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: 19345 →
Screen: 18230 →
Eligible: 2680 →
Included: 96
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 = 96 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 | 17285 | 2026-11-11 18:25 |
ref — reference lists of read works | 2033 | 2026-11-11 14:25 |
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 | 17285 | 2026-10-06 | 2026-11-11 |
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 | 9066 |
gemini-3.5-flash-lite | 8522 |
— | 708 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 235 |
gemini-3.8-flash | v2.1 | 116 |
gemini-3.7-flash | v2.1 | 108 |
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) | 17832 |
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): 15577 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 | 10063 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 4203 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1311 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1475 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1145 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 36 | 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)
6.49 / 10
Analyzed papers: 724
Quality Stratification
🟢 High rigor (M ≥ 7.0): 344
🟡 Moderate rigor (4.0 ≤ M < 7.0): 359
🔴 Low rigor (M < 4.0): 21
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 276 |
other | 54 |
theoretical | 27 |
rct | 16 |
assessment_design | 196 |
natural_experiment | 5 |
quasi_experiment | 61 |
review | 22 |
detector_evaluation | 47 |
survey | 7 |
experimental | 1 |
qualitative | 12 |
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=366, 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 (113)
#Psychometrics (65)
#Item Response Theory (55)
#academic integrity (51)
#assessment validity (50)
#Assessment Validity (49)
#item response theory (45)
#higher education (42)
#Automated Essay Scoring (39)
#automated essay scoring (38)
#Generative AI (30)
#Construct Validity (27)
#construct validity (27)
#LLM as rater (25)
#Higher Education (24)
#IRT (24)
#Large Language Models (23)
#large language models (22)
#GPT-4 (21)
#LLM Evaluation (21)
#generative AI (21)
#Academic Integrity (19)
#Automated Scoring (19)
#assessment redesign (19)
#prompt engineering (19)
#ChatGPT (17)
#automated scoring (17)
#LLM as Rater (16)
#Prompt Engineering (16)
#assessment design (16)
#assessment_design (16)
#Assessment Redesign (15)
#AI-resilient assessment (14)
#validity (14)
#LLMs as raters (13)
#automated assessment (13)
#differential item functioning (13)
#Computer Science Education (12)
#LLM-as-a-judge (12)
#academic_integrity (12)
#benchmarking (12)
#inter-rater reliability (12)
#Automated Item Generation (11)
#LLMs as Test Takers (11)
#assessment-validity (11)
#assessment_validity (11)
#formative assessment (11)
#rater drift (11)
#AI-resistant assessment (10)
#Computerized Adaptive Testing (10)
#Inter-Rater Reliability (10)
#Knowledge Tracing (10)
#automated item generation (10)
#automated_essay_scoring (10)
#computerized adaptive testing (10)
#item_response_theory (10)
#DIF (9)
#LLM evaluation (9)
#Medical Education (9)
#concept drift (9)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 10401 | Screened Out |
Innovation Learning Process in New Normal Era: Effectiveness of Online Proctored Examination (OPE) Technology to Increase Student Honesty in Physics Lecture
KnE Social Sciences (2024) · doi:10.18502/kss.v9i8.15643 · v1.0 |
3.6 (gemini-3.5-flash-lite) | — |
| 10402 | Screened Out |
Analyzing Student Performance and Views on Online Asynchronous and Proctored Quizzes
Proceedings of the 2025 9th International Conference on Education and E-Learning (2025) · doi:10.1145/3789859.3789869 · v1.0 |
3.8 (gemini-3.5-flash-lite) | — |
| 10403 | Screened Out |
Exam Proctoring in Online Business Education: Empirical Insights and Strategic Recommendations
Journal of Applied Business and Economics (2025) · doi:10.33423/jabe.v27i1.7498 · v1.0 |
3.4 (gemini-3.5-flash-lite) | — |
| 10404 | Screened Out |
Accounting students’ performance in proctored online exams: early evidence from COVID-19 disrupted tertiary education learning
Accounting Education (2024) · doi:10.1080/09639284.2024.2346318 · v1.0 |
3.8 (gemini-3.5-flash-lite) | — |
| 10405 | Screened Out |
Comparing performance and integrity of proctored physical, unproctored online, and proctored online MBBS exams
Medical Teacher (2026) · doi:10.1080/0142159x.2026.2628961 · v1.0 |
4.9 (gemini-3.5-flash-lite) | — |
| 10406 | Screened Out |
Impact of proctored examinations on students' performance in an online undergraduate pharmacology course
Pharmacy Education (2025) · doi:10.46542/pe.2025.251.129135 · v1.0 |
3.4 (gemini-3.5-flash-lite) | — |
| 10407 | No Fulltext |
Deterring Cheating Online: A Framework for Proctoring Effectiveness
Journal of Management Education (2026) · doi:10.1177/10525629261432187 · v1.0 |
6.0 (gemini-3.1-flash-lite) | — |
| 10408 | Screened Out |
Effect of unproctored versus proctored examinations on student performance and long-term retention of knowledge.
Journal of Chiropractic Education (2024) · doi:10.7899/jce-23-16 · v1.0 |
4.2 (gemini-3.1-flash-lite) | — |
| 10409 | scored |
The Effects of Exam Setting on Students’ Test-Taking Behaviors and Performances: Proctored Versus Unproctored
International Review of Research in Open and Distance Learning (2023) · doi:10.19173/irrodl.v24i4.7145 · v1.0 |
5.0 (gemini-3.1-flash-lite) | — |
| 10410 | Screened Out |
Health behavior inventory – short form: a Chinese validation study using classical test theory and item response theory
BMC Public Health (2024) · doi:10.1186/s12889-024-19774-2 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10411 | Screened Out |
Commentary: Past, Present, and Future of Educational Measurement
Educational Measurement Issues and Practice (2024) · doi:10.1111/emip.12632 · v1.0 |
3.3 (gemini-3.1-flash-lite) | — |
| 10412 | Not Article |
RETRACTED: Do conservatives really have an advantage in mental health? An examination of measurement invariance
Scandinavian Journal of Psychology (2024) · doi:10.1111/sjop.13065 · v1.0 |
- | — |
| 10413 | Screened Out |
Evaluation of the psychometric properties of the homophobia scale in religious-based university students in Indonesia
Acta Psychologica (2024) · doi:10.1016/j.actpsy.2024.104474 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10414 | Screened Out |
Psychometric Properties of Fine Motor Function Measure in Children With Cerebral Palsy: A Rasch Analysis
Clinical Pediatrics (2024) · doi:10.1177/00099228241274295 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10415 | Screened Out |
An investigation into the effect of different missing data imputation methods on IRT-based differential item functioning
International Journal of Assessment Tools in Education (2024) · doi:10.21449/ijate.1417166 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 10416 | Screened Out |
Rethinking Measuring Moral Foundations in Prisoners: Validity Concerns and Implications
arXiv/Crossref (2024) · doi:10.31219/osf.io/tyd3q · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10417 | Screened Out |
Rasch Measurement Theory (RMT) Analyses of the Huntington’s Disease Everyday Functioning (Hi-DEF) to Evaluate Item Fit and Performance
Journal of Huntington s Disease (2024) · doi:10.3233/jhd-240001 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10418 | Screened Out |
Online Psychometric Performance of the Athens Insomnia Scale among Colombian People
Sleep and Vigilance (2024) · doi:10.1007/s41782-024-00279-2 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10419 | Screened Out |
Improving outcome measures in late onset Pompe disease: Modified Rasch‐Built Pompe‐Specific Activity scale
European Journal of Neurology (2024) · doi:10.1111/ene.16397 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10420 | Screened Out |
Myths about suicide - validating the Hungarian version of the Literacy of Suicide Scale (H-LOSS) on a community sample
BMC Public Health (2024) · doi:10.1186/s12889-024-19841-8 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10421 | scored |
Examining measures of schizotypy for gender and racial bias using item response theory and differential item functioning
Schizophrenia Research (2024) · doi:10.1016/j.schres.2024.08.015 · v1.0 |
0.0 (Flash Lite) | — |
| 10422 | Screened Out |
An interplay between cross-cultural and psychometric factors in the Montreal Cognitive Assessment: Experience from the language of a small nation
Applied Neuropsychology Adult (2024) · doi:10.1080/23279095.2024.2397041 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10423 | Screened Out |
Modeling construct change over time amidst potential changes in construct measurement: A longitudinal moderated factor analysis approach.
Psychological Methods (2024) · doi:10.1037/met0000685 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 10424 | scored |
472 Streets of struggle: understanding the safety perceptions of captive pedestrians
arXiv/Crossref (2024) · doi:10.1136/injuryprev-2024-safety.235 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 10425 | Screened Out |
Linguistic and cultural validation of symptom questionnaire for visual dysfunctions (SQVD) for psychometric analysis in the Chinese context
Medicine (2024) · doi:10.1097/md.0000000000039459 · v1.0 |
2.2 (gemini-3.5-flash-lite) | — |
| 10426 | Screened Out |
The comparison of the fit person Statistics Precision based on the Rasch model to identify the response style of acquiescence
DOAJ (DOAJ: Directory of Open Access Journals) (2024) · doi:10.22054/jep.2025.42356.2678 · v1.0 |
3.6 (gemini-3.5-flash-lite) | — |
| 10427 | Screened Out |
Meta-analysis of Studies Comparing the Likelihood Ratio and the Mantel-Haenszel Methods to Detect Differential Item Functioning
Jordan Journal of Educational Sciences (2024) · doi:10.47015/20.3.1 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10428 | Screened Out |
Measuring New and Returning Resident Assistants' Sense of Belonging
College student affairs journal (2024) · doi:10.1353/csj.2024.a950733 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10429 | failed |
Generalisability of the Barthel Index and the Functional Independence Measure: robustness of disability measures to Differential Item Functioning
Disability and Rehabilitation (2024) · doi:10.1080/09638288.2024.2391554 · v1.0 |
- | — |
| 10430 | failed |
Bias-Reduced Haebara and Stocking–Lord Linking
J — Multidisciplinary Scientific Journal (2024) · doi:10.3390/j7030021 · v1.0 |
- | — |
| 10431 | failed |
Measurement invariance and differential item functioning of the functionality appreciation scale (FAS) in Colombia and Spain
Body Image (2024) · doi:10.1016/j.bodyim.2024.101787 · v1.0 |
- | — |
| 10432 | failed |
Adaptive Ensemble Learning for Real-Time Predictive Analytics in Streaming Big Data Environments
Communications on Applied Nonlinear Analysis (2025) · doi:10.52783/cana.v32.6519 · v1.0 |
- | — |
| 10433 | failed |
Dataset Construction and Analysis for Evaluating Green Remodeling Effects in Public Daycare Centers Based on Actual Energy Consumption
Journal of the Korean Solar Energy Society (2025) · doi:10.7836/kses.2025.45.3.083 · v1.0 |
- | — |
| 10434 | failed |
Adaptive Machine Learning Models for Effort Estimation and Risk Detection in Distributed Software Project Pipelines
Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications (2025) · doi:10.58346/jowua.2025.i2.004 · v1.0 |
- | — |
| 10435 | failed |
Characterizing saddle point approximations for tail probability estimation with applications to Teesta valley hydroelectric feasibility
International Journal of Statistics and Applied Mathematics (2025) · doi:10.22271/maths.2025.v10.i7c.2579 · v1.0 |
- | — |
| 10436 | failed |
IOPC survey of test security practices and perspectives of licensed psychologists
The Clinical Neuropsychologist (2025) · doi:10.1080/13854046.2025.2528903 · v1.0 |
- | — |
| 10437 | failed |
Development and Evaluation of a Case-Based Serious Game for Diagnosis and Treatment Planning in Orthodontic Education: Quasi-Experimental Study
JMIR Serious Games (2025) · doi:10.2196/73956 · v1.0 |
- | — |
| 10438 | Not Article |
Charter of the International Virtual University of Regenerative Health
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21970219 · v1.0 |
- | — |
| 10439 | scored |
Re-Cultivating Academic Integrity Through University Education In Nigerian Universities
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21854462 · v1.0 |
0.0 (Flash Lite) | — |
| 10440 | failed |
Cheating and the fascistic public pedagogy of gamed capitalism: Pyramid schemes, sexist advice, misogynist scams and the perfect perfect bastard
The Review of Education Pedagogy & Cultural Studies (2026) · doi:10.1080/10714413.2026.2715447 · v1.0 |
- | — |
| 10441 | failed |
Generative Artificial Intelligence as a Metacognitive Partner: a Theoretical Model of Shared Regulation of Learning in Higher Education
Journal of Intelligent Decision Making and Information Science (2026) · doi:10.59543/jidmis.v3.1474 · v1.0 |
- | — |
| 10442 | failed |
ChatGPT and Large Language Models in Contemporary Nursing
Journal of Clinical Nursing (2026) · doi:10.1111/jocn.70490 · v1.0 |
- | — |
| 10443 | failed |
Students' Voices on the Implementation of Gen-AI Feedback in EFL Writing Classroom
ETERNAL (English Teaching Journal) (2026) · doi:10.26877/eternal.v17i2.3644 · v1.0 |
- | — |
| 10444 | failed |
The Future of English Studies in the Age of Artificial Intelligence: A Review of Emerging Research on Language, Literature and Writing Pedagogy in the Generative AI Era
International Journal of Innovative Science and Research Technology (IJISRT) (2026) · doi:10.38124/ijisrt/26aug075 · v1.0 |
- | — |
| 10445 | failed |
Challenges of research topic selection and documentation among postgraduate students in Nasarawa State University, Keffi
Aminu Kano Academic Scholars Association Multidisciplinary Journal (2026) · doi:10.64726/cpzvyg63 · v1.0 |
- | — |
| 10446 | failed |
Artificial Intelligence in English Language Teaching: Pedagogical Innovations, Critical Scaffolding, and Sociolinguistic Challenge
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23241047 · v1.0 |
- | — |
| 10447 | failed |
ANALISIS PENERAPAN ETIKA KEILMUAN DALAM PENULISAN KARYA ILMIAH MAHASISWA
Pendas Jurnal Ilmiah Pendidikan Dasar (2026) · doi:10.23969/jp.v11i03.63064 · v1.0 |
- | — |
| 10448 | failed |
AI-Enabled Misconduct During Interviews and Its Possible Impact on Future Physicians’ Critical Thinking
Cureus (2026) · doi:10.7759/cureus.114239 · v1.0 |
- | — |
| 10449 | Screened Out |
PERCEPTION OF BLENDED LEARNING METHODS IN FACILITATING CONTINUOUS EDUCATION AMONG HEALTH CARE PROFESSIONALS: A CROSS-SECTIONAL STUDY
EPRA International Journal of Multidisciplinary Research (IJMR) (2026) · doi:10.36713/epra28831 · v1.0 |
1.7 (gemini-3.5-flash-lite) | — |
| 10450 | Not Article |
THE ROLE OF ARTIFICIAL INTELLIGENCE IN ELT: BALANCING PEDAGOGICAL BENEFITS AND ACADEMIC INTEGRITY
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21857870 · v1.0 |
- | — |