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: 17315 →
Screen: 16299 →
Eligible: 2428 →
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 | 15497 | 2026-11-07 10:23 |
ref — reference lists of read works | 1791 | 2026-11-07 10:25 |
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 | 15497 | 2026-10-06 | 2026-11-07 |
Selection process & automation (8, 9, 11)
- Abstract screening: a single automated reviewer — an LLM (gemini-3.1-flash-lite, gemini-3.5-flash-lite), batches of 20, models alternate; every evaluation is stored (prefilter_evaluations) with model and rubric version.
- Full-text analysis and data collection: gemini-3.8-flash, gemini-3.7-flash, gemini-3.6-flash, prompt
v2.1. Only after PDF→Markdown conversion; the model is never called without full text. - Verification: code checks every quote against the text (exact match after normalisation); unconfirmed claims are discarded.
- Human: no independent second reviewer. The owner gives 👍/👎 verdicts on a sample (50% near threshold, 30% top, 20% random); they steer the queue and few-shot examples but do not change the score.
- Data items (10): summary, method/data/sample, claims, limitations, study design (assessment_design, psychometric_modeling, natural_experiment, detector_evaluation, survey, rct, quasi_experiment, qualitative, theoretical, review, other), tags, rubric criteria.
- Risk of bias (11): not assessed formally (RoB, GRADE not applied); criterion M (methodological rigor, 0–10) is a ranking score, not a RoB tool. No effect measures or meta-analysis (12, 13d–f, 15): synthesis is qualitative, via article analyses and tags.
| Screening model | Scored |
|---|---|
gemini-3.1-flash-lite | 8071 |
gemini-3.5-flash-lite | 7675 |
— | 619 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 173 |
gemini-3.8-flash | v2.1 | 107 |
gemini-3.7-flash | v2.1 | 100 |
Scoring rubric v2.3 (0–10 anchors)
The model scores 0–10 per criterion; the anchors below are embedded in the prompt. Formula and weights are in the footnote above. Changing anchors or weights without a new version is blocked by a hash check.
| Criterion | Weight | 2 | 5 | 8 |
|---|---|---|---|---|
| Topic relevance (R) | × min(1, R/7.0) | No link to education or assessment: image, audio or model watermarking, model IP protection, general NLP benchmarks, AI in industry. | Adjacent: generative AI in education without an assessment focus; academic integrity with no evidence on assessment outcomes; detectors of AI-generated text or of student AI use; general psychometrics (adaptive testing, test assembly, IRT) with no drift, validity or AI angle. | Core: validity of assessment under generative AI (what grades and scores mean when AI is available); how grades shift under unsupervised conditions or cheating (proctored vs unproctored exams, grade inflation); AI-resistant or AI-resilient assessment design; quantitative methods that detect or model distorted assessment or learning data (DIF between humans and LLMs, item parameter or rater drift, item compromise/preknowledge, change-point or concept-drift detection, LLMs as test takers or raters). |
| Methodological rigor (M) | 0.32 | Vague evaluation, convenience sample of a few dozen, self-report only, no control group or baseline. | Clear dataset or sample (hundreds), standard metrics or validated instruments, comparison with baselines or a control group. | Randomized/controlled or preregistered, large or multi-site/multilingual data, adversarial or robustness checks, effect sizes with uncertainty. |
| Engineering countermeasures (E) | 0.25 | No method or countermeasure: describes impact, attitudes or policy only. | A concrete method or countermeasure with an evaluation: statistical test or model, detector, scoring procedure, or an assessment design tried on real cohorts. | A rigorous, reusable method: formal model with guarantees or error control, open code, validated on both real and simulated data. |
| Consensus challenge (C) | 0.43 | Reconfirms standard wisdom. | Nuances the consensus (e.g. detectors fail under mild paraphrasing; AI use does not harm an outcome it is assumed to harm). | Directly refutes high-profile claims with rigorous evidence. |
| Novelty (N) | 0.00 | Replicates well-known findings (another survey of student attitudes to ChatGPT, another test of one detector on a handful of prompts). | New dataset, benchmark, population or setting, or a previously untried technique. | Fundamentally new attack or defence, a theoretical/impossibility result, or a finding that changes how the field frames the problem. |
The model interpolates between anchors. The deep-analysis score is computed by code with the same formula from criteria_breakdown.
| Rubric version | Papers scored |
|---|---|
v2.0 | 261 |
v2.1 | 183 |
v2.2 | 20 |
v2.3 (current) | 15901 |
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): 13898 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 | 8987 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3747 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1164 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1325 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1049 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 30 | 2 (too few) | 2 | 100% |
| Screening model | Seed recall | AUC | Sens / Spec | Prec / NPV | F₁ / AC₁ | 👍 / 👎 (TP/FP/FN/TN) |
|---|---|---|---|---|---|---|
gemini-3.1-flash-lite |
50% (1/2), 95% CI 9–91% | 0.63 | 100% (12/12), 95% CI 76–100% / 16% (3/19), 95% CI 6–38% | 43% (12/28), 95% CI 27–61% / 100% (3/3), 95% CI 44–100% | 0.60 / 0.05 | 12 / 19 (12/16/0/3) |
gemini-3.5-flash-lite |
— | — | — / 100% (1/1), 95% CI 21–100% | — / 100% (1/1), 95% CI 21–100% | — / 1.00 | 0 / 1 (0/0/0/1) |
— |
— | — | — / — | — / — | — / — | 0 / 0 (0/0/0/0) |
To narrow intervals, judge works from lower score bands: /admin/taste includes 20% random picks. Metrics update automatically with each verdict.
Methodological Rigor & Study Designs (11, 18)
Methodological rigor M (0–10) is evaluated during full-text analysis. Study design distribution reflects verified empirical classifications.
Mean Rigor (M)
6.55 / 10
Analyzed papers: 645
Quality Stratification
🟢 High rigor (M ≥ 7.0): 323
🟡 Moderate rigor (4.0 ≤ M < 7.0): 304
🔴 Low rigor (M < 4.0): 18
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 248 |
other | 45 |
theoretical | 27 |
rct | 13 |
assessment_design | 170 |
natural_experiment | 5 |
quasi_experiment | 56 |
detector_evaluation | 42 |
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=341, 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 (104)
#Psychometrics (61)
#Item Response Theory (50)
#academic integrity (47)
#assessment validity (39)
#Assessment Validity (38)
#item response theory (38)
#higher education (37)
#Automated Essay Scoring (31)
#automated essay scoring (31)
#Generative AI (29)
#construct validity (26)
#Construct Validity (24)
#IRT (24)
#LLM as rater (22)
#generative AI (20)
#Academic Integrity (19)
#Higher Education (19)
#Large Language Models (18)
#Automated Scoring (17)
#assessment redesign (17)
#LLM Evaluation (16)
#assessment_design (16)
#large language models (16)
#prompt engineering (16)
#automated scoring (15)
#AI-resilient assessment (14)
#GPT-4 (14)
#LLM as Rater (14)
#validity (14)
#Assessment Redesign (13)
#assessment design (13)
#automated assessment (13)
#differential item functioning (13)
#ChatGPT (12)
#Prompt Engineering (12)
#academic_integrity (12)
#LLMs as raters (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#inter-rater reliability (11)
#Automated Item Generation (10)
#LLM-as-a-judge (10)
#LLMs as Test Takers (10)
#item_response_theory (10)
#Computerized Adaptive Testing (9)
#DIF (9)
#Inter-Rater Reliability (9)
#LLM evaluation (9)
#automated_essay_scoring (9)
#concept drift (9)
#formative assessment (9)
#rater drift (9)
#AI-resistant assessment (8)
#Assessment Design (8)
#Computer Science Education (8)
#Differential Item Functioning (8)
#Knowledge Tracing (8)
#Validity (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 5301 | Screened Out |
AI-Reality Discrimination Assessment (ARDA-20): A Standardized Psychometric Instrument for Evaluating Individual Capacity to Discriminate AI-Generated Content from Authentic Reality
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.18928605 · v1.0 |
3.9 (gemini-3.1-flash-lite) | — |
| 5302 | scored |
Accuracy And Risk: Drivers Of Strategic Trading Decisions
Journal of Informatics Education and Research (2026) · doi:10.52783/jier.v6i1.4492 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5303 | scored |
Extending the information acceptance model: understanding student adoption and learning outcomes in AI-supported education
Learning Futures and Emerging Technologies (2026) · doi:10.1108/lfet-03-2025-0038 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5304 | Screened Out |
AIPsychoBench: Understanding the Psychometric Differences Between LLMs and Humans
Topics in Cognitive Science (2026) · doi:10.1111/tops.70041 · v1.0 |
4.6 (gemini-3.1-flash-lite) | — |
| 5305 | Screened Out |
Artificial intelligence–enabled teaching: Insights from Kazakhstan higher education students
Social Sciences & Humanities Open (2026) · doi:10.1016/j.ssaho.2026.102660 · v1.0 |
1.9 (gemini-3.1-flash-lite) | — |
| 5306 | Screened Out |
Artificial Intelligence Tools in English-Medium Academic Publishing: A Comparative Study of Scholarly Writing Practices in Iraq and Türkiye
SAGE Open (2026) · doi:10.1177/21582440261491814 · v1.0 |
3.9 (gemini-3.5-flash-lite) | — |
| 5307 | scored |
7 From stethoscope to silicon: is artificial intelligence the physician’s next essential tool?
arXiv/Crossref (2026) · doi:10.1136/heartjnl-2026-ics.7 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5308 | scored |
Reliable Detection, Unreliable Clinical Decision-making: Artificial Intelligence Models in Burn Assessment
Plastic & Reconstructive Surgery Global Open (2026) · doi:10.1097/gox.0000000000008141 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5309 | scored |
Green Nanoparticles in Precision Agriculture: A Critical Review of Smart Crop Management, Nano-Enabled Delivery Systems, and Sustainable Farming
Nanotechnology Science and Applications (2026) · doi:10.2147/nsa.s632332 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5310 | Screened Out |
INTELIGÊNCIA ARTIFICIAL GENERATIVA NA EDUCAÇÃO BÁSICA: POSSIBILIDADES, RISCOS E DESAFIOS PARA A FORMAÇÃO DOCENTE
REDEEMER JOURNAL (2026) · doi:10.67340/042 · v1.0 |
2.8 (gemini-3.5-flash-lite) | — |
| 5311 | scored |
2 Novice performed, AI-guided cardiac and lung ultrasound for acute heart failure diagnosis and risk stratification
arXiv/Crossref (2026) · doi:10.1136/heartjnl-2026-ics.2 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5312 | Screened Out |
A Study on the Role of Artificial Intelligence (AI) in School Education
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23362984 · v1.0 |
1.3 (gemini-3.5-flash-lite) | — |
| 5313 | Screened Out |
CRITICAL THINKING OF UNIVERSITY STUDENTS IN THE ERA OF ARTIFICIAL INTELLIGENCE
arXiv/Crossref (2026) · doi:10.59787/2413-5488-2026-55-3-60-68 · v1.0 |
4.9 (gemini-3.5-flash-lite) | — |
| 5314 | scored |
Advancements in echocardiography for congenital heart defects (CHDs): a literature review.
PubMed (2026) · doi:10.1097/ms9.0000000000005273 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5315 | scored |
P1.010: Performance of Sybil Artificial Intelligence for Lung Cancer Risk Assessment: A Head-to-Head Comparison of Cardiac vs. Lung CT Scans
Journal of Thoracic Oncology (2026) · doi:10.1016/j.jtho.2026.104416 · v1.0 |
0.0 (Flash Lite) | — |
| 5316 | Screened Out |
Epistemic Cognition in the Era of Artificial Intelligence
Journal of Medical Education and Curricular Development (2026) · doi:10.1177/23821205261499188 · v1.0 |
3.6 (gemini-3.5-flash-lite) | — |
| 5317 | scored |
Hybrid Explainable Deep Ensemble Framework with self-attention mechanism for early prediction of depression severity using PHQ-9 clinical and behavioral data.
PubMed (2026) · doi:10.1097/ms9.0000000000005555 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5318 | scored |
Individualization in homoeopathy and its relationship with personalized medicine: A systematic review
International journal of applied research (2026) · doi:10.22271/allresearch.2026.v12.i10c.14221 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5319 | Screened Out |
APRENDER A JULGAR: DÉDALO, O LABIRINTO E A FORMAÇÃO ÉTICA DIANTE DA INTELIGÊNCIA ARTIFICIAL
REDEEMER JOURNAL (2026) · doi:10.67340/039 · v1.0 |
2.4 (gemini-3.5-flash-lite) | — |
| 5320 | scored |
3 External validation of AI-based CT analysis for aortic root assessment and valve sizing in TAVI: a core lab study
arXiv/Crossref (2026) · doi:10.1136/heartjnl-2026-ics.3 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5321 | scored |
Waste-to-value upgrading of edible mushroom processing residues: green extraction innovation, AI optimization, and development of functional ingredients for future foods
Journal of Future Foods (2026) · doi:10.1016/j.jfutfo.2026.10.019 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5322 | scored |
Multidimensional Evolution of Integrated Assessment Modeling Research: A Structural Literature Analysis Using LLM-Assisted Classification
Journal of Energy and Climate Change (2026) · doi:10.3724/j.issn.2097-4981.jecc-2026-0051 · v1.0 |
0.0 (Flash Lite) | — |
| 5323 | Screened Out |
ChatGPT Use and Critical Thinking Among Undergraduate Students: A Mixed Methods Study
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.35719/jier.v7i4.604 · v1.0 |
4.2 (gemini-3.5-flash-lite) | — |
| 5324 | scored |
Predictive analysis in acute ischemic stroke imaging.
PubMed (2026) · doi:10.1097/ms9.0000000000005277 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5325 | scored |
Comparison of Integrated Versus Sequential Artificial Intelligence–Based Computer-Aided Diagnosis Systems in Breast Ultrasound: A Prospective Pilot Study on Examination Time, Reproducibility, and Diagnostic Performance
Academic Radiology (2026) · doi:10.1016/j.acra.2026.08.038 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5326 | scored |
Psychometric Properties of the French Version of the Physical Literacy in Children Questionnaire (PL-C Quest)
Journal of Functional Morphology and Kinesiology (2026) · doi:10.3390/jfmk11040425 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5327 | Not Article |
irtlink: IRT Linking and Linking Error Estimation Across Time Points and Groups
arXiv/Crossref (2026) · doi:10.32614/cran.package.irtlink · v1.0 |
- | — |
| 5328 | scored |
Psychometric properties of a short knowledge test to evaluate awareness of external ventricular drain care practices among Chinese nurses: a Rasch model analysis of an efficient 7-item self-reported assessment tool
Frontiers in Public Health (2026) · doi:10.3389/fpubh.2026.1963782 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5329 | scored |
Measurement invariance and item functioning of the PHQ-15, DASS-21, and Paykel Suicide Scale across gender and cohorts in Peruvian university students
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23420477 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5330 | scored |
Rasch analysis of an intimate partner violence tool for healthcare students
Discover Public Health (2026) · doi:10.1186/s12982-026-03058-8 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5331 | Not Article |
d-metric: A unified effect size measure for differential item functioning
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23470190 · v1.0 |
- | — |
| 5332 | No Fulltext |
Elevating Test Security in Validity Theory
Language Testing (2026) · doi:10.1177/02655322261490117 · v1.0 |
6.8 (gemini-3.5-flash-lite) | — |
| 5333 | Screened Out |
Psychometric Evaluation of the Intuitive Eating Scale-3 for Youth (IES-3-Y) in a German-Speaking Sample
Open Science Framework (2026) · doi:10.17605/osf.io/vs4y7 · v1.0 |
2.6 (gemini-3.5-flash-lite) | — |
| 5334 | Screened Out |
Psychometric Scale Development with the PsychtrixWeb Scale Sandbox
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23482608 · v1.0 |
3.1 (gemini-3.5-flash-lite) | — |
| 5335 | Screened Out |
Cross-national validation of the Depression, Anxiety and Stress Scale for Youth (DASS-Y) in Lebanese and Chinese adolescents
PLoS ONE (2026) · doi:10.1371/journal.pone.0360807 · v1.0 |
3.2 (gemini-3.5-flash-lite) | — |
| 5336 | Screened Out |
The Relational Architecture of Complex Thinking in International Postgraduate Education: Psychometric Network Evidence from Japan
Trends in Higher Education (2026) · doi:10.3390/higheredu5040121 · v1.0 |
4.0 (gemini-3.5-flash-lite) | — |
| 5337 | Not Article |
Code: Item response theory reveals measurement bias in gesture imitation tests of post-stroke apraxia
Jülich DATA (2026) · doi:10.26165/juelich-data/diroun · v1.0 |
- | — |
| 5338 | Screened Out |
Development and validation of the professional identity generic scale for chinese university students: Factor-analytic and item response theory approaches
BMC Psychology (2026) · doi:10.1186/s40359-026-05831-5 · v1.0 |
3.8 (gemini-3.5-flash-lite) | — |
| 5339 | scored |
Differential Item Functioning
Emerald Group Publishing Limited eBooks (2026) · doi:10.1108/978-1-80791-470-720261009 · v1.0 |
0.0 (Flash Lite) | — |
| 5340 | Screened Out |
Applied Psychometrics Using R Software
Emerald Group Publishing Limited eBooks (2026) · doi:10.1108/978-1-80791-470-7 · v1.0 |
3.0 (gemini-3.5-flash-lite) | — |
| 5341 | Screened Out |
Multidimensional Emotional Assessment for Community Mental Health: Exploring Sample-Based Reference Values for the Core Seven-Emotions Inventory–Short Form (CSEI-s)
Preprints.org (2026) · doi:10.20944/preprints202610.2310.v1 · v1.0 |
2.6 (gemini-3.5-flash-lite) | — |
| 5342 | Screened Out |
Cultural adaptation to Quechua and psychometric properties of the Family APGAR Scale in Peruvian adults in the San Martín Region
Medwave (2026) · doi:10.5867/medwave.2026.09.3208 · v1.0 |
2.6 (gemini-3.5-flash-lite) | — |
| 5343 | scored |
Explainable AI and Real-Time Decisioning for Automated Government Case Management in Saudi Arabia
Iconic Research and Engineering Journals (2026) · doi:10.64388/irev10i4-1724296 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5344 | Screened Out |
A systematic review of differential item functioning: Trends in methods, sample size, groups, and analytical software (2016–2025)
Multidisciplinary Reviews (2026) · doi:10.31893/multirev.2027143 · v1.0 |
2.9 (gemini-3.5-flash-lite) | — |
| 5345 | Not Article |
A Computerized Scale for Detecting Indicators of Dyslexia Among Fourth-Grade Primary School Pupils: A Psychometric Standardization Study on a National Algerian Sample
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23627680 · v1.0 |
- | — |
| 5346 | Screened Out |
Technique Analysis for Multilayer Perceptrons to Deal with Concept Drift in Data Streams
Interdisciplinary Journal of Information Knowledge and Management (2024) · doi:10.28945/5405 · v1.0 |
3.9 (gemini-3.5-flash-lite) | — |
| 5347 | scored |
Sealing Degradation Modeling and Reliability Analysis Method of Deep‐Water Pipeline Connector Based on the Two‐Phase Nonlinear Wiener Process
Modelling and Simulation in Engineering (2024) · doi:10.1155/2024/5427464 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5348 | scored |
The dire consequences of a corrupt sport contest
Applied Economics (2026) · doi:10.1080/00036846.2026.2699430 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5349 | In Buffer |
Automation bias in teachers’ evaluation of student writing: effects of algorithmic warnings and visual risk cues in AI detection reports
Frontiers in Psychology (2026) · doi:10.3389/fpsyg.2026.1889402 · v1.0 |
5.4 (gemini-3.6-flash) | — |
| 5350 | scored |
Automating Research in Higher Education
Advances in computational intelligence and robotics book series (2026) · doi:10.4018/979-8-3373-7665-3.ch004 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |