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: 18966 →
Eligible: 2771 →
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 | 9449 |
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 | 263 |
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) | 18568 |
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): 16222 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 | 10498 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 4369 | 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 ✓ | 1182 | 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: 752
Quality Stratification
🟢 High rigor (M ≥ 7.0): 355
🟡 Moderate rigor (4.0 ≤ M < 7.0): 371
🔴 Low rigor (M < 4.0): 26
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 286 |
other | 54 |
theoretical | 27 |
rct | 17 |
assessment_design | 209 |
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=384, 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)
#Automated Essay Scoring (43)
#higher education (43)
#automated essay scoring (40)
#Generative AI (32)
#Construct Validity (31)
#construct validity (27)
#LLM as rater (26)
#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)
#automated item generation (10)
#automated_essay_scoring (10)
#computerized adaptive testing (10)
#item_response_theory (10)
#AI-Resilient Assessment (9)
#DIF (9)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 13051 | scored |
An AI-guided virtual reality framework integrating multimodal NDT and remote sensing for bridge inspection
CRC Press eBooks (2026) · doi:10.1201/9781003778677-232 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13052 | scored |
Artificial Intelligence Mentorship Perception Scale for Preservice Teachers: Development and Preliminary Psychometric Evidence
Journal of Technology Applications in Education (2026) · doi:10.29329/jtae.2026.1432.5 · v1.0 |
5.3 (gemini-3.1-flash-lite) | — |
| 13053 | scored |
Evaluating Human and AI Translation Strategies: A Process-Oriented Comparative Case Study Across Genres
English Language Teaching (2026) · doi:10.5539/elt.v19n8p1 · v1.0 |
5.3 (gemini-3.1-flash-lite) | — |
| 13054 | Not Article |
Replication Data and Code for: Zero-Shot Large Language Model Assessment of Narrative Agency and Communion for Predicting Severe Psychological Stress in Social Media
Figshare (2026) · doi:10.6084/m9.figshare.32923121 · v1.0 |
- | — |
| 13055 | scored |
ITDF Universal KPI Framework: Supplementary Workbook for Evidence-Governed Islamic Theological Dialogue
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21253949 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13056 | Screened Out |
Artificial Intelligence-Associated Psychotic Experiences ("AI Psychosis"): A Scoping Review of Definitions, Proposed Mechanisms, Vulnerability Factors, and Clinical-Preventive Frameworks
arXiv/Crossref (2026) · doi:10.17605/osf.io/eqdm6 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 13057 | Screened Out |
Generative AI and Academic Integrity in Higher Education: Challenges and Future Directions
International Journal of Advanced Research in Science Communication and Technology (2026) · doi:10.48175/ijarsct-37626 · v1.0 |
3.7 (gemini-3.5-flash-lite) | — |
| 13058 | Screened Out |
Mapping the Echo Chamber and Filter Bubble Literature: Intellectual Structure, Thematic Evolution, and Research Gaps A Bibliometric Analysis (2001–2025)
arXiv/Crossref (2026) · doi:10.17605/osf.io/j2zgv · v1.0 |
1.8 (gemini-3.5-flash-lite) | — |
| 13059 | Screened Out |
Perceived Impact of a Simulated AI-Enabled EMR Autofill System on Resident Physician Burnout and Workflow Efficiency: A Research Protocol
Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal (2026) · doi:10.26685/urncst.1041 · v1.0 |
1.7 (gemini-3.5-flash-lite) | — |
| 13060 | No Fulltext |
Agreement, calibration, and failure of three large language models as high-stakes multimodal ospe graders: a comparative psychometric analysis
Medical Education Online (2026) · doi:10.1080/10872981.2026.2684837 · v1.0 |
7.8 (gemini-3.5-flash-lite) | — |
| 13061 | scored |
Artificial intelligence assessment of endoscopic severity and extent: A machine learning approach to continuous evaluation of endoscopic inflammation in ulcerative colitis
Inflammatory Bowel Diseases (2026) · doi:10.1093/ibd/izag135 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13062 | Screened Out |
Using Artificial Intelligence in Mobile Learning for Students of Technical Specialties
International Journal of Interactive Mobile Technologies (iJIM) (2026) · doi:10.3991/ijim.v20i13.60967 · v1.0 |
3.8 (gemini-3.5-flash-lite) | — |
| 13063 | scored |
The legalization of international instruments: a hybrid RAG scoring framework based on chain-of-thought prompting
Frontiers in Artificial Intelligence (2026) · doi:10.3389/frai.2026.1784200 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13064 | scored |
Behavioral Mechanisms of a Digital Health Intervention for Self-Management in Type 2 Diabetes Mellitus: Prospective Longitudinal Cohort Study
JMIR mhealth and uhealth (2026) · doi:10.2196/79081 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13065 | Not Article |
Agreement is not measurement: auditing AMÁLIA-9B as a social-science annotator — data and analysis code
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21275659 · v1.0 |
- | — |
| 13066 | scored |
Analysis of the Validity of ChatGPT in the Assessment of Perceived Stress and Psychological Distress Among University Students: A Cross-Sectional Study
Psychiatric Quarterly (2026) · doi:10.1007/s11126-026-10298-z · v1.0 |
0.0 (Flash Lite) | — |
| 13067 | scored |
Analisis Persepsi Mahasiswa Akuntansi terhadap Peran Sistem Informasi Akuntansi dalam Pencegahan Fraud di Era Digital
SCIENTIFIC JOURNAL OF REFLECTION Economic Accounting Management and Business (2026) · doi:10.37481/sjr.v9i4.1648 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13068 | scored |
Application of Retrieval-Augmented Generation Large Language Models in Stomatology: A Scoping Review
International Dental Journal (2026) · doi:10.1016/j.identj.2026.111195 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13069 | Screened Out |
Teachers’ use of generative artificial intelligence in K–12 education: a systematic review
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1935733 · v1.0 |
2.1 (gemini-3.5-flash-lite) | — |
| 13070 | No Fulltext |
Can LLMs effectively annotate learner writing? Prompting strategies and proficiency effects in annotating L2 learner argumentative essays
System (2026) · doi:10.1016/j.system.2026.104169 · v1.0 |
5.9 (gemini-3.5-flash-lite) | — |
| 13071 | scored |
The Architecture of Demerzel: What Asimov Hid Inside the Three Laws
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23091604 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13072 | Screened Out |
Could AI chatbots mislead orthodontic patients? An assessment from the perspectives of accuracy, risk, and ethics
BMC Oral Health (2026) · doi:10.1186/s12903-026-09894-z · v1.0 |
2.5 (gemini-3.5-flash-lite) | — |
| 13073 | scored |
Associations of metacognition, self-directed learning ability, and AI literacy with academic achievement among university students predominantly enrolled in health-related majors: a cross-sectional study
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1902381 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13074 | scored |
Research trends in post-ERCP pancreatitis: a dual-database bibliometric analysis
Frontiers in Medicine (2026) · doi:10.3389/fmed.2026.1966046 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13075 | scored |
Passes Alone, Fails Together: Benchmarking Semantic Coordination in Parallel LLM-Agent Development
arXiv/Crossref (2026) · doi:10.1145/3842650.3843171 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13076 | scored |
AI-Driven Support For Military Training Documentation Design
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23063251 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13077 | scored |
Artificial Intelligence in Echocardiography: Five Clinical Challenges and a Roadmap Toward Responsible Integration by 2030
Turk Kardiyoloji Dernegi Arsivi-Archives of the Turkish Society of Cardiology (2026) · doi:10.5543/tkda.2026.30071 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13078 | scored |
Next-Generation Modulation and Control of Power Electronic Systems: AI-Enabled Control, Digital Twins, Fault-Tolerant Operation, and Cyber-Physical Security
Electronics (2026) · doi:10.3390/electronics15194522 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13079 | scored |
From Plots to Words: Model-Aware Multimodal Explanations as a Foundation for Accessible, Non-Visual Interaction
Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) (2026) · doi:10.1145/3776591.3833865 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13080 | scored |
Using LLMs for Multi-Criteria Decision Analysis: The Swedish Nord-Syd Case
Electronics (2026) · doi:10.3390/electronics15194519 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13081 | Screened Out |
Code-to-Speech: An Exploratory Study of AI Text-to-Speech for Source Code Readability
INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (2026) · doi:10.1145/3776574.3831223 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 13082 | scored |
Multicenter, Multivendor Development and Validation of Automated Liver Prescription
Journal of Magnetic Resonance Imaging (2026) · doi:10.1002/jmri.70575 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13083 | scored |
COMPARATIVE ANALYSIS OF LISTENER PERCEPTION AND EMOTIONAL RESPONSE TO AI-GENERATED AND HUMAN-COMPOSED MUSIC ACROSS MULTIPLE GENRES
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21757045 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13084 | Screened Out |
How YouTube Frames ChatGPT Use in Education: An Epistemic Network Analysis with Supporting Multimodal Metadata
INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (2026) · doi:10.1145/3776574.3831127 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 13085 | scored |
The AI Productivity Paradox in Software Engineering: A Systematic Review of Code Quality, Technical Debt, and Organizational Throughout (2024–2026)
arXiv/Crossref (2026) · doi:10.66977/xsci.2610.0006 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13086 | scored |
Emerging Applications of Virtual Reality and Artificial Intelligence in Schema Therapy: A Scoping Review
Healthcare (2026) · doi:10.3390/healthcare14193292 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13087 | scored |
Leadership Models, Social Selection, and the Evolution of Western Civilization
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23107967 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13088 | scored |
The constant of doubly uniform regret in one-dimensional online linear regression is three
arXiv (Cornell University) (2026) · doi:10.5281/zenodo.23095139 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13089 | Screened Out |
LLM-Enabled Educational Artefacts in Computer Science Education: A Systematic Mapping Study
Applied Sciences (2026) · doi:10.3390/app16199778 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 13090 | scored |
Is Critical Thinking the Main Educational Goal in the Era of Artificial Intelligence?
Высшее образование в России (2026) · doi:10.31992/0869-3617-2026-35-8-9-70-93 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13091 | scored |
Mining the Self-Rewriting System: A Vision for Data Mining on AI-Native Infrastructure
arXiv (2026) · doi:10.48550/arxiv.2610.25374 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13092 | In Buffer |
A Measurement Perspective on Benchmarking LLM-Generated Text Detectors using Item Response Theory
arXiv (2026) · doi:10.48550/arxiv.2610.24972 · v1.0 |
4.0 (gemini-3.8-flash) | — |
| 13093 | Included |
Inside the Scorer: Mechanistic Evidence on Rubric Control in LLM-Based Automated Essay Scoring from 25 Base Models
arXiv (2026) · doi:10.48550/arxiv.2610.25724 · v1.0 |
7.4 (gemini-3.7-flash) | — |
| 13094 | scored |
SPLASH: Contrastive Audit Pressure for Detecting and Localizing Security Hallucinations in Generated Code
arXiv (2026) · doi:10.48550/arxiv.2610.26001 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13095 | scored |
Scaling Synthetic Pre-training Data and Evaluation for Indic LLMs
arXiv (2026) · doi:10.48550/arxiv.2610.26006 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13096 | scored |
Honeyed Words, Hidden Daggers: Benchmarking and Training LLMs to See Through Mock Politeness
arXiv (2026) · doi:10.48550/arxiv.2610.26059 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13097 | scored |
Small Average Error, Wide Individual Error: General-Purpose LLMs Estimating Energy Intake from Meal Photos in a Feeding Trial
arXiv (2026) · doi:10.48550/arxiv.2610.26538 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 13098 | scored |
Measuring AI Literacy in Elementary Students: Development and Validation of a Psychometric Instrument
Education and Information Technologies (2026) · doi:10.1007/s10639-026-14022-z · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 13099 | Screened Out |
Incorporation of Generative AI into Science Communication Education in Multilingual Environments
Discover Education (2026) · doi:10.1007/s44217-026-01891-2 · v1.0 |
2.3 (gemini-3.5-flash-lite) | — |
| 13100 | scored |
Analysis of Differential Item Functioning on Students’ Mathematical Problem-Solving Ability Assessment Instruments
EDUMATICA | Jurnal Pendidikan Matematika (2025) · doi:10.22437/edumatica.v15i2.44361 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |