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: 18223 →
Eligible: 2679 →
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 | 8515 |
— | 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) | 17825 |
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): 15571 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 | 10062 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 4200 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1309 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1474 | 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 |
|---|---|---|---|---|
| 10201 | No Fulltext |
IRT-Based Internal Measures of Differential Functioning of Items and Tests
Applied Psychological Measurement (1995) · doi:10.1177/014662169501900405 · v1.0 |
5.5 (gemini-3.5-flash-lite) | — |
| 10202 | Screened Out |
The development of the Cough Hypersensitivity Questionnaire for chronic cough
ERJ Open Research (2024) · doi:10.1183/23120541.00468-2024 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10203 | Screened Out |
The Use of Multidimensional Item Response Theory Estimations in Controlling Differential Item Functioning
Measurement Interdisciplinary Research and Perspectives (2024) · doi:10.1080/15366367.2024.2359450 · v1.0 |
4.5 (gemini-3.1-flash-lite) | — |
| 10204 | Screened Out |
Uncovering measurement bias: Differential item functioning by income in the Couples Satisfaction Index.
Journal of Family Psychology (2024) · doi:10.1037/fam0001247 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 10205 | Screened Out |
Leader-Member Exchange: An Investigation into Dyadic Measures
Academy of Management Proceedings (2024) · doi:10.5465/amproc.2024.18908abstract · v1.0 |
1.5 (gemini-3.1-flash-lite) | — |
| 10206 | No Fulltext |
Heterogeneity of Item-Treatment Interactions Masks Complexity and Generalizability in Randomized Controlled Trials
Journal of Research on Educational Effectiveness (2024) · doi:10.1080/19345747.2024.2361337 · v1.0 |
6.4 (gemini-3.1-flash-lite) | — |
| 10207 | Not Article |
Supplemental Material for Assessing Attention-Deficit/Hyperactivity Disorder-Related Impairment: Differential Item Functioning Based on Child Demographic Characteristics
School Psychology (2024) · doi:10.1037/spq0000643.supp · v1.0 |
- | — |
| 10208 | Screened Out |
Introduction and validation of the open symptom framework: a public domain modular framework for patient-reported measurement of symptoms related to cancer and its treatment
Quality of Life Research (2024) · doi:10.1007/s11136-024-03656-6 · v1.0 |
2.0 (gemini-3.1-flash-lite) | — |
| 10209 | Screened Out |
Conducting cross-cultural, multi-lingual or multi-country scale development and validation in health care research: A 10-step framework based on a scoping review
Journal of Global Health (2024) · doi:10.7189/jogh.14.04151 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10210 | Screened Out |
Assessing ChatGPT-4's Proficiency in English College Entrance Examinations Using Web Raschonline: A Comparative Study (Preprint)
arXiv/Crossref (2024) · doi:10.2196/preprints.64556 · v1.0 |
4.8 (gemini-3.1-flash-lite) | — |
| 10211 | Screened Out |
Differential item functioning of the revised Multigroup Ethnic Identity Measure (MEIM-R) in racially and income diverse youth with type 1 diabetes
Journal of Pediatric Psychology (2024) · doi:10.1093/jpepsy/jsae059 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10212 | Screened Out |
Evaluating Item Response Format and Content Using Partial Credit Trees in Scale Development
Journal of Survey Statistics and Methodology (2024) · doi:10.1093/jssam/smae028 · v1.0 |
2.3 (gemini-3.1-flash-lite) | — |
| 10213 | Screened Out |
Further Psychometric Evaluation of the Eight-Item Hospice Philosophy Scale: Results From a National Sample of Interdisciplinary Hospice Clinicians
Journal of Applied Gerontology (2024) · doi:10.1177/07334648241265183 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10214 | Screened Out |
Differential Item Functioning (DIF): Sebuah Analisis Bibliometrik
EDUKATIF JURNAL ILMU PENDIDIKAN (2024) · doi:10.31004/edukatif.v6i4.7463 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10215 | scored |
Ensuring fairness in assessment in health professions education: rapid analysis tools to detect differential item functioning across groups
International Journal of Medical Education (2024) · doi:10.5116/ijme.6694.de69 · v1.0 |
0.0 (Flash Lite) | — |
| 10216 | Screened Out |
Age-related Psychometric Dimensionality Using the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition Opioid Use Disorder Diagnostic Criteria
Journal of Addiction Medicine (2024) · doi:10.1097/adm.0000000000001343 · v1.0 |
2.3 (gemini-3.1-flash-lite) | — |
| 10217 | Screened Out |
Cross-cultural adaptation and validation of the caregiver self-efficacy in contributing to patient self-care scale in China
BMC Public Health (2024) · doi:10.1186/s12889-024-19534-2 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 10218 | scored |
Using dynamic item response theory and machine learning based on natural language processing to improve the reliability of the Operant Motive Test.
Motivation Science (2024) · doi:10.1037/mot0000358 · v1.0 |
0.0 (Flash Lite) | — |
| 10219 | scored |
PCOS endometrium-derived epithelial organoids as a novel model to study endometrial dysfunction
Human Reproduction (2025) · doi:10.1093/humrep/deaf113 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 10220 | scored |
ℓ1 trend clustering: Trend estimation, clustering, and change detection for spatial-temporal ground displacement data from satellite remote sensing
Geodata and AI. (2025) · doi:10.1016/j.geoai.2025.100020 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 10221 | scored |
Assessment of hydroclimatic variability and aridity trends in the Mississippi River Basin using parametric and non-parametric techniques
Frontiers in Climate (2025) · doi:10.3389/fclim.2025.1481926 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 10222 | scored |
Unveiling the Dark Side of the Force: Factors underlying the misuse and dependence of Generative Artificial Intelligence in Higher Education
Research Square (2026) · doi:10.21203/rs.3.rs-10599376/v1 · v1.0 |
0.0 (Flash Lite) | — |
| 10223 | scored |
From Prompts to Verified Loops: The PCHL-HEFramework for Generative AI-AssistedEducational and Research Content Creation inHigher Education
arXiv/Crossref (2026) · doi:10.35542/osf.io/wudk7_v1 · v1.0 |
5.2 (gemini-3.1-flash-lite) | — |
| 10224 | Screened Out |
STUDI ETNOPEDAGOGI TENTANG PENDIDIKAN ANAK DALAM KELUARGA MASYARAKAT BATAK DI MEDAN DENAI BERBASIS NILAI-NILAI KEARIFAN LOKAL
Jurnal Dialect (2026) · doi:10.46576/dl.v2i2.9088 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 10225 | Screened Out |
Digital Tools as Educational Resources
Advances in computational intelligence and robotics book series (2026) · doi:10.4018/979-8-3373-9484-8.ch009 · v1.0 |
1.7 (gemini-3.1-flash-lite) | — |
| 10226 | scored |
Head-pose-assisted recalibration for robust fatigue detection of construction machinery operators
Engineering Applications of Artificial Intelligence (2026) · doi:10.1016/j.engappai.2026.115834 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 10227 | No Fulltext |
Ethical Epistemic Collaborative Output Dialogue (EECOD): A Practical Solution Providing Visible Thinking in an Era of AI-Assisted Educational Teaching and Learning
arXiv/Crossref (2026) · doi:10.35542/osf.io/eaygt_v1 · v1.0 |
5.5 (gemini-3.1-flash-lite) | — |
| 10228 | Screened Out |
Beyond the Similarity Score: A Phenomenological Case Study of Faculty Perspectives on Turnitin Plagiarism Detection Software at Landmark University, Nigeria
Inkunabula Journal of Library Science and Islamic Information (2026) · doi:10.24239/inkunabula.v5i2.5178 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 10229 | Screened Out |
TRANSFORMING ALGEBRA LEARNING IN THE ERA OF ARTIFICIAL INTELLIGENCE
INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (2026) · doi:10.5281/zenodo.21820109 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 10230 | Screened Out |
A Mixed Method Study of Experience in Using ChatGPT in Essay Writing from EFL Learners’ Perspectives
International Journal of Technology in Education (2026) · doi:10.46328/ijte.8941 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 10231 | Screened Out |
Educational artificial intelligence as paradoxical infrastructure: Governing uncertainty and regulatory ritualism in Australian higher education
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21819250 · v1.0 |
3.3 (gemini-3.1-flash-lite) | — |
| 10232 | scored |
SOCIO-PSYCHOLOGICAL FACTORS OF EMOTIONAL DEPENDENCE AND THEIR INTERRELATIONSHIP
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21820549 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 10233 | Screened Out |
Mapping research on ChatGPT and LLMs in EFL writing: A scoping review with bibliometric analysis
Contemporary Educational Technology (2026) · doi:10.30935/cedtech/19080 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 10234 | scored |
Social Media in Education: Advantages and Disadvantages
International Journal For Multidisciplinary Research (2026) · doi:10.36948/ijfmr.2026.v08i04.85265 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 10235 | Not Article |
Correction: The moderating role of academic integrity in the relationship between artificial intelligence and critical thinking among graduate students in Jordan
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1858671 · v1.0 |
- | — |
| 10236 | Screened Out |
Rethinking Higher Education Pedagogy for Generation Z: Strategies, Challenges, and Theoretical Perspectives on Artificial Intelligence Integration
Journal of Asia Entrepreneurship and Sustainability (2026) · doi:10.66635/9a8bbs13 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 10237 | scored |
AI as a Teaching Tool in Higher Education: Transforming Learning and Safety
River Publishers eBooks (2026) · doi:10.1201/9788743812135-2 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 10238 | Screened Out |
Intellectual Degradation Amidst the Development of AI Technology: A Comparison Between AI Dependency Students and AI Assisted Learning Students
Tafkir Interdisciplinary Journal of Islamic Education (2026) · doi:10.31538/tijie.v7i4.3207 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 10239 | Screened Out |
Generative artificial intelligence in forensic science education: Student perspectives on AI-supported crime scene decision-making
Forensic Science International Synergy (2026) · doi:10.1016/j.fsisyn.2026.100723 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 10240 | Screened Out |
Measuring human metacognition in GPT-assisted cognition: development and psychometric validation of a novel scale
Frontiers in Psychology (2026) · doi:10.3389/fpsyg.2026.1823525 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 10241 | scored |
Conditional Acceptance and the Optimism–Knowledge Gap: A Scoping Review of Attitudes and Perceptions of Artificial Intelligence in Healthcare in Italy
Medical Sciences (2026) · doi:10.3390/medsci14020276 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 10242 | No Fulltext |
Know when to trust: Making AI scoring more reliable for educational assessment
Behavior Research Methods (2026) · doi:10.3758/s13428-026-03058-1 · v1.0 |
7.6 (gemini-3.5-flash-lite) | — |
| 10243 | Not Article |
Review for "Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agents"
arXiv/Crossref (2026) · doi:10.1098/rsos.251802/v2/review2 · v1.0 |
- | — |
| 10244 | No Fulltext |
An AI-Driven Approach to TIMSS Item Verification and Alignment
International Association for the Evaluation of Educational Achievement (2026) · doi:10.58150/rd_4_1 · v1.0 |
6.3 (gemini-3.5-flash-lite) | — |
| 10245 | Screened Out |
Guidance on artificial intelligence use for rapid evidence mapping of early childhood intervention outcome measures
Developmental Medicine & Child Neurology (2026) · doi:10.1111/dmcn.70323 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 10246 | scored |
LLM4KT: Enhancing Knowledge Tracing via Large Language Models
Research Square (2026) · doi:10.21203/rs.3.rs-9653429/v1 · v1.0 |
0.0 (Flash Lite) | — |
| 10247 | scored |
New and old challenges for clinical psychology and psychotherapy toward 2030
Frontiers in Psychology (2026) · doi:10.3389/fpsyg.2026.1789643 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 10248 | No Fulltext |
Comparative Analysis of Psychometric Profiling of AI-Generated and Teacher Constructed Multiple-Choice Question Items
Journal of Innovations in Educational Assessment (2026) · doi:10.66545/s7vd5d22 · v1.0 |
6.3 (gemini-3.5-flash-lite) | — |
| 10249 | scored |
Artifacts for: Context Contamination in LLM Analysis of Network Security Logs (LogInject-1.0)
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20436934 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 10250 | Screened Out |
Artificial intelligence in interior design education: technology acceptance and creative agency in a resource-constrained context
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1837890 · v1.0 |
3.2 (gemini-3.5-flash-lite) | — |