Systematic Review Flow (PRISMA 2020)
Transparent flow of article identification, LLM screening, full-text eligibility assessment, and inclusion into living synthesis with expert validation.
✓ PRISMA funnel conservation verified (100% stage closure)
Zero data leakage: n_in = n_out + ∑n_excl + n_pending verified across identification, screening, eligibility, and synthesis.
ID: 17315 →
Screen: 16299 →
Eligible: 2428 →
Included: 84
Expert Validation & Taste Loop
Independent of the formal PRISMA selection rules: editorial verdicts by the curator to calibrate quality and active learning.
👍 10
Approved in digest
(14 total in corpus)
👎 29
Rejected with reason
(1 pre / 18 post analysis)
Breakdown of expert rejection reasons:
not_novel: 4
not_for_educators: 3
off_topic: 13
too_narrow: 2
duplicate: 5
other: 1
weak_method: 1
📋 Structured Review Summary (PRISMA 2020: Item 2)
Background: Living systematic review of Large Language Models (LLMs) and autonomous agents in higher education. Focuses on engineering countermeasures to academic dishonesty and redesign of assessment.
Objectives: Systematic identification, screening, and synthesis of empirical evidence regarding reliability, methodological rigor, and consensus challenges in higher education GenAI integration.
Eligibility Criteria: Scholarly works with open-access full text; topic relevance R ≥ 4.0; weighted final score ≥ 5.0.
Information Sources: Multi-register tracking: arXiv, Crossref, OpenAlex, Semantic Scholar, ERIC, plus forward/backward citation loops (depth 1–2).
Risk of Bias & Quality: Multi-criteria methodological rigor (M, 0–10). Screening reliability validated via dual evaluation: Gwet's AC1, stratified recall, and ROC AUC.
Synthesis & Included: Included n = 84 verified works into the living review with extracted claims, limitations, and empirical findings.
🎯 PICO / PECO Search & Eligibility Framework (PRISMA 2020: Items 4–5)
| Component | Protocol Definition | Inclusion / Exclusion Boundaries |
|---|---|---|
| P (Population) | Higher education: universities, colleges, undergraduate/graduate students, instructors, and examiners. | Included: higher/tertiary education. Excluded: purely K-12 primary education without transferability. |
| I / E (Intervention / Exposure) | Use of modern LLMs (GPT-4, Claude, Gemini, Llama) and agentic frameworks for exam solving, tutoring, or automated grading. | Included: empirical testing and deployment of LLMs. Excluded: legacy non-generative ML or symbolic AI. |
| C (Comparator) | Traditional non-AI assessments, human baselines, or comparative evaluation of engineering defenses (guardrails, multi-agent validation). | Included: baselines, cohort comparisons, or countermeasures. Excluded: purely speculative essays without comparator. |
| O (Outcomes) | Task performance, exam breach rates, countermeasure resilience, shift in assessment rubrics, cognitive learning outcomes. | Included: empirical accuracy, resilience, rubric difficulty, psychometrics. Excluded: unverified claims. |
| S (Study Design) | Empirical studies: controlled experiments (RCT), quasi-experiments, case studies, psychometric evaluations, benchmark audits. | Included: peer-reviewed/preprint research with empirical data. Excluded: opinion blogs, press releases. |
* Footnote *: Prefilter Mathematical Model, Indicators & Scoring Formula (Rubric v2.3)
Rubric Indicators & Parameters
| Symbol | Indicator Name | Weight / Value |
|---|---|---|
| M | Methodological Rigor | wM = 0.32 |
| E | Engineering Countermeasures | wE = 0.25 |
| C | Consensus Challenge | wC = 0.43 |
| N | Novelty | wN = 0.00 |
| R | Topic Relevance (0–10) | Rmin = 4.0, Rfull = 7.0 |
| Gate | Binary Scope Gate | PASS / FAIL |
| Поріг | Pass Threshold | Score ≥ 5.0 |
Relevance R is a multiplier, not a summand: papers with R < R_min receive 0, and high-quality off-topic papers cannot outrank topical ones.
Scoring Formulae (MathML Core)
1. Final Prefilter Score:
2. Weighted Quality Score (Q):
3. Logical Prefilter Screening Criterion:
Methods & reporting (PRISMA 2020 items 1–15, 23–27)
Review type, objective, eligibility (1, 3–5)
Living systematic review: a daily-updated, automation-assisted selection. It is a literature-selection tool, not a published review with meta-analysis.
Topic (given to the model): Validity of educational assessment under generative AI and how to keep it (AI-resistant / AI-resilient assessment). Core: how generative AI changes what grades and test scores mean (validity arguments in the sense of Kane and Messick), assessment redesign, and quantitative methods that detect or model the distortion in assessment and learning data: item response theory, differential item functioning between humans and LLMs, item parameter drift, rater drift, item compromise and preknowledge, score shifts under unsupervised or online conditions (proctored vs unproctored exams, cheating, grade inflation), change-point and concept-drift detection, LLMs as test takers or raters, computerized adaptive testing and automated test assembly. Computational, statistical and engineering work is preferred, but a solid conceptual or empirical education study of assessment under generative AI belongs here too. Detectors of AI-generated text and watermarking are only adjacent to the topic. The audience is university educators and researchers: work about primary and secondary school (K-12) is off topic unless its findings or methods clearly transfer to higher education.
- Include: a scholarly article with topic relevance R ≥ 4.0, final score ≥ 5.0; a legal open-access full text that was actually read.
- Exclude: non-articles (datasets, code, errata), duplicates and preprints of published works, retracted works, K-12 with no transfer to higher education, works without full text (paywall, fetch errors), expert-rejected works (👎, reason recorded).
- Manual additions (origin = manual) bypass score screening but go through eligibility assessment.
Information sources & last searched (6)
| Channel | Records | Last record |
|---|---|---|
search — arXiv, OpenAlex, Semantic Scholar, ERIC | 15497 | 2026-11-07 10:23 |
ref — reference lists of read works | 1791 | 2026-11-07 10:25 |
manual — added manually | 27 | 2026-10-07 12:05 |
Citation candidates take metadata from OpenAlex; Crossref/Unpaywall supply DOIs, versions and open PDFs.
Search strategy (7)
Current search version: v1.0. A work matches if its title/abstract hits a term from EVERY group of at least one query (OR within a group; case-insensitive, word boundaries).
Queries (groups):
education × ai; psychometrics × ai; integrity × education; drift × education; drift × psychometrics
education: education, educational, student, learner, teacher, lecturer, classroom, university, higher education, school, curriculum, assessment, exam, examination, examinee, test taker, test score, grading, homework, coursework, online course, essay, academic integrity, academic misconduct, plagiarism, cheating, e-learning, MOOC
ai: generative AI, GenAI, large language model, LLM, ChatGPT, GPT-4, chatbot, AI-generated, AI-assisted, AI-resistant, AI-resilient, AI tool, artificial intelligence
integrity: proctoring, proctored, unproctored, non-proctored, remote proctoring, online exam, online test, online testing, exam integrity, testing integrity, academic integrity, academic dishonesty, cheating, contract cheating, grade inflation
psychometrics: item response theory, IRT, Rasch, psychometric, educational measurement, educational testing, differential item functioning, computerized adaptive testing, adaptive testing, automated test assembly, knowledge tracing, automated scoring, automated essay scoring, validity argument, construct validity, assessment validity, predictive validity
drift: parameter drift, dynamic item response, item parameter drift, rater drift, severity drift, concept drift, change point, change-point detection, item compromise, preknowledge, item exposure, test security, aberrant response, person-fit, differential item functioning
Semantic Scholar phrases: assessment validity generative AI; AI-resilient assessment higher education; AI-resistant assessment design; generative AI exam performance natural experiment; differential item functioning large language models humans; LLM as test taker item response theory; proctored versus unproctored online exam scores; generative AI grade inflation; item parameter drift detection; item compromise preknowledge detection; concept drift knowledge tracing; change point detection educational testing
arXiv: cs.CY, cs.CL, cs.HC, cs.AI, cs.LG, stat.AP, stat.ME. Result caps per query: OpenAlex 50, Semantic Scholar 25, ERIC 25. Citation searching: depth 1 from read and 👍 works, depth 2 from 👍 only; neighbours of 👍 works bypass the lexical filter.
| Search version | Records (search) | First | Last |
|---|---|---|---|
v1.0 | 15497 | 2026-10-06 | 2026-11-07 |
Selection process & automation (8, 9, 11)
- Abstract screening: a single automated reviewer — an LLM (gemini-3.1-flash-lite, gemini-3.5-flash-lite), batches of 20, models alternate; every evaluation is stored (prefilter_evaluations) with model and rubric version.
- Full-text analysis and data collection: gemini-3.8-flash, gemini-3.7-flash, gemini-3.6-flash, prompt
v2.1. Only after PDF→Markdown conversion; the model is never called without full text. - Verification: code checks every quote against the text (exact match after normalisation); unconfirmed claims are discarded.
- Human: no independent second reviewer. The owner gives 👍/👎 verdicts on a sample (50% near threshold, 30% top, 20% random); they steer the queue and few-shot examples but do not change the score.
- Data items (10): summary, method/data/sample, claims, limitations, study design (assessment_design, psychometric_modeling, natural_experiment, detector_evaluation, survey, rct, quasi_experiment, qualitative, theoretical, review, other), tags, rubric criteria.
- Risk of bias (11): not assessed formally (RoB, GRADE not applied); criterion M (methodological rigor, 0–10) is a ranking score, not a RoB tool. No effect measures or meta-analysis (12, 13d–f, 15): synthesis is qualitative, via article analyses and tags.
| Screening model | Scored |
|---|---|
gemini-3.1-flash-lite | 8071 |
gemini-3.5-flash-lite | 7675 |
— | 619 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 175 |
gemini-3.8-flash | v2.1 | 108 |
gemini-3.7-flash | v2.1 | 100 |
Scoring rubric v2.3 (0–10 anchors)
The model scores 0–10 per criterion; the anchors below are embedded in the prompt. Formula and weights are in the footnote above. Changing anchors or weights without a new version is blocked by a hash check.
| Criterion | Weight | 2 | 5 | 8 |
|---|---|---|---|---|
| Topic relevance (R) | × min(1, R/7.0) | No link to education or assessment: image, audio or model watermarking, model IP protection, general NLP benchmarks, AI in industry. | Adjacent: generative AI in education without an assessment focus; academic integrity with no evidence on assessment outcomes; detectors of AI-generated text or of student AI use; general psychometrics (adaptive testing, test assembly, IRT) with no drift, validity or AI angle. | Core: validity of assessment under generative AI (what grades and scores mean when AI is available); how grades shift under unsupervised conditions or cheating (proctored vs unproctored exams, grade inflation); AI-resistant or AI-resilient assessment design; quantitative methods that detect or model distorted assessment or learning data (DIF between humans and LLMs, item parameter or rater drift, item compromise/preknowledge, change-point or concept-drift detection, LLMs as test takers or raters). |
| Methodological rigor (M) | 0.32 | Vague evaluation, convenience sample of a few dozen, self-report only, no control group or baseline. | Clear dataset or sample (hundreds), standard metrics or validated instruments, comparison with baselines or a control group. | Randomized/controlled or preregistered, large or multi-site/multilingual data, adversarial or robustness checks, effect sizes with uncertainty. |
| Engineering countermeasures (E) | 0.25 | No method or countermeasure: describes impact, attitudes or policy only. | A concrete method or countermeasure with an evaluation: statistical test or model, detector, scoring procedure, or an assessment design tried on real cohorts. | A rigorous, reusable method: formal model with guarantees or error control, open code, validated on both real and simulated data. |
| Consensus challenge (C) | 0.43 | Reconfirms standard wisdom. | Nuances the consensus (e.g. detectors fail under mild paraphrasing; AI use does not harm an outcome it is assumed to harm). | Directly refutes high-profile claims with rigorous evidence. |
| Novelty (N) | 0.00 | Replicates well-known findings (another survey of student attitudes to ChatGPT, another test of one detector on a handful of prompts). | New dataset, benchmark, population or setting, or a previously untried technique. | Fundamentally new attack or defence, a theoretical/impossibility result, or a finding that changes how the field frames the problem. |
The model interpolates between anchors. The deep-analysis score is computed by code with the same formula from criteria_breakdown.
| Rubric version | Papers scored |
|---|---|
v2.0 | 261 |
v2.1 | 183 |
v2.2 | 20 |
v2.3 (current) | 15901 |
Screening Validation: Reliability & Agreement Metrics
Reference standard: expert verdicts. Full PRISMA systematic review metrics computed: recall, specificity, NPV, F₁, F₂, ROC AUC, DOR, paradox-immune Gwet's AC₁, and continuous score agreement.
| Measure | Value | Meaning |
|---|---|---|
| Seed-set recall | 50% (1/2), 95% CI 9–91% | Share of manually added 👍 works (bypassing screening) scoring ≥ threshold 5.0. Unbiased sensitivity estimate on this set. |
| Estimated corpus recall | 100% (100–100%) | Post-stratified by score band from judged works; conservative interval. Relevant works missed ≈ 0 (0–0). Not measured (too few verdicts in band): 13898 screened-out works. |
| Sensitivity / Recall | 100% (12/12), 95% CI 76–100% | Share of relevant works passed by screening: TP / (TP + FN). |
| Specificity | 20% (4/20), 95% CI 8–42% | Share of irrelevant works rejected by screening: TN / (TN + FP). |
| Precision / PPV | 43% (12/28), 95% CI 27–61% | Share of expert-approved among screening passes: TP / (TP + FP). |
| Negative Predictive Value (NPV) | 100% (4/4), 95% CI 51–100% | Share of truly irrelevant among screened-out works: TN / (TN + FN). Protects against losing relevant papers. |
| F-scores (F₁ and F₂) | F₁: 0.60 · F₂: 0.79 | F₂ weights recall twice as much as precision — critical safety metric against false exclusions in PRISMA. |
| Balanced Accuracy | 60.0% | Arithmetic mean of sensitivity and specificity, unaffected by class imbalance. |
| Diagnostic Odds Ratio (DOR) | 6.82 | DOR = (TP · TN) / (FP · FN) with Haldane-Anscombe correction. Overall discriminative capability of screening. |
| ROC AUC | 0.65 (95% CI 0.43–0.84) | Probability a 👍 work outscores a 👎 one (0.5 = chance). CI computed via DeLong / Bootstrap. |
| Inter-Rater Agreement (Gwet's AC₁ & Cohen's κ) | — | Gwet's AC₁ is paradox-immune inter-rater agreement on double-scored works, robust to severe class imbalance (80-90% screening rejection). |
| Continuous Score Agreement |
MAD: — r Пірсона: — |
Mean Absolute Difference (MAD) and Pearson r between scores of screening models on shared papers. |
| Score band | Works | Judged | 👍 | 👍 rate |
|---|---|---|---|---|
| 0.0–2.0 | 8987 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3747 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1164 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1325 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1049 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 30 | 2 (too few) | 2 | 100% |
| Screening model | Seed recall | AUC | Sens / Spec | Prec / NPV | F₁ / AC₁ | 👍 / 👎 (TP/FP/FN/TN) |
|---|---|---|---|---|---|---|
gemini-3.1-flash-lite |
50% (1/2), 95% CI 9–91% | 0.63 | 100% (12/12), 95% CI 76–100% / 16% (3/19), 95% CI 6–38% | 43% (12/28), 95% CI 27–61% / 100% (3/3), 95% CI 44–100% | 0.60 / 0.05 | 12 / 19 (12/16/0/3) |
gemini-3.5-flash-lite |
— | — | — / 100% (1/1), 95% CI 21–100% | — / 100% (1/1), 95% CI 21–100% | — / 1.00 | 0 / 1 (0/0/0/1) |
— |
— | — | — / — | — / — | — / — | 0 / 0 (0/0/0/0) |
To narrow intervals, judge works from lower score bands: /admin/taste includes 20% random picks. Metrics update automatically with each verdict.
Methodological Rigor & Study Designs (11, 18)
Methodological rigor M (0–10) is evaluated during full-text analysis. Study design distribution reflects verified empirical classifications.
Mean Rigor (M)
6.55 / 10
Analyzed papers: 648
Quality Stratification
🟢 High rigor (M ≥ 7.0): 324
🟡 Moderate rigor (4.0 ≤ M < 7.0): 305
🔴 Low rigor (M < 4.0): 19
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 249 |
other | 45 |
theoretical | 27 |
rct | 13 |
assessment_design | 171 |
natural_experiment | 5 |
quasi_experiment | 56 |
detector_evaluation | 43 |
survey | 7 |
review | 20 |
experimental | 1 |
qualitative | 11 |
PRISMA 2020 27-Item Compliance Checklist
Full audit of compliance with PRISMA 2020 checklist (Page et al., 2021) for automated systematic reviews.
| № | Section / Item | Checklist Description | Status | Implementation |
|---|---|---|---|---|
| 1 | Title | Identify as living systematic review | ✓ Впроваджено | Page title, report metadata |
| 2 | Abstract | Structured summary (background, objectives, methods, results) | ✓ Впроваджено | #prisma-abstract block above |
| 3 | Rationale | Rationale in context of existing knowledge | ✓ Впроваджено | Topic description and rationale |
| 4 | Objectives | Explicit objectives using PICO / PECO | ✓ Впроваджено | #pico-framework table above |
| 5 | Eligibility criteria | Inclusion/exclusion criteria, language, dates | ✓ Впроваджено | Thresholds R, Score, Open Access |
| 6 | Information sources | Databases, registers, search dates | ✓ Впроваджено | arXiv, Crossref, OpenAlex, Semantic Scholar, ERIC, citations |
| 7 | Search strategy | Full search strategy, boolean filters, versions | ✓ Впроваджено | search_version, term groups, API limits |
| 8 | Selection process | Selection process, automation, independent screening | ✓ Впроваджено | LLM screening rotation + editorial taste loop |
| 9 | Data collection | Data collection from full-text reports | ✓ Впроваджено | PDF → MD parser, deep prompt v2.1 |
| 10a-b | Data items | List of outcomes and study characteristics | ✓ Впроваджено | Summary, sample, design, claims, countermeasures |
| 11 | Study risk of bias | Assessment of study methodological quality | ✓ Впроваджено | Rigor criterion M (0–10), quality distribution |
| 12 | Effect measures | Effect measures for outcomes | — Обґрунтовано N/A | Qualitative synthesis without meta-analytic pooled effects |
| 13a-f | Synthesis methods | Synthesis criteria and grouping methods | ✓ Впроваджено | Tag-based clustering, comparative study cards |
| 14 | Reporting bias | Assessment of reporting/publication bias | ✓ Впроваджено | Analysis of paywall exclusions & open access skew |
| 15 | Certainty assessment | Certainty assessment of body of evidence | ✓ Впроваджено | Automated quote grounding against full-text Markdown |
| 16 | Study selection | Selection results (flow diagram, counts) | ✓ Впроваджено | 100% stage conservation closure verified |
| 17 | Study characteristics | Characteristics of included studies table | ✓ Впроваджено | Interactive table with DOI, venue, year, tags |
| 18 | Risk of bias in studies | Risk of bias findings across studies | ✓ Впроваджено | Methodological rigor M & study designs table |
| 19 | Results of individual studies | Results for each individual study | ✓ Впроваджено | Public paper pages /doi/{doi} with full analysis |
| 20a-d | Results of syntheses | Results of syntheses & sub-groups | ✓ Впроваджено | Daily living editions, RSS feed, tag filters |
| 21 | Reporting biases | Results of reporting biases assessment | ✓ Впроваджено | Paywall n=342, retracted n=0 |
| 22 | Certainty of evidence | Summary of evidence certainty | ✓ Впроваджено | Gwet's AC₁, Cohen's κ, stratified recall, ROC AUC |
| 23a-d | Discussion | Discussion, limitations, implications | ✓ Впроваджено | Limitations section (23b, 23c) in methods |
| 24a-c | Registration & protocol | Registration, protocol availability, amendments | ✓ Впроваджено | Public repo, version history of search & rubric |
| 25 | Support | Financial & non-financial support | ✓ Впроваджено | Free API tiers declared in methods |
| 26 | Competing interests | Competing interests statement | ✓ Впроваджено | Declared in methods section |
| 27 | Data & code availability | Availability of data, analytical code, materials | ✓ Впроваджено | Export to CSV / JSON buttons, open-source code |
Limitations (23b, 23c)
- Open access only: paywalled papers never reach the synthesis, so the sample skews to open and preprint work.
- Screening ranks by quality and novelty rather than pure eligibility, so the sample skews towards counter-consensus work. Validity is measured against one expert (see above); a single LLM reviewer, model bias and version differences remain possible.
- The lexical filter and result caps can miss works with other terminology; sources skew English-language.
- Records scored under an older rubric version are not rescored automatically (see version table).
- Free Gemini quota: daily limits slow analysis; some selected works wait in the queue.
Registration, protocol, amendments, support, data (24–27)
- Registration: not registered. The protocol is the configuration (topic, queries, rubric, thresholds) in the project repository; it is reproduced in this block.
- Amendments: search versions (search_version) and rubric versions (rubric_version), tables above.
- Funding: none declared, free API tiers are used. Competing interests: none declared.
- Data: analyses and metadata are on article pages, RSS and sitemap; full texts are private (copyright) and not published.
Filter by Tags
Select one or more tags to refine the flow numbers and article corpus.
#psychometrics (104)
#Psychometrics (62)
#Item Response Theory (50)
#academic integrity (48)
#Assessment Validity (39)
#assessment validity (39)
#item response theory (38)
#higher education (37)
#Automated Essay Scoring (31)
#automated essay scoring (31)
#Generative AI (29)
#construct validity (26)
#Construct Validity (24)
#IRT (24)
#LLM as rater (22)
#generative AI (20)
#Academic Integrity (19)
#Higher Education (19)
#Large Language Models (18)
#Automated Scoring (17)
#LLM Evaluation (17)
#assessment redesign (17)
#assessment_design (16)
#large language models (16)
#prompt engineering (16)
#automated scoring (15)
#AI-resilient assessment (14)
#GPT-4 (14)
#LLM as Rater (14)
#validity (14)
#Assessment Redesign (13)
#ChatGPT (13)
#assessment design (13)
#automated assessment (13)
#differential item functioning (13)
#Prompt Engineering (12)
#academic_integrity (12)
#LLMs as raters (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#inter-rater reliability (11)
#Automated Item Generation (10)
#LLM-as-a-judge (10)
#LLMs as Test Takers (10)
#item_response_theory (10)
#Computerized Adaptive Testing (9)
#DIF (9)
#Inter-Rater Reliability (9)
#LLM evaluation (9)
#automated_essay_scoring (9)
#concept drift (9)
#formative assessment (9)
#rater drift (9)
#AI-resistant assessment (8)
#Assessment Design (8)
#Computer Science Education (8)
#Differential Item Functioning (8)
#Knowledge Tracing (8)
#Validity (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 5451 | scored |
Artificial Intelligence-Mediated Active Methodologies and Meaningful Learning in University Students: A Systematic Literature Review
Research Square (2026) · doi:10.21203/rs.3.rs-10273239/v1 · v1.0 |
0.0 (Flash Lite) | — |
| 5452 | scored |
From threat to tool: AI self-efficacy and user profiles in higher education
Knowledge Management & E-Learning An International Journal (2026) · doi:10.34105/j.kmel.2026.18.017 · v1.0 |
5.0 (gemini-3.5-flash-lite) | — |
| 5453 | scored |
Efficiency or intensification? A systematic review of generative AI and academic workload in higher education
Acta Psychologica (2026) · doi:10.1016/j.actpsy.2026.107353 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5454 | scored |
Penguatan Karakter Generasi Muda: Sosialisasi Kode Etik, Anti Plagiasi, Anti Perundungan, dan Anti Narkoba di Lingkungan Pendidikan
Jurnal Pengabdian kepada Masyarakat Bina Darma (2026) · doi:10.33557/x3qfq702 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5455 | scored |
Beyond academic use: Emotional reliance, social withdrawal, and anxiety in university students’ narratives of ChatGPT
Health Psychology Research (2026) · doi:10.36922/hpr.0415 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5456 | scored |
A Quantitative Assessment of Senior High School Students' Satisfaction with the Utilization of Artificial Intelligence in Thesis Writing
Psychology and Education A Multidisciplinary Journal (2026) · doi:10.70838/pemj.590703 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5457 | scored |
Redefining the Roles of Educators and Students in the AI Era
IGI Global eBooks (2026) · doi:10.4018/979-8-2600-2551-2.ch011 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5458 | scored |
Child Legitimacy: Class President 2
arXiv/Crossref (2026) · doi:10.17605/osf.io/6pyzs · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5459 | scored |
PERCEPTIONS AND EXPERIENCES OF SENIOR ENGLISH TEACHERS INTEGRATING ARTIFICIAL INTELLIGENCE INTO ELT PRACTICES
FRASA English Education and Literature Journal (2026) · doi:10.47701/frasa.v7i2.6036 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5460 | scored |
Editorial: The dynamics of workplace violence: emotions, abusive relationships, and response in organizations
Frontiers in Psychology (2026) · doi:10.3389/fpsyg.2026.1913769 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5461 | scored |
Assessment of Academic Integrity among Nursing Students of Ebonyi State University Abakaliki, Nigeria
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21273846 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5462 | scored |
Grammarly: A Modern Ally in Language Teaching. From NLP Theory to Classroom Practice
International Journal of Education and Information Technologies (2026) · doi:10.46300/9109.2026.20.15 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5463 | scored |
Intimate Partner Violence, Ambiguous and Non‐Consensual Non‐Monogamy, and Breaking Up During Young Adulthood
Journal of Marriage and the Family (2026) · doi:10.1111/jomf.70098 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5464 | scored |
Mapping the landscape of undergraduate artificial intelligence use in higher education: a scoping review
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1851969 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5465 | scored |
A Commentary on Accounting Education in Transition: Reflections on What 800+ Students Taught Us About ChatGPT
Accounting Perspectives (2026) · doi:10.1111/1911-3838.70031 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5466 | scored |
Artificial Intelligence in University English Classes in Taiwan: Opportunities, Risks, and a Context-Sensitive Framework for Integration.
World Journal of Advanced Research and Reviews (2026) · doi:10.30574/wjarr.2026.31.1.1867 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5467 | scored |
THE ROLE OF DIGITAL DEVICES AND AI-BASED TOOL IN ENHANCING SELF-DIRECTED LEARNING AMONG EFL UNDERGRADUATE STUDENTS OF FERGANA STATE UNIVERSITY
International Journal of Literature and Language Studies (2026) · doi:10.5281/zenodo.21279452 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5468 | scored |
An Investigation of Teachers’ Perspectives on Utilizing Artificial Intelligence in IELTS Writing Assessment
International Journal of Social Science Humanity & Management Research (2026) · doi:10.58806/ijsshmr.2026.v5i7n01 · v1.0 |
5.3 (gemini-3.5-flash-lite) | — |
| 5469 | scored |
Embracing Generative AI in Initial Teacher Education: Research, Impact and Innovation
Figshare (2026) · doi:10.48420/32942540 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5470 | scored |
Role of library professionals in protecting research integrity: A review
IP Indian Journal of Library Science and Information Technology (2026) · doi:10.18231/j.ijlsit.16745.1781447422 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5471 | Screened Out |
Clustering generative AI usage among university students based on productivity, verification, and integrity
JISTecS Journal of Information Systems Technology and Security (2026) · doi:10.65881/jistecs.v1i2.116 · v1.0 |
2.5 (gemini-3.1-flash-lite) | — |
| 5472 | Screened Out |
Measuring affective development in AI-integrated classrooms: development and validation of a multidimensional scale
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1791052 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 5473 | scored |
Measurement
arXiv/Crossref (2026) · doi:10.31234/osf.io/rg765_v1 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5474 | scored |
Trust Over Creepiness: AI Chatbot Perceptions in Balkan Countries
International Journal of Human-Computer Interaction (2026) · doi:10.1080/10447318.2026.2638548 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5475 | scored |
Artificial intelligence and machine learning in assessing and promoting health and well‐being: Integrating human insight with computational intelligence
Applied Psychology Health and Well-Being (2026) · doi:10.1111/aphw.70140 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5476 | In Buffer |
K-12EduBench: A Benchmark for Evaluating Large Language Models’ Knowledge, Problem-Solving, and Educational Goal Cognition in K-12 Education
Proceedings of the AAAI Conference on Artificial Intelligence (2026) · doi:10.1609/aaai.v40i40.40744 · v1.0 |
3.8 (gemini-3.6-flash) | — |
| 5477 | Screened Out |
IntelliProof: An Argumentation Network-based Conversational Helper for Organized Reflection
Proceedings of the AAAI Conference on Artificial Intelligence (2026) · doi:10.1609/aaai.v40i48.42345 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 5478 | In Buffer |
From Diagnosis to Generalization: A Cognitive Approach to Data Selection for Educational LLMs
Proceedings of the AAAI Conference on Artificial Intelligence (2026) · doi:10.1609/aaai.v40i26.39300 · v1.0 |
7.0 (gemini-3.6-flash) | — |
| 5479 | Screened Out |
MAGIC: Multi-Agent Argumentation and Grammar Integrated Critiquer
Proceedings of the AAAI Conference on Artificial Intelligence (2026) · doi:10.1609/aaai.v40i47.41506 · v1.0 |
4.8 (gemini-3.1-flash-lite) | — |
| 5480 | scored |
Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space
Proceedings of the AAAI Conference on Artificial Intelligence (2026) · doi:10.1609/aaai.v40i17.38494 · v1.0 |
5.2 (gemini-3.1-flash-lite) | — |
| 5481 | In Buffer |
Assessing the Quality of AI-Generated Exams: A Large-Scale Field Study
Proceedings of the AAAI Conference on Artificial Intelligence (2026) · doi:10.1609/aaai.v40i45.41205 · v1.0 |
6.6 (gemini-3.8-flash) | — |
| 5482 | In Buffer |
AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition
Proceedings of the AAAI Conference on Artificial Intelligence (2026) · doi:10.1609/aaai.v40i48.42123 · v1.0 |
6.3 (gemini-3.7-flash) | — |
| 5483 | Screened Out |
The Relationship between Generative AI Use Anxiety and College Students’ Innovation Competence: The Mediating Role of Cognitive Load
Information (Koganei) (2026) · doi:10.47880/inf2901-11 · v1.0 |
1.7 (gemini-3.1-flash-lite) | — |
| 5484 | Screened Out |
A DESIGN FRAMEWORK FOR PERSONALIZED INTELLIGENT TUTORING SYSTEMS INTEGRATING KNOWLEDGE TRACING AND RETRIEVAL-AUGMENTED GENERATION
Journal of Theoretical and Applied Information Technology (2026) · doi:10.5281/zenodo.19136666 · v1.0 |
4.0 (gemini-3.1-flash-lite) | — |
| 5485 | Screened Out |
From Writing to Research: Process-Based Authorship in AI-Assisted Literature Review, Data Analysis, and Scientific Inquiry
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.19036609 · v1.0 |
4.0 (gemini-3.1-flash-lite) | — |
| 5486 | Screened Out |
Examining the Mediating Roles of Digital Literacy and Critical Thinking in the Relationship between ChatGPT Use and University Students’ Mathematical Problem-Solving Efficacy
Journal of mathematics science and technology education. (2026) · doi:10.12973/jmste.3.1.1 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 5487 | scored |
Data and analysis scripts for "Performance within the Foundation Paradox: The Case for Small, Qualitative Evaluations" (Neural Computing and Applications, accepted 2026).
arXiv/Crossref (2026) · doi:10.17605/osf.io/ruafc · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5488 | scored |
AI-assisted navigation (iNav) to improve diagnosis-to-treatment interval and research enrollment in patients with suspected pancreatic cancer.
JCO Oncology Practice (2026) · doi:10.1200/op.2026.22.10_suppl.753 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5489 | scored |
Clinical performance and practical limitations of large language models in respiratory medicine
Breathe (2026) · doi:10.1183/20734735.0125-2026 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5490 | scored |
Regulatory Compliance for AI-Powered IoT in Telemedicine and Remote Monitoring
BENTHAM SCIENCE PUBLISHERS eBooks (2026) · doi:10.2174/9798898816872126010012 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 5491 | scored |
Using artificial intelligence (AI) to enhance collection of patient natural language adverse events (AEs) post-ambulatory cancer surgery.
JCO Oncology Practice (2026) · doi:10.1200/op.2026.22.10_suppl.443 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5492 | scored |
Caring for the Person's Mirror: The Entrustment of the Data‐Person as a Nursing Theory for the Algorithmic Age
Nursing Philosophy (2026) · doi:10.1111/nup.70130 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5493 | scored |
Drone based crop monitoring system
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2026) · doi:10.55041/ijsrem67908 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5494 | scored |
Adaptive learning strategies in the age of artificial intelligence: development and validation of a scale for gifted students
Current Psychology (2026) · doi:10.1007/s12144-026-10137-1 · v1.0 |
0.0 (Flash Lite) | — |
| 5495 | scored |
The emerging role of large language models in oncology care delivery: A bibliometric and thematic analysis.
JCO Oncology Practice (2026) · doi:10.1200/op.2026.22.10_suppl.759 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5496 | scored |
Evaluation of goals-of-care documentation quality in oncology using a large language model.
JCO Oncology Practice (2026) · doi:10.1200/op.2026.22.10_suppl.748 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5497 | scored |
From stigma to trust: A paradigm shift for understanding perceptions of individuals with bipolar disorder.
Journal of Experimental Psychology General (2026) · doi:10.1037/xge0001988 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5498 | scored |
A pilot study of large language model (LLM)–derived patient (pt)-friendly summaries derived from consultation notes: Analysis of accuracy, consistency, and readability.
JCO Oncology Practice (2026) · doi:10.1200/op.2026.22.10_suppl.392 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5499 | scored |
Patients’ attitudes toward artificial intelligence across the cancer care continuum: A mixed-methods study.
JCO Oncology Practice (2026) · doi:10.1200/op.2026.22.10_suppl.428 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 5500 | scored |
Insights from artificial intelligence (AI)–powered patient navigation: Proactive identification of practical and emotional needs for people with cancer.
JCO Oncology Practice (2026) · doi:10.1200/op.2026.22.10_suppl.399 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |