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: 20240 →
Screen: 19073 →
Eligible: 2784 →
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 | 18128 | 2026-11-13 22:29 |
ref — reference lists of read works | 2085 | 2026-11-13 22:30 |
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 | 18128 | 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 | 9509 |
gemini-3.5-flash-lite | 8898 |
— | 732 |
| 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) | 18675 |
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): 16316 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 | 10554 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 4399 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1363 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1537 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1186 | 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=387, 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 |
|---|---|---|---|---|
| 15501 | scored |
Validation of the Arabic Version of the Chronic Heart Failure Health-Related Quality of Life Questionnaire in Jordan
Healthcare (2026) · doi:10.3390/healthcare14081076 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15502 | scored |
Measuring receptive vocabulary in German students with special educational needs
arXiv/Crossref (2026) · doi:10.31234/osf.io/t8q76_v1 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15503 | scored |
Gender dynamics in rape myth acceptance: A psychometric evaluation of the IRMA-S “She Asked For It” subscale in Spanish [Author Accepted Manuscript]
arXiv/Crossref (2026) · doi:10.23668/psycharchives.21859 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15504 | scored |
Psychometric Validation of the Training Needs Assessment Tool (TNAT-38): Comprehensive COSMIN-Aligned Structural Validity, Measurement Invariance, and Differential Item Functioning Analysis v1
arXiv/Crossref (2026) · doi:10.17504/protocols.io.x54v9qnozl3e/v1 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15505 | Screened Out |
Governance Signal Convergence: A Framework for Cross-Mechanism Pattern Detection in Enterprise AI Governance
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20668398 · v1.0 |
4.6 (gemini-3.1-flash-lite) | — |
| 15506 | Not Article |
PD-AHC-CD: Source Code and Supplementary Materials for "Adaptive Hybrid Clustering Based on Partitioning and Density for Streaming Data with Concept Drift Handling"
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20677632 · v1.0 |
- | — |
| 15507 | scored |
Facilitating in flipped classroom teaching scenarios – lessons learned
International Conference on Higher Education Advances (HEAd) (2026) · doi:10.4995/head26.2026.21545 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15508 | scored |
Interannual Variability and Recurring Drought Hotspots in Ethiopia’s South Wollo Highlands
Hydrology (2026) · doi:10.3390/hydrology13060156 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15509 | scored |
Hydrological regime shifts and their implications for drought monitoring in a Mediterranean river basin
Hydrological Sciences Journal (2026) · doi:10.1080/02626667.2026.2690185 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15510 | scored |
Pan-Arctic Sea Ice Decline and Permafrost Coastal Vulnerability: An Exploratory 168-Year Assessment
Land (2026) · doi:10.3390/land15061075 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15511 | Not Article |
Phase-Based Modeling of Malaria Transmission with Climate Modulation: An R Software Pipeline
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20742309 · v1.0 |
- | — |
| 15512 | scored |
Assessment of long term hydrological variability and flow regime alterations in the Subarnarekha River basin using non parametric trend analysis and wavelet approaches
Discover Geoscience (2026) · doi:10.1007/s44288-026-00601-7 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15513 | scored |
ADHD and Creativity: Contribution of ADHD Trait Dimensions and an Exploration of Other Contributing Factors
arXiv/Crossref (2026) · doi:10.31234/osf.io/m8vr3_v1 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15514 | Screened Out |
Hybrid deep learning framework for AI-generated text detection
Complex & Intelligent Systems (2026) · doi:10.1007/s40747-026-02501-2 · v1.0 |
3.9 (gemini-3.5-flash-lite) | — |
| 15515 | Screened Out |
Editorial: Generative artificial intelligence and writing instruction in K–12 and college education
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1984450 · v1.0 |
3.5 (gemini-3.5-flash-lite) | — |
| 15516 | scored |
From Employability to Human Flourishing: A Governance Framework for University-Industry Partnerships in African Higher Education
arXiv/Crossref (2026) · doi:10.35542/osf.io/b7gea_v1 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15517 | scored |
AN AFFORDABLE WEARABLE SYSTEM TO MONITOR AND IMPROVE ADOLESCENT ATHLETE BREATHING USING LOW-COST SENSORS AND AI-DRIVEN COACHING
arXiv/Crossref (2026) · doi:10.5121/csit.2026.161708 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15518 | Screened Out |
Traditional and Artificial Intelligence-based Assessment in Foreign Universities
Natural Resources for Human Health (2026) · doi:10.53365/nrfhh.1969 · v1.0 |
2.8 (gemini-3.5-flash-lite) | — |
| 15519 | scored |
Below the Surface: Creep, Corrosion, and Tipping Points in the Social Resilience of Science
arXiv (Cornell University) (2026) · doi:10.48550/arxiv.2609.22863 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15520 | Screened Out |
Assessing Generative Artificial Intelligence Tools Usage Guidelines at the Top 200 Universities Worldwide
Future Internet (2026) · doi:10.3390/fi18090492 · v1.0 |
3.8 (gemini-3.5-flash-lite) | — |
| 15521 | Screened Out |
Mechanisms of public governance for developing higher education students' responsibility for their own learning (academic agency)
Scientific Notes of Lviv University of Business and Law (2026) · doi:10.66556/2663-0117.51.klymyshyn-n · v1.0 |
1.9 (gemini-3.5-flash-lite) | — |
| 15522 | Screened Out |
Student engagement with generative AI in Greek higher education: Use patterns and academic integrity concerns
Journal of Digital Educational Technology (2026) · doi:10.29333/jdet/19285 · v1.0 |
4.0 (gemini-3.5-flash-lite) | — |
| 15523 | scored |
The Influence of Moral Creed Learning on the Formation of Students' Religious Character in Madrasah Aliyah
Educational Journal of Learning Technology (2026) · doi:10.58230/edutech.v4i1.112 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15524 | Screened Out |
Application of generative artificial intelligence in teaching Business Analysis to undergraduate students
Eurasian Journal of Current Research in Psychology and Pedagogy (2026) · doi:10.46914/2959-3999-2026-1-3-99-111 · v1.0 |
2.8 (gemini-3.5-flash-lite) | — |
| 15525 | Screened Out |
Responsible Generative AI in Doctoral Education: Balancing Innovation, Academic Integrity, and Researcher Autonomy
International Journal of Modern Science and Research Technology (2026) · doi:10.67317/ijmsrt26sep073 · v1.0 |
2.4 (gemini-3.5-flash-lite) | — |
| 15526 | Screened Out |
Vietnamese Lecturers' Emotional Responses and Regulatory Strategies Towards AI in L2 Writing
ICTE Conference Proceedings (2026) · doi:10.54855/ictep.26117 · v1.0 |
2.7 (gemini-3.5-flash-lite) | — |
| 15527 | Screened Out |
BO‘LAJAK O‘QITUVCHILARNING ILMIY-TADQIQOT KOMPETENSIYASINI SUN’IY INTELLEKT VOSITALARI ASOSIDA RIVOJLANTIRISH METODIKASI (KOMPARATIVISTIK PEDAGOGIKA FANI MISOLIDA)
Zamonaviy jamiyat va innovatsiyalar ilmiy-nazariy jurnali (2026) · doi:10.67547/fanistiqbollari.2026.493 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 15528 | scored |
Kinship Does Not Transfer What multicellularity and symbiosis imply about the form AGI oversight must take — which biological enforcement mechanisms survive the move to human–AGI relations, which do not, and the cooperative transition humanity must complete first
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.22844143 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15529 | No Fulltext |
Do Respondents’ Commitments Improve Data Quality in Opt-in Online Surveys? An International Experiment on Participants’ Attentiveness, Cheating, Satisficing, and Skipping
Sociological Methods & Research (2026) · doi:10.1177/00491241261481205 · v1.0 |
6.0 (gemini-3.1-flash-lite) | — |
| 15530 | Screened Out |
THE ROLE OF ARTIFICIAL INTELLIGENCE IN PERSONALIZING THE EDUCATIONAL PROCESS
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.22843081 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 15531 | scored |
SIGNIFICANCE OF AMANAHA AS ONE OF THE MORAL QUALITIES IN THE LIFE OF THE MUSLIM
International Journal of Humanities, Literature and Art Research (2026) · doi:10.70382/mejhlar.v13i6.0131 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15532 | scored |
Beyond simple structures: a bifactor-ESEM Approach to understanding student acceptance of generative AI in higher education
Current Psychology (2026) · doi:10.1007/s12144-026-09959-w · v1.0 |
0.0 (Flash Lite) | — |
| 15533 | Screened Out |
From AI assistance to responsible academic agency: A human-centered framework for generative artificial intelligence in higher education
Journal of Progress in Artificial Intelligence (2026) · doi:10.68152/2426 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 15534 | Screened Out |
Teacher-Guided AI-Assisted Gamified Learning for EFL Vocabulary and Motivation
Mağallaẗ al-Nūr li-l-dirāsāt al-insāniyyaẗ. (2026) · doi:10.69513/jnfh.v4.i3.a7 · v1.0 |
3.6 (gemini-3.1-flash-lite) | — |
| 15535 | scored |
Integrating Emotional Intelligence and Digital Empathy in Digital and Hybrid Teaching: A Conceptual Framework and Research Agenda
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.34118/sej.v8i3.4868 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15536 | Screened Out |
AI-Enhanced Virtual Simulation as an Adjunct to Skills Laboratory and Clinical Training in Radiography Education: A Commentary from Somalia and Other Resource-Constrained Settin
Advances in Medical Education and Practice (2026) · doi:10.2147/amep.s637719 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 15537 | Screened Out |
DEVELOPMENT OF CRITICAL ACADEMIC LITERACY THROUGH AI-SUPPORTED WRITING PRACTICES IN UNIVERSITY EDUCATION
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.22694968 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 15538 | Screened Out |
Large language models as psychometric laboratories? Maybe not yet. Cautionary evidence from synthetic personality data
Psychiatry and Clinical Neurosciences Reports (2026) · doi:10.1002/pcn5.70417 · v1.0 |
4.2 (gemini-3.1-flash-lite) | — |
| 15539 | scored |
Psychometric Evaluation of the Artificial Intelligence Perception Scale for Cancer Patients: A Structural Equation Modeling Approach
Journal of Evaluation in Clinical Practice (2026) · doi:10.1111/jep.70606 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15540 | Screened Out |
Is Higher Academic Workload Associated with AI Dependency? Investigating the Roles of Academic Anxiety, Procrastination, and Satisfaction with AI
Psychology Research and Behavior Management (2026) · doi:10.2147/prbm.s634780 · v1.0 |
3.0 (gemini-3.1-flash-lite) | — |
| 15541 | scored |
EXPLORING THE POTENTIALS OF ARTIFICIAL INTELLIGENCE TOWARDS ENTREPRENEURIAL INTENTIONS AMONG STUDENTS IN DELTA STATE
Spectrum Journal of Social Science. (2026) · doi:10.61552/sjss.2026.03.005 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15542 | scored |
511. Multimodal AI for depression detection: integrating facial expressions and semantic analysis in outpatient care
The International Journal of Neuropsychopharmacology (2026) · doi:10.1093/ijnp/pyag040.157 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 15543 | scored |
Yapay Zeka Çağında 21. Yüzyıl Becerileri ve Değerlendirilmesi
arXiv/Crossref (2026) · doi:10.71284/norm.2026155197 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15544 | scored |
How Undergraduates Interact with a Generative AI Peer: Eight Participation Roles and the Conditions for Effective Collaboration in University Mathematics
Research Square (2026) · doi:10.21203/rs.3.rs-9851111/v1 · v1.0 |
0.0 (Flash Lite) | — |
| 15545 | Screened Out |
Psychometric validation of the AI academic phobia scale for pre-service teachers: A confirmatory factor analysis
Journal of Pedagogical Sociology and Psychology (2026) · doi:10.33902/jpsp.202646699 · v1.0 |
3.5 (gemini-3.5-flash-lite) | — |
| 15546 | Screened Out |
TEST MANUAL: KUAI-Kids—Knowledge, Use, and Attitudes toward AI Chatbots Instrument for Kids
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.22227527 · v1.0 |
3.5 (gemini-3.5-flash-lite) | — |
| 15547 | scored |
Artificial Intelligence Tools and Student Learning Outcomes in Open and Distance Learning in South-South Region, Nigeria
WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY (2026) · doi:10.56201/wjimt.v9.no12.2025.pg18.28 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15548 | In Buffer |
Feature or Leak? Guarding an LLM-Driven NPC Against Answer Leakage in an Educational Game
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23150868 · v1.0 |
4.0 (gemini-3.6-flash) | — |
| 15549 | scored |
Design and Implementation of an Intelligent-Based Chatbot for Al-Hikmah University
FUDMA Journal of Sciences (2026) · doi:10.33003/fjs-2026-1019-6161 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15550 | Not Article |
Comment on egusphere-2026-3957
arXiv/Crossref (2026) · doi:10.5194/egusphere-2026-3957-rc2 · v1.0 |
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