item response theory (39)
#higher education (37)
#automated essay scoring (32)
#Automated Essay Scoring (31)
#Generative AI (29)
#construct validity (26)
#Construct Validity (24)
#IRT (24)
#LLM as rater (22)
#generative AI (20)
#Academic Integrity (19)
#Higher Education (19)
#Large Language Models (18)
#Automated Scoring (17)
#LLM Evaluation (17)
#assessment redesign (17)
#large language models (17)
#assessment_design (16)
#prompt engineering (16)
#automated scoring (15)
#AI-resilient assessment (14)
#GPT-4 (14)
#LLM as Rater (14)
#validity (14)
#Assessment Redesign (13)
#ChatGPT (13)
#assessment design (13)
#automated assessment (13)
#differential item functioning (13)
#Prompt Engineering (12)
#academic_integrity (12)
#LLMs as raters (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#inter-rater reliability (11)
#Automated Item Generation (10)
#LLM-as-a-judge (10)
#LLMs as Test Takers (10)
#item_response_theory (10)
#Computerized Adaptive Testing (9)
#DIF (9)
#Inter-Rater Reliability (9)
#LLM evaluation (9)
#automated_essay_scoring (9)
#concept drift (9)
#formative assessment (9)
#rater drift (9)
#AI-resistant assessment (8)
#Assessment Design (8)
#Computer Science Education (8)
#Differential Item Functioning (8)
#Knowledge Tracing (8)
#Validity (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 5801 | scored |
Urdu Translation and Validation of General Attitude towards Artificial Intelligence Scale
Journal for social science archives (2026) · doi:10.59075/jssa.v4i1.651 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5802 | scored |
Giving the Teacher AI Self‑Efficacy Measure a Psychometric Tune‑Up: Revalidating in the Pakistani Context
Research Journal for Social Affairs (2026) · doi:10.71317/rjsa.004.03(s4).0991 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5803 | scored |
Understanding and managing patient resistance to AI chatbots adoption in healthcare: A comprehensive model of perceived functional and organizational barriers
Human Factors in Healthcare (2026) · doi:10.1016/j.hfh.2026.100132 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5804 | scored |
Red Teaming Generative Artificial Intelligence in Nursing Education: A Risk-Based Audit
Journal of Nursing Regulation (2026) · doi:10.1016/j.jnr.2026.09.002 · v1.0 |
0.0 (Flash Lite) | — |
| 5805 | scored |
A Training Program for Improving Teachers’ Knowledge and Attitudes Toward AI Apps Supporting Speech and Language Skills of Deaf and Hard-Of-Hearing Students
SAGE Open (2026) · doi:10.1177/21582440261496715 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5806 | In Buffer |
Checkpointed Evaluation in AI-Assisted Integrated Reading-to-Write Revision: Conscious-Access Routines and No-AI Transfer
Language Teaching Research Quarterly (2026) · doi:10.32038/ltrq.2026.56.05 · v1.0 |
6.4 (gemini-3.7-flash) | — |
| 5807 | scored |
RETHINKING AUTHORSHIP: A DOCTRINAL ANALYSIS OF COPYRIGHT PROTECTION FOR ARTIFICIAL INTELLIGENCE-GENERATED WORKS
LawFoyer International Journal of Doctrinal Legal Research. (2026) · doi:10.70183/lijdlr.2026.v04.336 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5808 | scored |
A dynamic patching LLM framework for adaptive yield trend prediction
Digital Chemical Engineering (2026) · doi:10.1016/j.dche.2026.100349 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5809 | No Fulltext |
From AI-Generated Answers to Collaborative Inquiry: How Socratic Questioning Scaffolds Reorganize Peer Dialogic Feedback in GenAI-Supported Learning
SAGE Open (2026) · doi:10.1177/21582440261494631 · v1.0 |
5.9 (gemini-3.1-flash-lite) | — |
| 5810 | scored |
ARTIFICIAL INTELLIGENCE AND ALGORITHMIC BIAS IN LEGAL DECISION-MAKING: A COMPARATIVE ANALYSIS OF NIGERIA, INDIA AND SELECTED JURISDICTIONS
LawFoyer International Journal of Doctrinal Legal Research. (2026) · doi:10.70183/lijdlr.2026.v04.340 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5811 | scored |
MALICIOUS USE OF DEEPFAKES IN INDIA: LEGAL, TECHNOLOGICAL AND SOCIO-ECONOMIC CHALLENGES AND THE NEED FOR AN EFFECTIVE REGULATORY FRAMEWORK
LawFoyer International Journal of Doctrinal Legal Research. (2026) · doi:10.70183/lijdlr.2026.v04.348 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5812 | scored |
Patterns and Predictors of Misclassifications by Large Language Models in Multimodal Glaucoma Diagnosis
Ophthalmology Science (2026) · doi:10.1016/j.xops.2026.101434 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5813 | scored |
Barriers to artificial intelligence applications in ophthalmology: beyond diagnostic accuracy toward patient outcomes, workflow integration, ethics, and trust
Asia-Pacific Journal of Ophthalmology (2026) · doi:10.1016/j.apjo.2026.100382 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5814 | Screened Out |
Assessment of Perceptions, Trust, and Adoption of Clinical Artificial Intelligence Tools Among Physicians and Dentists, Riyadh, Saudi Arabia
Journal of Multidisciplinary Healthcare (2026) · doi:10.2147/jmdh.s635220 · v1.0 |
1.7 (gemini-3.1-flash-lite) | — |
| 5815 | scored |
Use of Artificial Intelligence in Dermatology Residency Education: A Scoping Review
JAAD reviews. (2026) · doi:10.1016/j.jdrv.2026.09.013 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5816 | scored |
Hardware implementation of AI-based fuzzy logic for real-time sign language interpreter robotic control using FPGA
Journal of Electrical Engineering (2026) · doi:10.2478/jee-2026-0049 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5817 | Screened Out |
Perspectives of Indonesian dental practitioners on application of artificial intelligence in dentistry
IAES International Journal of Artificial Intelligence (2026) · doi:10.11591/ijai.v15.i5.pp3991-4000 · v1.0 |
1.7 (gemini-3.1-flash-lite) | — |
| 5818 | scored |
An efficient computational framework for abstracting program slices from Java code
IAES International Journal of Artificial Intelligence (2026) · doi:10.11591/ijai.v15.i5.pp4969-4979 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5819 | scored |
Different Speeds, Different Strategies: Mapping AI Adoption Across Career Areas to Prioritize Workforce Preparation
Human capital leadership. (2026) · doi:10.70175/hclreview.2020.39.3.2 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5820 | Screened Out |
Generative Artificial Intelligence in Education: Conceptual Dimensions, Trends, and Research Directions
SAGE Open (2026) · doi:10.1177/21582440261492792 · v1.0 |
1.7 (gemini-3.1-flash-lite) | — |
| 5821 | scored |
“Snow Globe”: a wargaming system based on large language models
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.3969/j.issn.1673-3819.2026.05.016 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5822 | Screened Out |
Application of adaptive artificial intelligence for personalizing learning paths in e-learning
IAES International Journal of Artificial Intelligence (2026) · doi:10.11591/ijai.v15.i5.pp4992-5000 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 5823 | Screened Out |
Aligning AI and Human Feedback in EFL Writing: A Mixed-Methods Comparison of ChatGPT, Peer, and Teacher Feedback
SAGE Open (2026) · doi:10.1177/21582440261497008 · v1.0 |
4.3 (gemini-3.1-flash-lite) | — |
| 5824 | Screened Out |
Automated government document analysis and classification using robotic process automation-ChatGPT model
IAES International Journal of Artificial Intelligence (2026) · doi:10.11591/ijai.v15.i5.pp4862-4877 · v1.0 |
3.0 (gemini-3.1-flash-lite) | — |
| 5825 | scored |
Assessing corn quality classification with deep-learning module
IAES International Journal of Artificial Intelligence (2026) · doi:10.11591/ijai.v15.i5.pp4096-4104 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5826 | Screened Out |
Learner Resources and Grammar Achievement in AI-Assisted EFL Learning: A Motivation-Mediation Model in Chinese Private Higher Education
SAGE Open (2026) · doi:10.1177/21582440261496363 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 5827 | scored |
ETHICAL ASPECTS OF IMPLEMENTING ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN A CLASSICAL UNIVERSITY: ANALYSIS OF STUDENT AUDIENCE ATTITUDE
Lifelong education the XXI century (2025) · doi:10.15393/j5.art.2025.10585 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5828 | scored |
Introduction of Academic Honesty as a Necessary Prerequisite and an Important Component of Quality Education for Future Economists
Revista Romaneasca pentru Educatie Multidimensionala (2021) · doi:10.18662/rrem/13.1/361 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5829 | scored |
THE USE OF TEXT NEURAL NETWORKS AND ARTIFICIAL INTELLIGENCE IN STUDENTS' ACADEMIC PAPERS
Problems of Modern Education (Problemy Sovremennogo Obrazovaniya) (2023) · doi:10.31862/2218-8711-2023-5-206-216 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5830 | scored |
Artificial Intelligence in Promoting Teaching and Learning Transformation in Schools
International journal of innovation, creativity and change (2021) · doi:10.53333/ijicc2013/15369 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5831 | scored |
Organizational digital transformation: from evolution to future trends
Digital Transformation and Society (2023) · doi:10.1108/dts-08-2023-0061 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5832 | scored |
Quality of life in multiple scenarios: The impact of work mode and social contact quantity
Frontiers in Psychology (2023) · doi:10.3389/fpsyg.2023.1018415 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5833 | scored |
Differential Item Functioning of Verbal Ability Test in the Gulf Multiple Mental Abilities Scale by Mental-Haenszel and Likelihood Ratio Test
International Journal of Academic Research in Business and Social Sciences (2023) · doi:10.6007/ijarbss/v13-i1/16162 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5834 | Screened Out |
Comparing Robust Linking and Regularized Estimation for Linking Two Groups in the 1PL and 2PL Models in the Presence of Sparse Uniform Differential Item Functioning
Stats (2023) · doi:10.3390/stats6010012 · v1.0 |
4.1 (gemini-3.5-flash-lite) | — |
| 5835 | scored |
Needs evaluation questionnaire for liver disease: a novel assessment of unmet needs in patients with chronic liver disease
Hepatology Communications (2023) · doi:10.1097/hc9.0000000000000007 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5836 | scored |
The Gender Self-Report: A multidimensional gender characterization tool for gender-diverse and cisgender youth and adults.
American Psychologist (2023) · doi:10.1037/amp0001117 · v1.0 |
5.2 (gemini-3.1-flash-lite) | — |
| 5837 | Not Article |
Decision letter for "Evaluation of psychometric properties of the Dental Anxiety Inventory (DAI‐36) questionnaire using iterative hybrid ordinal logistic: Differential item functioning (DIF)"
arXiv/Crossref (2023) · doi:10.1002/brb3.3129/v1/decision1 · v1.0 |
- | — |
| 5838 | scored |
COMPARISON OF PARAMETER LOGISTIC MODELS OF RAJU DIFFERENTIAL ITEM FUNCTIONING ITEM RESPONSE BASED
The African Journal of Behavioural and Scale Development Research (2023) · doi:10.58579/ajb-sdr/5.1.2023.70 · v1.0 |
5.0 (gemini-3.1-flash-lite) | — |
| 5839 | Screened Out |
A psychometric evaluation of Chinese chronic hepatitis B virus infection-related stigma scale using classical test theory and item response theory
Frontiers in Psychology (2023) · doi:10.3389/fpsyg.2023.1035071 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 5840 | Screened Out |
A Rasch analysis of the High Potential Trait Indicator: A South African sample
African Journal of Psychological Assessment (2023) · doi:10.4102/ajopa.v5i0.115 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 5841 | Screened Out |
An Evaluation of Healthy Eating Scale for Patients with Pre-Diabetes Using Rasch Analysis
Applied Sciences (2023) · doi:10.3390/app13042050 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 5842 | Screened Out |
Development and Rasch Analysis of the 18-Item Health Resilience Profile (HRP)
Nutrients (2023) · doi:10.3390/nu15040807 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 5843 | scored |
Evidence of item bias in a national flourishing measure for autistic youth
Autism Research (2023) · doi:10.1002/aur.2900 · v1.0 |
5.2 (gemini-3.1-flash-lite) | — |
| 5844 | Not Article |
Author response for "Evaluation of psychometric properties of the Dental Anxiety Inventory (DAI‐36) questionnaire using iterative hybrid ordinal logistic: Differential item functioning (DIF)"
arXiv/Crossref (2023) · doi:10.1002/brb3.3129/v2/response1 · v1.0 |
- | — |
| 5845 | No Fulltext |
Three-Step Latent Class Analysis with Inverse Propensity Weighting in the Presence of Differential Item Functioning
Structural Equation Modeling A Multidisciplinary Journal (2023) · doi:10.1080/10705511.2022.2161384 · v1.0 |
6.0 (gemini-3.1-flash-lite) | — |
| 5846 | Screened Out |
Physical body experiences questionnaire simplified for active aging (PBE-QAG): Rasch validation
PLoS ONE (2023) · doi:10.1371/journal.pone.0280198 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 5847 | Screened Out |
The development and validation of a new resilience inventory based on inner strength
Scientific Reports (2023) · doi:10.1038/s41598-023-29848-7 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 5848 | Screened Out |
Rasch Calibration of the International Knee Documentation Committee Subjective Knee Form
Journal of Sport Rehabilitation (2023) · doi:10.1123/jsr.2022-0221 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 5849 | Screened Out |
Cross-cultural validity and psychometric properties of the International Trauma Questionnaire in a clinical refugee sample
European Journal of Psychotraumatology (2023) · doi:10.1080/20008066.2023.2172256 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 5850 | Not Article |
Review 1: Validating the Behavioral Defining Issues Test across Different Genders, Political and Religious Affiliations
arXiv/Crossref (2023) · doi:10.1017/exp.2023.6.pr1 · v1.0 |
- | — |