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: 17222 →
Screen: 16208 →
Eligible: 2414 →
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 | 15429 | 2026-11-07 04:07 |
ref — reference lists of read works | 1766 | 2026-11-07 02:24 |
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 | 15429 | 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 | 8020 |
gemini-3.5-flash-lite | 7635 |
— | 619 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 169 |
gemini-3.8-flash | v2.1 | 104 |
gemini-3.7-flash | v2.1 | 96 |
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) | 15810 |
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): 13821 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 | 8944 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3725 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1152 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1316 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1044 | 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)
6.57 / 10
Analyzed papers: 634
Quality Stratification
🟢 High rigor (M ≥ 7.0): 322
🟡 Moderate rigor (4.0 ≤ M < 7.0): 294
🔴 Low rigor (M < 4.0): 18
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 245 |
other | 43 |
theoretical | 27 |
rct | 13 |
assessment_design | 168 |
natural_experiment | 5 |
quasi_experiment | 54 |
detector_evaluation | 42 |
survey | 7 |
review | 18 |
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=336, 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 (103)
#Psychometrics (59)
#Item Response Theory (48)
#academic integrity (46)
#assessment validity (38)
#item response theory (38)
#Assessment Validity (37)
#higher education (36)
#automated essay scoring (31)
#Automated Essay Scoring (30)
#Generative AI (28)
#construct validity (26)
#IRT (24)
#Construct Validity (23)
#LLM as rater (21)
#generative AI (20)
#Academic Integrity (19)
#Automated Scoring (17)
#Higher Education (17)
#Large Language Models (17)
#LLM Evaluation (16)
#assessment redesign (16)
#assessment_design (16)
#large language models (16)
#prompt engineering (15)
#AI-resilient assessment (14)
#LLM as Rater (14)
#automated scoring (14)
#GPT-4 (13)
#assessment design (13)
#automated assessment (13)
#differential item functioning (13)
#validity (13)
#Assessment Redesign (12)
#ChatGPT (12)
#Prompt Engineering (12)
#academic_integrity (12)
#LLMs as raters (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#Automated Item Generation (10)
#LLM-as-a-judge (10)
#inter-rater reliability (10)
#item_response_theory (10)
#Computerized Adaptive Testing (9)
#DIF (9)
#Inter-Rater Reliability (9)
#LLM evaluation (9)
#LLMs as Test Takers (9)
#concept drift (9)
#rater drift (9)
#AI-resistant assessment (8)
#Assessment Design (8)
#Differential Item Functioning (8)
#Knowledge Tracing (8)
#Validity (8)
#automated_essay_scoring (8)
#computerized adaptive testing (8)
#construct_validity (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 4451 | failed |
Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)
Proceedings of the AAAI Conference on Artificial Intelligence (2023) · doi:10.1609/aaai.v37i13.26943 · v1.0 |
- | — |
| 4452 | failed |
Studi Persepsi Anak Usia Sekolah Dasar Terhadap Ketidakjujuran Akademik: Refleksi Dan Evaluasi Moral Perilaku Menyontek Di UPT SD Negeri 072 Eno Kecamatana Seko Kabupaten Luwu Utara
Bosowa Journal of Education (2026) · doi:10.35965/bje.v6i2.6164 · v1.0 |
- | — |
| 4453 | failed |
Evaluation of the Quality of Learning Management System With Real-Time Supervision Using ISO/IEC 25010 in Aviation Vocational Education
AIRMAN Jurnal Teknik dan Keselamatan Transportasi (2026) · doi:10.46509/ajtk.v9i1.888 · v1.0 |
- | — |
| 4454 | failed |
From emergency to opportunity: Greek primary school teachers’ journey in digital assessment before, during, and after COVID-19
Ricerche di pedagogia e didattica (2026) · doi:10.60923/issn.1970-2221/22237 · v1.0 |
- | — |
| 4455 | failed |
The Roles and Impacts of Using AI for Critical Reading: A Literature Review
Teaching English as Foreign Language Literature and Linguistics (2026) · doi:10.33752/teflics.v6i1.11883 · v1.0 |
- | — |
| 4456 | failed |
AI Ethics in ChatGPT Usage Among University Students
Jurnal Pendidikan dan Sastra Inggris (2026) · doi:10.55606/jupensi.v6i2.7355 · v1.0 |
- | — |
| 4457 | failed |
Deep Review Preprint Platform System: An Integrated Institutional Design for Digital Publishing in Open Science
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21048754 · v1.0 |
- | — |
| 4458 | failed |
Measuring educational impact of AI and Supplemental Math interventions with Kudu
iPlaces-Kotahi (2026) · doi:10.60950/1cab159c-3408-419a-ada4-7bacdf5d8518 · v1.0 |
- | — |
| 4459 | failed |
Governing Epistemic Risk in Large Language Model Use for Art History and Art Criticism Instruction: A Policy Analysis
arXiv/Crossref (2026) · doi:10.17613/h0576-dh350 · v1.0 |
- | — |
| 4460 | failed |
Addressing fragmentation: A systematic review of risk and remedies associated with AI and English language teaching
Lubelski Rocznik Pedagogiczny (2026) · doi:10.17951/lrp.2026.45.2.217-236 · v1.0 |
- | — |
| 4461 | failed |
The Use of ChatGPT in Academic Writing: A Systematic Literature Review of EFL and Higher Education Contexts
Education and Linguistics Research (2026) · doi:10.5296/elr.v12i1.23888 · v1.0 |
- | — |
| 4462 | failed |
Integrating Artificial Intelligence Proficiency in Undergraduate Psychology Curricula: Opportunities and Challenges: Comment on Robinson et al. (2026)
Psychology Learning & Teaching (2026) · doi:10.1177/14757257261463957 · v1.0 |
- | — |
| 4463 | failed |
ChatGPT in the classroom: balancing transformative opportunities with pedagogical and ethical challenges
HAMKA INSIGHT (2026) · doi:10.51574/hamka.v4i1.124 · v1.0 |
- | — |
| 4464 | failed |
Unpacking L2 teachers’ and learners’ reflections on the hidden challenges of AI-assisted language education
Porta Linguarum Revista Interuniversitaria de Didáctica de las Lenguas Extranjeras (2026) · doi:10.30827/portalin.vi46.37082 · v1.0 |
- | — |
| 4465 | failed |
Ethical Integration of Artificial Intelligence in Teacher Education: Opportunities, Risks and Policy Implications
Eurasian Journal of Education and Psychology (2026) · doi:10.68283/ejep.v1.i1.a09 · v1.0 |
- | — |
| 4466 | failed |
Ethics of Scientific Writing and Publication in Higher Education: A Qualitative Analysis
International Journal of Community Service (IJCS) (2026) · doi:10.55299/ijcs.v5i2.1972 · v1.0 |
- | — |
| 4467 | failed |
Impacto y proyecciones de la Inteligencia Artificial en las universidades y en la educación médica
Revista médica de Chile (2026) · doi:10.4067/s0034-98872026000701096 · v1.0 |
- | — |
| 4468 | Screened Out |
Student and Faculty Adoption of Generative Artificial Intelligence Across Engineering Education From 2023 to 2024
The ASEE Computers in Education (CoED) Journal (2026) · doi:10.18260/b49a-99-96068 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 4469 | scored |
“Equal Rights for Oil and Electricity”? Public Perspectives and Moral Divisions in China’s Vehicle Energy Transition
SAGE Open (2026) · doi:10.1177/21582440261492831 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4470 | scored |
Reef and Siren: A Grounded Theory Study of Cheating Legitimacy Through the Lens of Absolute Recoil
SAGE Open (2026) · doi:10.1177/21582440261490644 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4471 | Screened Out |
Hybrid Lexical and Forensic Linguistic Features for AI-Generated Text Detection Using Machine Learning
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.25673/125322 · v1.0 |
4.2 (gemini-3.5-flash-lite) | — |
| 4472 | scored |
Artificial Intelligence in Teacher Education as a Sociotechnical Negotiation: Evidence From Post-Conflict Liberian Colleges of Education
SAGE Open (2026) · doi:10.1177/21582440261490974 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4473 | Screened Out |
Academic Integrity: The Prevalence and Attitude of Malaysian Clinical Dental Students
Malaysian Dental Journal (2026) · doi:10.4103/mdj.mdj_32_25 · v1.0 |
2.4 (gemini-3.1-flash-lite) | — |
| 4474 | scored |
Behavior-Based Assessment for 21st-Century Professionals
OSF eBooks (2026) · doi:10.17605/osf.io/74avx · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4475 | scored |
Transcultural adaptation and psychometric properties of the Chinese version of the Artificial Intelligence Attitude Scale for Nurses (AIASN)
Research Square (2026) · doi:10.21203/rs.3.rs-6836319/v2 · v1.0 |
0.0 (Flash Lite) | — |
| 4476 | scored |
Assessing Personality Using Zero-Shot Generative AI Scoring of Brief Open-Ended Text
arXiv/Crossref (2026) · doi:10.31234/osf.io/4zx2k_v2 · v1.0 |
5.3 (gemini-3.1-flash-lite) | — |
| 4477 | scored |
Validation of the Chinese version of the scientific imagination inventory in primary school students
Frontiers in Psychology (2026) · doi:10.3389/fpsyg.2026.1757878 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4478 | No Fulltext |
Integrating Artificial Intelligence and Learning Sciences to Reduce Cognitive Load and Achievement Gaps in Data-Driven K-12 Instructional Systems
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21604670 · v1.0 |
6.6 (gemini-3.1-flash-lite) | — |
| 4479 | scored |
AI/ML-Based Demand Forecasting Across Industries: Transforming Traditional Sales Prediction Through Advanced Analytics
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.18483667 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4480 | scored |
Personality-based interpretation of online reviews: an AI-driven reader-centered framework
Tourism Review (2026) · doi:10.1108/tr-08-2025-0939 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4481 | Screened Out |
Psychometric validation of the Turkish Brief General AI Self-Efficacy Scale: examining its role in AI anxiety, acceptance, and demographic predictors
Frontiers in Psychology (2026) · doi:10.3389/fpsyg.2026.1714147 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 4482 | scored |
Optimizing Requirements Coverage in Software Testing Through Large Language Model-Based Scoring
International Informatics and Software Engineering Conference (IISEC) (2026) · doi:10.1109/iisec69317.2026.11418443 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4483 | scored |
Development and Validation of a Multimodal AI Framework for Assessing Craving Intensity and Vedic Personality Traits in Substance Use Disorders
International Journal of Global Mental Health Innovation Policy Action Culture & Transformation (2026) · doi:10.61113/impact.v2i1.1245 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4484 | Screened Out |
Procedural Legitimacy Without Absorption: AI, Functional Illiteracy, Credential Inflation, and the Institutional Misalignment of U.S. Higher Education
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.18498139 · v1.0 |
3.9 (gemini-3.1-flash-lite) | — |
| 4485 | Screened Out |
Can Large Language Model Embeddings Predict Test Item Correlations? A Multilevel Analysis
arXiv/Crossref (2026) · doi:10.31234/osf.io/jh4vg_v1 · v1.0 |
4.7 (gemini-3.1-flash-lite) | — |
| 4486 | Screened Out |
A multi-agent hierarchical framework for students' risk-aware mental health monitoring. SENTIA: State-Enabled Neuro-symbolic Trajectory-aware Intelligent Architecture
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.18496511 · v1.0 |
4.0 (gemini-3.1-flash-lite) | — |
| 4487 | scored |
Integrated AI-driven virtual reality framework for bridge inspection with multimodal NDT and remote sensing
Advances in Structural Engineering (2026) · doi:10.1177/13694332261420441 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4488 | abstract_only |
Automatic item generation for personality situational judgment tests with large language models
Computers in Human Behavior Reports (2026) · doi:10.1016/j.chbr.2026.100964 · v1.0 |
5.6 (gemini-3.1-flash-lite) | — |
| 4489 | scored |
Perceiving AI as an Epistemic Authority or Algority: A User Study on the Human Attribution of Authority to AI
Machine Learning and Knowledge Extraction (2026) · doi:10.3390/make8020036 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4490 | No Fulltext |
Does an LLM-Based Feedback Agent Move the Needle? An Empirical Study of Student Correctness and Hint Dependency on the ASSISTments 2017 Interaction Logs
Journal of Global Engineering Review (2026) · doi:10.66372/jger.v4i1.11 · v1.0 |
5.9 (gemini-3.1-flash-lite) | — |
| 4491 | No Fulltext |
Fine-grained Rubric-Aware Automated Scoring of Programming Short Answer Questions via Large Language Models in Vocational Education
International Conference on Big Data and Informatization Education (ICBDIE) (2026) · doi:10.1145/3806980.3807016 · v1.0 |
5.8 (gemini-3.1-flash-lite) | — |
| 4492 | scored |
The Surgeon in the Loop: Artificial Intelligence, Robotic Surgery, and the Future of Surgical Judgment, Performance, and Training A Systematic Review with Structured Narrative Synthesis
Intelligent Surgery (2026) · doi:10.1016/j.isurg.2026.10.004 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4493 | scored |
Artificial Intelligence in Surgical Education
Clinics in Plastic Surgery (2026) · doi:10.1016/j.cps.2026.09.001 · v1.0 |
0.0 (Flash Lite) | — |
| 4494 | scored |
FROM MOLECULES TO MARKET: PHYSICAL CHEMISTRY TOOLS FOR FOOD INTEGRITY AND CONSUMER TRUST
Workshop: Food and Drug Safety and Quality (2026) · doi:10.68725/fdsq_workshop.2026.9.7 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4495 | scored |
Application of an advanced large language model in post-molecular anamnesis for PR-10 cross-reactivity assessment in birch pollen sensitized patients
Clinica Chimica Acta (2026) · doi:10.1016/j.cca.2026.122522 · v1.0 |
0.0 (Flash Lite) | — |
| 4496 | scored |
Large Language Model‐Driven Throat‐Wearable Sensing System for Real‐Time Recognition and Evaluation of Swallowing Disorders (Adv. Healthcare Mater. 39/2026)
Advanced Healthcare Materials (2026) · doi:10.1002/adhm.71758 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4497 | scored |
Ethical Dilemmas and Humanistic Return Paths in Medical Artificial Intelligence Nursing Practice
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.12014/j.issn.1002-0772.2026.19.06 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4498 | scored |
Artificial Intelligence and Epistemic Dependence in Higher Education
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.34874/b76bkf09 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4499 | scored |
Objective Artificial Intelligence-based Physiotherapeutic Assessment of Upper Cross Syndrome in Healthcare Professionals: A Cross-sectional Study
Medical Journal of Dr D Y Patil Vidyapeeth (2026) · doi:10.4103/mjdrdypu.mjdrdypu_94_26 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4500 | scored |
Confidence at the crossroads: A systematic review of AI literacy, parental influence, and self-confidence in students’ career choice
DOAJ (DOAJ: Directory of Open Access Journals) (2026) · doi:10.17509/ijotis.v6i1.104402 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |