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: 20150 →
Screen: 18986 →
Eligible: 2773 →
Included: 102
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 = 102 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 | 18044 | 2026-11-13 16:07 |
ref — reference lists of read works | 2079 | 2026-11-13 14:28 |
manual — added manually | 27 | 2026-10-07 12:05 |
Citation candidates take metadata from OpenAlex; Crossref/Unpaywall supply DOIs, versions and open PDFs.
Search strategy (7)
Current search version: v1.0. A work matches if its title/abstract hits a term from EVERY group of at least one query (OR within a group; case-insensitive, word boundaries).
Queries (groups):
education × ai; psychometrics × ai; integrity × education; drift × education; drift × psychometrics
education: education, educational, student, learner, teacher, lecturer, classroom, university, higher education, school, curriculum, assessment, exam, examination, examinee, test taker, test score, grading, homework, coursework, online course, essay, academic integrity, academic misconduct, plagiarism, cheating, e-learning, MOOC
ai: generative AI, GenAI, large language model, LLM, ChatGPT, GPT-4, chatbot, AI-generated, AI-assisted, AI-resistant, AI-resilient, AI tool, artificial intelligence
integrity: proctoring, proctored, unproctored, non-proctored, remote proctoring, online exam, online test, online testing, exam integrity, testing integrity, academic integrity, academic dishonesty, cheating, contract cheating, grade inflation
psychometrics: item response theory, IRT, Rasch, psychometric, educational measurement, educational testing, differential item functioning, computerized adaptive testing, adaptive testing, automated test assembly, knowledge tracing, automated scoring, automated essay scoring, validity argument, construct validity, assessment validity, predictive validity
drift: parameter drift, dynamic item response, item parameter drift, rater drift, severity drift, concept drift, change point, change-point detection, item compromise, preknowledge, item exposure, test security, aberrant response, person-fit, differential item functioning
Semantic Scholar phrases: assessment validity generative AI; AI-resilient assessment higher education; AI-resistant assessment design; generative AI exam performance natural experiment; differential item functioning large language models humans; LLM as test taker item response theory; proctored versus unproctored online exam scores; generative AI grade inflation; item parameter drift detection; item compromise preknowledge detection; concept drift knowledge tracing; change point detection educational testing
arXiv: cs.CY, cs.CL, cs.HC, cs.AI, cs.LG, stat.AP, stat.ME. Result caps per query: OpenAlex 50, Semantic Scholar 25, ERIC 25. Citation searching: depth 1 from read and 👍 works, depth 2 from 👍 only; neighbours of 👍 works bypass the lexical filter.
| Search version | Records (search) | First | Last |
|---|---|---|---|
v1.0 | 18044 | 2026-10-06 | 2026-11-13 |
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 | 9469 |
gemini-3.5-flash-lite | 8853 |
— | 730 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 265 |
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) | 18588 |
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): 16240 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 | 10505 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 4380 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1355 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1528 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1184 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 37 | 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)
6.46 / 10
Analyzed papers: 754
Quality Stratification
🟢 High rigor (M ≥ 7.0): 355
🟡 Moderate rigor (4.0 ≤ M < 7.0): 373
🔴 Low rigor (M < 4.0): 26
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 286 |
other | 55 |
theoretical | 27 |
rct | 17 |
assessment_design | 210 |
natural_experiment | 5 |
quasi_experiment | 61 |
review | 24 |
detector_evaluation | 48 |
survey | 7 |
experimental | 1 |
qualitative | 13 |
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=386, 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 (115)
#Psychometrics (71)
#Item Response Theory (60)
#academic integrity (52)
#assessment validity (52)
#Assessment Validity (51)
#item response theory (46)
#higher education (44)
#Automated Essay Scoring (43)
#automated essay scoring (40)
#Generative AI (32)
#Construct Validity (31)
#LLM as rater (27)
#construct validity (27)
#Large Language Models (25)
#Higher Education (24)
#IRT (24)
#large language models (24)
#LLM Evaluation (23)
#GPT-4 (22)
#generative AI (22)
#assessment redesign (20)
#Academic Integrity (19)
#Automated Scoring (19)
#prompt engineering (19)
#ChatGPT (18)
#LLM as Rater (17)
#Prompt Engineering (17)
#automated scoring (17)
#Assessment Redesign (16)
#assessment design (16)
#assessment_design (16)
#AI-resilient assessment (14)
#validity (14)
#LLMs as raters (13)
#automated assessment (13)
#differential item functioning (13)
#Computer Science Education (12)
#Computerized Adaptive Testing (12)
#LLM-as-a-judge (12)
#academic_integrity (12)
#benchmarking (12)
#formative assessment (12)
#inter-rater reliability (12)
#Automated Item Generation (11)
#Differential Item Functioning (11)
#Knowledge Tracing (11)
#LLMs as Test Takers (11)
#Quadratic Weighted Kappa (11)
#assessment-validity (11)
#assessment_validity (11)
#rater drift (11)
#AI-resistant assessment (10)
#Inter-Rater Reliability (10)
#LLM evaluation (10)
#automated item generation (10)
#automated_essay_scoring (10)
#computerized adaptive testing (10)
#item_response_theory (10)
#AI-Resilient Assessment (9)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 13951 | scored |
Exp-Bagging: A Validation-Based Exponential Weighting Strategy for Decision Tree Ensembles
Informatica (2026) · doi:10.31449/inf.v50i9.12730 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13952 | scored |
GWFlowAI: A One-Step, Location-Based Artificial Intelligence (AI) Framework for Exploring Public Groundwater Datasets in India
arXiv/Crossref (2026) · doi:10.5194/egusphere-egu26-20010 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13953 | scored |
Hydrological Non-stationarity in SWAT+ model Simulations under Changing Climate and Land Use Cover
arXiv/Crossref (2026) · doi:10.5194/egusphere-egu26-22388 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13954 | scored |
Comparative Approaches for Detecting Critical Transitions in Food Crises
arXiv/Crossref (2026) · doi:10.5194/egusphere-egu26-20227 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13955 | scored |
Robustness of Predictive-Regime Monitoring Under Sensor Degradation and Nonstationarity
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.19034120 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13956 | scored |
Privacy-Preserving Federated Continual Hierarchical TranConvAD for Scalable Multi-Source Log Anomaly Detection
International Conference on Intelligent Computing and Control Systems (ICICCS) (2026) · doi:10.1109/iciccs67901.2026.11502897 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13957 | Screened Out |
Intraoperative Video Recording: Capturing Opportunities to Advance Health Professions Education Research
Journal of surgical education (2026) · doi:10.1016/j.jsurg.2026.103928 · v1.0 |
3.7 (gemini-3.5-flash-lite) | — |
| 13958 | scored |
Navigating the AI Paradox in Higher Education: An Integrated Academic Governance Framework
Preprints.org (2026) · doi:10.20944/preprints202608.1221.v2 · v1.0 |
5.2 (gemini-3.5-flash-lite) | — |
| 13959 | Not Article |
DIGITAL TECHNOLOGIES IN MODERN FOREIGN LANGUAGE EDUCATION: OPPORTUNITIES AND CHALLENGES
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.22304368 · v1.0 |
- | — |
| 13960 | Screened Out |
A narrative discussion on the impact of respiratory infectious diseases on dental education in China: implications for curriculum design and student outcomes
Frontiers in Dental Medicine (2026) · doi:10.3389/fdmed.2026.1897947 · v1.0 |
3.5 (gemini-3.5-flash-lite) | — |
| 13961 | scored |
Trophies for All? Children's and Adolescents’ Reasoning About Inclusive Versus Selective Awards
Developmental Science (2026) · doi:10.1111/desc.70278 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13962 | Screened Out |
Exploring the Ethical Boundaries of AI Use in Secondary Vocational Education
International Journal of Engineering Pedagogy (iJEP) (2026) · doi:10.3991/ijep.v16i5.60331 · v1.0 |
3.3 (gemini-3.5-flash-lite) | — |
| 13963 | Screened Out |
Future direction of artificial intelligence in higher education assessment: a systematic review
Cogent Education (2026) · doi:10.1080/2331186x.2026.2728183 · v1.0 |
4.8 (gemini-3.5-flash-lite) | — |
| 13964 | Screened Out |
Designing Human-Centered Assessment in an AI World: A Framework for Making Student Thinking Visible
Intersection A Journal at the Intersection of Assessment and Learning (2026) · doi:10.61669/001c.169458 · v1.0 |
4.8 (gemini-3.5-flash-lite) | — |
| 13965 | No Fulltext |
The dependency trap: a systematic review of the contributing factors and consequences of student over-reliance on generative AI in higher education
Humanities and Social Sciences Communications (2026) · doi:10.1057/s41599-026-08959-2 · v1.0 |
5.8 (gemini-3.5-flash-lite) | — |
| 13966 | Screened Out |
Advancing a model for ethical artificial intelligence (AI) integration in higher education from the results of a system-wide survey
International Journal of Educational Research Open (2026) · doi:10.1016/j.ijedro.2026.100590 · v1.0 |
3.3 (gemini-3.5-flash-lite) | — |
| 13967 | scored |
American Nurses Association Code of Ethics as Foundation for Academic Integrity and Responsible Use of Artificial Intelligence
Nurse Educator (2026) · doi:10.1097/nne.0000000000002315 · v1.0 |
0.0 (Flash Lite) | — |
| 13968 | Not Article |
Future direction of artificial intelligence in higher education assessment: a systematic review
Figshare (2026) · doi:10.6084/m9.figshare.33436448 · v1.0 |
- | — |
| 13969 | scored |
THE CHEATERS PUZZLE
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.22304507 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13970 | Screened Out |
EFL Teachers’ Perceptions of Ethical AI Use in Teaching, Learning, and Assessment: Insights from an Omani University Context
Arab World English Journal (2026) · doi:10.24093/awej/vol17no3.20 · v1.0 |
3.3 (gemini-3.5-flash-lite) | — |
| 13971 | Screened Out |
OLIY TA’LIMDA SUN’IY INTELLEKT TEXNOLOGIYALARINI TADQIQOT FAOLIYATIGA INTEGRATSIYALASH TAMOYILLARI
Ijtimoiy-gumanitar sohada ilmiy-innovatsion tadqiqotlar (2026) · doi:10.67227/5zpyeg35 · v1.0 |
1.7 (gemini-3.5-flash-lite) | — |
| 13972 | Screened Out |
ChatGPT Use and Self-Reported Academic Writing Development among EFL Students: An Explanatory Sequential Mix-Methods Study
Datokarama English Education Journal (2026) · doi:10.24239/dee.v7i2.178 · v1.0 |
3.3 (gemini-3.5-flash-lite) | — |
| 13973 | Screened Out |
Models and Crowdsourcing Prompts for the Automatic Detection of Malicious Worker Behavior in Crowdsourced Paraphrases
International Journal of Cooperative Information Systems (2026) · doi:10.1142/s021884302650005x · v1.0 |
3.7 (gemini-3.5-flash-lite) | — |
| 13974 | scored |
Conditions and properties of non-Mendelian transmission of recombinants in female meiosis
bioRxiv (Cold Spring Harbor Laboratory) (2026) · doi:10.64898/2026.09.01.748656 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13975 | scored |
Perceived benefits, barriers, and health system consequences of introducing new models of online sexually transmitted and blood-borne infection testing in Ontario, Canada
BMC Health Services Research (2026) · doi:10.1186/s12913-026-15501-2 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13976 | Screened Out |
The Efficiency Trap: Fragmented Curricula and AI-Driven Surface Learning in Higher Education
arXiv/Crossref (2026) · doi:10.1145/3800935.3830885 · v1.0 |
2.8 (gemini-3.5-flash-lite) | — |
| 13977 | scored |
The credential and the clinician: Generative AI, cognitive substitution, and what universities owe society in nursing education
Nurse Education in Practice (2026) · doi:10.1016/j.nepr.2026.104969 · v1.0 |
0.0 (Flash Lite) | — |
| 13978 | scored |
F5CAB2 Certification Guide: BIG-IP Administration Data Plane Concepts - Syllabus, Sample Questions and Study Plan
OSF Preprints (OSF Preprints) (2026) · doi:10.5281/zenodo.22338454 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13979 | Screened Out |
The Use of ChatGPT in Undergraduate Scientific Research: Challenges and Opportunities in Higher Education
Journal of Educational and Social Research (2026) · doi:10.36941/jesr-2026-0417 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 13980 | Screened Out |
ATTITUDES TOWARD ARTIFICIAL INTELLIGENCE AND GENDER DIFFERENCES AMONG SPANISH EMERGING ADULTS
TEME (2026) · doi:10.22190/teme260131027g · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 13981 | No Fulltext |
Evaluating SHAP and LIME for Trustworthy Automatic Assessment of Concept-Map Proposit
Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer (2026) · doi:10.30812/matrik.v25i3.5972 · v1.0 |
6.7 (gemini-3.1-flash-lite) | — |
| 13982 | scored |
Artificial Intelligence Adoption and Labour Market Outcomes: A Study of Employment Perceptions in Central Europe
Inverge Journal of Social Sciences (2026) · doi:10.63544/ijss.v5i4.322 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13983 | scored |
Leadership Capacity Index (LCI): Toward an International Standard for Measuring Leadership Capacity
Preprints.org (2026) · doi:10.20944/preprints202607.2343.v1 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13984 | Screened Out |
Review on Explainable AI–Driven Intelligent Tutoring System for Hyper-Personalized Lifelong Learning
International Journal of Computer Applications (2026) · doi:10.5120/ijcad9695f624bf9 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 13985 | Screened Out |
AI-use preferences among metropolitan tourism students in the Philippines: a choice experiment study
Journal of Teaching in Travel & Tourism (2026) · doi:10.1080/15313220.2026.2713534 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 13986 | In Buffer |
Testing standards for AI-based scores in automated essay scoring
PLoS ONE (2026) · doi:10.1371/journal.pone.0354680 · v1.0 |
6.1 (gemini-3.7-flash) | — |
| 13987 | scored |
Determinants of individual investors’ decisions to invest in digital assets: Evidence from Vietnam
Social Sciences & Humanities Open (2026) · doi:10.1016/j.ssaho.2026.103278 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13988 | Screened Out |
Using the Reasoned Action Approach to Predict Students’ Misuse of Generative Artificial Intelligence in the Production of University Assessments
British Journal of Educational Studies (2026) · doi:10.1080/00071005.2026.2704595 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 13989 | Screened Out |
E-ASSESSMENT WITH AI: FAIRNESS, FEEDBACK, AND FUTURE SKILLS
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21572670 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 13990 | Not Article |
Homo Irrationalis or the All-Seeing Eye? Output Consistency and Construct Validity of Role-Playing by Large Language Models in Financial-Market Social Simulation
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21728599 · v1.0 |
- | — |
| 13991 | No Fulltext |
Evaluating Large Language Model–Based Automated Scoring in a Voice-Based Virtual Standardized Patient Platform for Medical Students: Cross-Sectional Agreement Study
JMIR Medical Education (2026) · doi:10.2196/95578 · v1.0 |
6.0 (gemini-3.1-flash-lite) | — |
| 13992 | Screened Out |
Eliciting Information about Teacher Applicants Using Open-Ended Questions
arXiv/Crossref (2026) · doi:10.17848/wp26-440 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 13993 | Screened Out |
Artificial Intelligence and Generative AI in Undergraduate Mathematics Education: A Systematic Review of Learning, Teaching, Mathematical Problem Solving, Challenges, and Future Directions.
International Journal of Versatile Research and Analysis (2026) · doi:10.56975/ijvra.v4i8.710454 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 13994 | Screened Out |
Data and Materials: Psychometric Properties of Self-Report Instruments Measuring Human-AI Relational and Attitudinal Constructs — A COSMIN-Based Systematic Review and Reliability Generalization Meta-Analysis
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.22643950 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 13995 | scored |
Exploring spiritual needs in inflammatory bowel disease: a qualitative study protocol adopting the think-aloud method
Minerva Gastroenterology (2026) · doi:10.23736/s2724-5985.26.04147-1 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13996 | scored |
Ergonomic risk assessment methods in agriculture: A comprehensive review of tools and techniques
International Journal of Research in Agronomy (2026) · doi:10.33545/2618060x.2026.v9.i8sf.6423 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13997 | scored |
Benchmarking compact language models for porous acoustic metamaterials
The Journal of the Acoustical Society of America (2026) · doi:10.1121/10.0045388 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13998 | Screened Out |
Image-native automated scoring of handwritten mathematical responses: reliability evidence and teacher–AI collaboration
International Journal of Evaluation and Research in Education (IJERE) (2026) · doi:10.11591/ijere.v15i4.38456 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 13999 | scored |
Artificial intelligence literacy and foreign language confidence among university second language learners
International Journal of Evaluation and Research in Education (IJERE) (2026) · doi:10.11591/ijere.v15i4.38707 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 14000 | Screened Out |
The Relationship Between Artificial Intelligence Use And Pedagogical Practice Innovation Among Vocational College Lecturers
International Journal on Research in STEM Education (2026) · doi:10.33830/ijrse.v8i1-1.15174 · v1.0 |
2.6 (gemini-3.5-flash-lite) | — |