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: 17746 →
Screen: 16723 →
Eligible: 2501 →
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 | 15818 | 2026-11-08 04:05 |
ref — reference lists of read works | 1901 | 2026-11-08 02:26 |
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 | 15818 | 2026-10-06 | 2026-11-08 |
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 | 8284 |
gemini-3.5-flash-lite | 7869 |
— | 636 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 186 |
gemini-3.8-flash | v2.1 | 110 |
gemini-3.7-flash | v2.1 | 102 |
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) | 16325 |
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): 14249 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 | 9201 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3849 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1199 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1369 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1077 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 31 | 2 (too few) | 2 | 100% |
| Screening model | Seed recall | AUC | Sens / Spec | Prec / NPV | F₁ / AC₁ | 👍 / 👎 (TP/FP/FN/TN) |
|---|---|---|---|---|---|---|
gemini-3.1-flash-lite |
50% (1/2), 95% CI 9–91% | 0.63 | 100% (12/12), 95% CI 76–100% / 16% (3/19), 95% CI 6–38% | 43% (12/28), 95% CI 27–61% / 100% (3/3), 95% CI 44–100% | 0.60 / 0.05 | 12 / 19 (12/16/0/3) |
gemini-3.5-flash-lite |
— | — | — / 100% (1/1), 95% CI 21–100% | — / 100% (1/1), 95% CI 21–100% | — / 1.00 | 0 / 1 (0/0/0/1) |
— |
— | — | — / — | — / — | — / — | 0 / 0 (0/0/0/0) |
To narrow intervals, judge works from lower score bands: /admin/taste includes 20% random picks. Metrics update automatically with each verdict.
Methodological Rigor & Study Designs (11, 18)
Methodological rigor M (0–10) is evaluated during full-text analysis. Study design distribution reflects verified empirical classifications.
Mean Rigor (M)
6.53 / 10
Analyzed papers: 663
Quality Stratification
🟢 High rigor (M ≥ 7.0): 328
🟡 Moderate rigor (4.0 ≤ M < 7.0): 316
🔴 Low rigor (M < 4.0): 19
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 256 |
other | 47 |
theoretical | 27 |
rct | 13 |
assessment_design | 177 |
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 (107)
#Psychometrics (62)
#Item Response Theory (51)
#academic integrity (49)
#Assessment Validity (41)
#assessment validity (40)
#item response theory (39)
#higher education (37)
#Automated Essay Scoring (35)
#automated essay scoring (32)
#Generative AI (29)
#Construct Validity (26)
#construct validity (26)
#IRT (24)
#LLM as rater (22)
#Large Language Models (21)
#generative AI (21)
#Academic Integrity (19)
#Higher Education (19)
#assessment redesign (18)
#Automated Scoring (17)
#LLM Evaluation (17)
#large language models (17)
#prompt engineering (17)
#GPT-4 (16)
#assessment_design (16)
#automated scoring (16)
#AI-resilient assessment (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)
#Automated Item Generation (11)
#LLMs as raters (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#inter-rater reliability (11)
#LLM-as-a-judge (10)
#LLMs as Test Takers (10)
#item_response_theory (10)
#rater drift (10)
#Computerized Adaptive Testing (9)
#DIF (9)
#Inter-Rater Reliability (9)
#Knowledge Tracing (9)
#LLM evaluation (9)
#Medical Education (9)
#automated_essay_scoring (9)
#concept drift (9)
#formative assessment (9)
#AI-resistant assessment (8)
#Assessment Design (8)
#Computer Science Education (8)
#Differential Item Functioning (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 8401 | Screened Out |
MACHINE LEARNING-BASED DETECTION OF AI-GENERATED CONTENT IN STUDENTS’ WRITTEN WORKS
NEWS OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN (2026) · doi:10.32014/2026.2518-1726.470 · v1.0 |
4.3 (gemini-3.1-flash-lite) | — |
| 8402 | No Fulltext |
SafeMachine: An evidence-centric architecture for traceable and context-aware machine safety assessment
Advanced Engineering Informatics (2026) · doi:10.1016/j.aei.2026.105321 · v1.0 |
6.7 (gemini-3.1-flash-lite) | — |
| 8403 | Not Article |
Dataset
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23087022 · v1.0 |
- | — |
| 8404 | scored |
From Population Averages to Individual Realities: A Debate on Using Intensive Longitudinal Methods in Mental Health Nursing Research
Journal of Psychiatric and Mental Health Nursing (2026) · doi:10.1111/jpm.70210 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8405 | scored |
AI in Higher Mathematics Education
Advances in computational intelligence and robotics book series (2026) · doi:10.4018/979-8-3373-8735-2.ch001 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8406 | scored |
Creativity, Design Thinking and Innovation in Advancing STEAM Education through AI and Digital Technologies
Advances in computational intelligence and robotics book series (2026) · doi:10.4018/979-8-3373-8735-2.ch006 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8407 | Not Article |
PREreview of "Scoring Rubric to Assess Ethical Issues in Clinical Artificial Intelligence Tools"
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23080513 · v1.0 |
- | — |
| 8408 | Not Article |
Attention Graph–Hypergraph and Fuzzy Fusion for Explainable Hallucination Risk Assessment in Medical Large Language Models
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23083049 · v1.0 |
- | — |
| 8409 | scored |
Transformation of Social Studies Pedagogy through Artificial Intelligence –Driving Technologies and Students’ Motivation in Calabar Metropolis of Cross River State, Nigeria
WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY (2026) · doi:10.56201/wjimt.v9.no6.2025.pg251.261 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8410 | scored |
Enhancing Second Language Proficiency through Jigsaw Cooperative Learning among Technical Learners at Tertiary Level
Journal of Daoist studies (2026) · doi:10.5281/zenodo.23078853 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8411 | scored |
The Conversation We’re Not Having about AI in Peer Review
ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!) (2026) · doi:10.1145/3840586.3843746 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8412 | scored |
Editorial: Integrating oral health into public health: bridging gaps to reduce health disparities in the US
Frontiers in Public Health (2026) · doi:10.3389/fpubh.2026.2001019 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8413 | scored |
Integrated Seismic Attribute Analysis and High-Resolution Inversion for Thin Sandstone Reservoir Characterization
Preprints.org (2026) · doi:10.20944/preprints202610.0053.v1 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8414 | Included |
Algorithmic drift in generative adaptive learning
arXiv (2026) · doi:10.48550/arxiv.2610.31041 · v1.0 |
7.3 (gemini-3.7-flash) | — |
| 8415 | No Fulltext |
My AI Wants to Know if This Will Be on the Exam: Testing OpenAI’s Codex on CS2 Programming Exercises
Australasian Computing Education Conference (2023) · doi:10.1145/3576123.3576134 · v1.0 |
5.8 (gemini-3.5-flash-lite) | — |
| 8416 | scored |
The Rise of Generative Artificial Intelligence and Its Impact on Education: The Promises and Perils
Computer (2023) · doi:10.1109/mc.2023.3253292 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 8417 | In Buffer |
Mathematical Capabilities of ChatGPT
neural information processing systems (2023) · doi:10.52202/075280-1205 · v1.0 |
4.0 (gemini-3.8-flash) | — |
| 8418 | Screened Out |
Students’ Perception on Plagiarism and Usage of Turnitin Anti-Plagiarism Software: The Role of the Library
Journal of Library Administration (2022) · doi:10.1080/01930826.2022.2146445 · v1.0 |
2.2 (gemini-3.5-flash-lite) | — |
| 8419 | scored |
A systematic review of research on cheating in online exams from 2010 to 2021
Education and Information Technologies (2022) · doi:10.1007/s10639-022-10927-7 · v1.0 |
0.0 (Flash Lite) | — |
| 8420 | scored |
Spanish Version of the Netherlands Empowerment List: Adaptation, Psychometric Validation, and Gender Differential Item Functioning Analysis
International Journal of Mental Health and Addiction (2024) · doi:10.1007/s11469-024-01321-0 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 8421 | scored |
The Patient Activation Measure-13 (PAM-13) in an oncology patient population: psychometric properties and dimensionality evaluation
Health and Quality of Life Outcomes (2024) · doi:10.1186/s12955-024-02255-w · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 8422 | scored |
The Experience of Embodiment Scale: An examination of its psychometric properties in women from the Republic of Cyprus
PLoS ONE (2024) · doi:10.1371/journal.pone.0303268 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 8423 | Screened Out |
Gender related differential item functioning on basic general mathematics examination of Kano state college of education and preliminary studies, Kano, Nigeria
Journal of Educational Research in Developing Areas (2024) · doi:10.47434/jereda.5.2.2024.162 · v1.0 |
1.6 (gemini-3.5-flash-lite) | — |
| 8424 | Screened Out |
Extending the PROMIS item bank “ability to participate in social roles and activities”: a psychometric evaluation using IRT
Quality of Life Research (2024) · doi:10.1007/s11136-024-03666-4 · v1.0 |
2.3 (gemini-3.5-flash-lite) | — |
| 8425 | scored |
A Systematic Review of the Reliability and Validity of the Patient Activation Measure Tool
Healthcare (2024) · doi:10.3390/healthcare12111079 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 8426 | scored |
Using moderated nonlinear factor models to adjust for differential item functioning in the Student-Teacher Relationship Scale from kindergarten to Grade 6
Journal of School Psychology (2024) · doi:10.1016/j.jsp.2024.101324 · v1.0 |
0.0 (Flash Lite) | — |
| 8427 | scored |
Psychometric evaluation of the pictorial scale of perceived movement skill competence in Chinese children: An item response theory analysis
Child Care Health and Development (2024) · doi:10.1111/cch.13275 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 8428 | Screened Out |
Optimizing Item Construction in Diagnostic Mathematics Test
TEM Journal (2024) · doi:10.18421/tem132-25 · v1.0 |
2.3 (gemini-3.5-flash-lite) | — |
| 8429 | Screened Out |
Rasch analysis of the self efficacy (SE-12) questionnaire measuring clinical communication skills
PEC Innovation (2024) · doi:10.1016/j.pecinn.2024.100296 · v1.0 |
2.2 (gemini-3.1-flash-lite) | — |
| 8430 | Screened Out |
Initial evaluation of measurement properties of the Work Environment Impact Questionnaire (WEIQ) - using Rasch analysis
Health and Quality of Life Outcomes (2024) · doi:10.1186/s12955-024-02260-z · v1.0 |
2.2 (gemini-3.1-flash-lite) | — |
| 8431 | scored |
Remarks From the Editor-in-Chief
Psychometrika (2024) · doi:10.1007/s11336-024-09981-6 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8432 | scored |
Clinicopathological Pattern of Renal Biopsies in Children with Nephrotic Syndrome
Clinical Medicine & Research (2024) · doi:10.3121/cmr.2024.1917 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8433 | scored |
PCR87 Supporting Diversity and Inclusion Through the Evaluation of Differential Item Functioning (DIF) By Race and Ethnicity in the Asthma Control Questionnaire and Asthma Quality of Life Questionnaire Using Dupilumab Phase 3 Data
Value in Health (2024) · doi:10.1016/j.jval.2024.03.1966 · v1.0 |
0.0 (Flash Lite) | — |
| 8434 | Screened Out |
Equivalence of Alcohol Use Disorder Symptom Assessments in Routine Clinical Care When Completed Remotely via Online Patient Portals Versus In Clinic via Paper Questionnaires: Psychometric Evaluation
Journal of Medical Internet Research (2024) · doi:10.2196/52101 · v1.0 |
4.4 (gemini-3.1-flash-lite) | — |
| 8435 | Screened Out |
Rasch Analysis of the Mobile Version of the eHealth Literacy Scale (m-eHEALS) in Cardiovascular Disease Patients (Preprint)
arXiv/Crossref (2024) · doi:10.2196/preprints.63128 · v1.0 |
2.0 (gemini-3.1-flash-lite) | — |
| 8436 | Screened Out |
Measurement properties of the Swedish version of the anterior cruciate ligament return to sport after injury scale (ACL‐RSI): A Rasch analysis
Journal of Experimental Orthopaedics (2024) · doi:10.1002/jeo2.12059 · v1.0 |
2.5 (gemini-3.1-flash-lite) | — |
| 8437 | Screened Out |
Psychometric assessment of the Runyankole-translated Marlowe-Crowne Social Desirability Scale among persons with HIV in Uganda
BMC Public Health (2024) · doi:10.1186/s12889-024-18886-z · v1.0 |
2.2 (gemini-3.1-flash-lite) | — |
| 8438 | Screened Out |
Reexamining gender differences and the transdiagnostic boundaries of various conceptualizations of perseverative cognition.
Psychological Assessment (2024) · doi:10.1037/pas0001326 · v1.0 |
2.5 (gemini-3.1-flash-lite) | — |
| 8439 | scored |
Comparison of Patient Health Questionnaire-9, Edinburgh Postnatal Depression Scale and Hospital Anxiety and Depression – Depression subscale scores by administration mode: An individual participant data differential item functioning meta-analysis
Journal of Affective Disorders (2024) · doi:10.1016/j.jad.2024.06.033 · v1.0 |
0.0 (Flash Lite) | — |
| 8440 | scored |
Self-optimizing Teacher and Auto-matching Student Framework for Change-point Representation Learning in Time Series Forecasting
ACM Transactions on Intelligent Systems and Technology (2025) · doi:10.1145/3718091 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8441 | scored |
Adaptive Data Mining Approaches for Efficient Predictive Analytics in Healthcare
International Journal for Research in Applied Science and Engineering Technology (2025) · doi:10.22214/ijraset.2025.68979 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8442 | scored |
An Ensemble Learning Model Based on Three-Way Decision for Concept Drift Adaptation
Tsinghua Science & Technology (2025) · doi:10.26599/tst.2024.9010085 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8443 | scored |
Prognostic Value of the Hyperinflammatory Phenotype Declines Over the Course of Critical Illness
American Journal of Respiratory and Critical Care Medicine (2025) · doi:10.1164/ajrccm.2025.211.abstracts.a3136 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 8444 | Screened Out |
GENERATIVE ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: A SYSTEMATIC REVIEW OF ITS IMPACT ON STUDENT LEARNING, CRITICAL THINKING, AND ACADEMIC INTEGRITY
Journal of Social Research Development (2026) · doi:10.59075/jsrd.v7i6.502 · v1.0 |
2.3 (gemini-3.1-flash-lite) | — |
| 8445 | Not Article |
Replication materials for 'Do respondents’ commitments improve data quality in opt-in online surveys?'
Harvard Dataverse (2026) · doi:10.7910/dvn/uonzw7 · v1.0 |
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| 8446 | Screened Out |
Methodological Integration of Artificial Intelligence in University Teaching for the Development of Professional Competencies
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21799003 · v1.0 |
2.5 (gemini-3.1-flash-lite) | — |
| 8447 | Screened Out |
Generative Artificial Intelligence and Higher Education's Epistemic Shift: Rethinking Academic Integrity as an Ecosystem
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21791892 · v1.0 |
2.0 (gemini-3.1-flash-lite) | — |
| 8448 | Screened Out |
Sovereignty, Collaboration, and Capability: Three Dilemmas and a Transformation Framework for Sci-Tech Journal Editors in the Digital-Intelligence Era
Journal of Scholarly Communication (2026) · doi:10.62160/jsc77 · v1.0 |
1.5 (gemini-3.1-flash-lite) | — |
| 8449 | Screened Out |
Artificial Intelligence in English Second-Language Acquisition: Roles, Challenges, and Optimization Pathways
Communications in Humanities Research (2026) · doi:10.54254/2753-7064/2026.35895 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 8450 | scored |
Learning Motivation, Opportunity, Religiosity, with Artificial Intelligence as a Moderating Variable on Academic Cheating Behavior
arXiv/Crossref (2026) · doi:10.21070/ups.11476 · v1.0 |
0.0 (Flash Lite) | — |