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: 17015 →
Screen: 16006 →
Eligible: 2384 →
Included: 81
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 = 81 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 | 15228 | 2026-11-06 18:23 |
ref — reference lists of read works | 1760 | 2026-11-06 10:24 |
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 | 15228 | 2026-10-06 | 2026-11-06 |
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 | 7925 |
gemini-3.5-flash-lite | 7532 |
— | 615 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 163 |
gemini-3.8-flash | v2.1 | 101 |
gemini-3.7-flash | v2.1 | 95 |
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) | 15608 |
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): 13649 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 | 8838 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3675 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1136 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1294 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1036 | 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)
6.59 / 10
Analyzed papers: 624
Quality Stratification
🟢 High rigor (M ≥ 7.0): 320
🟡 Moderate rigor (4.0 ≤ M < 7.0): 287
🔴 Low rigor (M < 4.0): 17
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 240 |
other | 42 |
theoretical | 27 |
rct | 13 |
assessment_design | 165 |
natural_experiment | 5 |
quasi_experiment | 54 |
detector_evaluation | 41 |
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=333, 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 (101)
#Psychometrics (57)
#Item Response Theory (47)
#academic integrity (46)
#item response theory (38)
#Assessment Validity (37)
#assessment validity (37)
#higher education (36)
#Automated Essay Scoring (29)
#Generative AI (28)
#automated essay scoring (27)
#construct validity (26)
#IRT (24)
#Construct Validity (23)
#generative AI (20)
#Academic Integrity (19)
#LLM as rater (19)
#Automated Scoring (17)
#Higher Education (17)
#Large Language Models (17)
#assessment redesign (16)
#assessment_design (16)
#LLM Evaluation (15)
#AI-resilient assessment (14)
#automated scoring (14)
#large language models (14)
#LLM as Rater (13)
#assessment design (13)
#automated assessment (13)
#prompt engineering (13)
#validity (13)
#GPT-4 (12)
#academic_integrity (12)
#differential item functioning (12)
#Assessment Redesign (11)
#ChatGPT (11)
#Prompt Engineering (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#LLM-as-a-judge (10)
#LLMs as raters (10)
#item_response_theory (10)
#Automated Item Generation (9)
#Computerized Adaptive Testing (9)
#DIF (9)
#Inter-Rater Reliability (9)
#LLM evaluation (9)
#LLMs as Test Takers (9)
#concept drift (9)
#inter-rater reliability (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 |
|---|---|---|---|---|
| 2551 | Screened Out |
Enhancing Student Engagement with AI-Driven Icebreaking Techniques in Engineering Courses
IISE Annual Conference & Expo (2026) · doi:10.21872/annual2025_6702 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 2552 | scored |
Application of Artificial Intelligence (AI) For Effective Record Management in Secondary Schools in Cross River State, Nigeria
WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY (2026) · doi:10.56201/wjimt.v10.no1.2026.pg125.140 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 2553 | scored |
Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches
Journal of Advanced Research in Natural and Applied Sciences (2026) · doi:10.28979/jarnas.1942318 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 2554 | failed |
CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection
arXiv (Cornell University) (2026) · doi:10.48550/arxiv.2610.14799 · v1.0 |
- | — |
| 2555 | failed |
PROKRASTINASI AKADEMIK SEBAGAI PREDIKTOR PERILAKU MENYONTEK PADA MAHASISWA PENGGUNA TEKNOLOGI ARTIFICIAL INTELLIGENCE
Journal Ilmiah Rinjani Media Informasi Ilmiah Universitas Gunung Rinjani (2026) · doi:10.53952/jir.v14i1.680 · v1.0 |
- | — |
| 2556 | failed |
Etika Digital dalam Kode Etik Guru: Tren Penelitian dan Implikasinya terhadap Profesionalisme Pendidik pada Era Artificial Intelligence
Didaktik Jurnal Ilmiah PGSD STKIP Subang (2026) · doi:10.36989/didaktik.v12i02.14796 · v1.0 |
- | — |
| 2557 | failed |
A CRITICAL ANALYSIS OF TEACHER PROFESSIONAL COMPETENCIES IN PANCASILA EDUCATION LEARNING IN DEVELOPING STUDENTS' RESPONSIBLE CHARACTERS IN ELEMENTARY SCHOOLS
SOSIOEDUKASI JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL (2026) · doi:10.36526/sosioedukasi.v15i2.8150 · v1.0 |
- | — |
| 2558 | failed |
Knowledge Sharing among University Students in Vietnam: Current Practices and Solutions in the Context of Digital Transformation
RESEARCH REVIEW International Journal of Multidisciplinary (2026) · doi:10.31305/rrijm.2026.v11.n06.008 · v1.0 |
- | — |
| 2559 | failed |
Artificial Intelligence in Higher Education: a systematic review across academic disciplines
Νημερτής (2026) · doi:10.26220/rev.5651 · v1.0 |
- | — |
| 2560 | failed |
The Compatibility of Artificial Intelligence and Hermeneutic Approach in Modern Education
Journal of Universal Science Research (2026) · doi:10.66301/0d665967 · v1.0 |
- | — |
| 2561 | failed |
Educating for academic integrity: effectiveness of a digital resource on plagiarism awareness among postgraduate bioscience students
International Journal for Educational Integrity (2026) · doi:10.1007/s40979-026-00224-y · v1.0 |
- | — |
| 2562 | failed |
Automatic Code Plagiarism Detection using AST and Machine Learning
International Journal of Scientific Research in Science Engineering and Technology (2026) · doi:10.32628/ijsrset2613348 · v1.0 |
- | — |
| 2563 | failed |
Dhafer AlZayani Archive: From Before 2007 to 2026 and Beyond - Complete Works Volumes 1-3
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20689181 · v1.0 |
- | — |
| 2564 | failed |
ANALISIS TINGKAT KEPUASAN PENGGUNA WEBSITE E-LEARNING UINSU MENGGUNAKAN METODE SYSTEM USABILITY SCALE
CENDEKIA Jurnal Ilmu Pengetahuan (2026) · doi:10.51878/cendekia.v6i3.11489 · v1.0 |
- | — |
| 2565 | failed |
Assessment Without Protection: High-Stakes Digital Assessment Environments and the Hidden Risks of Technology-Facilitated Gender-Based Violence Against Girls and Young Women in Schools
International Journal of Instructional Technology and Educational Studies (2026) · doi:10.21608/ihites.2026.471890.1317 · v1.0 |
- | — |
| 2566 | failed |
TRANSFORMASI SISTEM PENDIDIKAN INDONESIA DI ERA DIGITAL: KAJIAN KEBIJAKAN DAN IMPLIKASI SISTEMIK
CENDEKIA Jurnal Ilmu Pengetahuan (2026) · doi:10.51878/cendekia.v6i3.11799 · v1.0 |
- | — |
| 2567 | failed |
Teachers' Attitudes Toward the Use of Artificial Intelligence in English Language Teaching: A Scoping Review With Implications for Bangladesh
Artificial Intelligence in Lifelong and Life-Course Education (2026) · doi:10.66053/aillce.v1i2.35 · v1.0 |
- | — |
| 2568 | failed |
The AI-powered co-inquirer: a systematic review of ChatGPT for inquiry-based learning in STEAM education
International Journal of STEM Education (2026) · doi:10.1186/s40594-026-00628-9 · v1.0 |
- | — |
| 2569 | failed |
ChatGPT as Digital Scaffolding in Argumentative Writing for First-Year Students: A Systematic Literature Review (2020–2025)
EDUKASIA Jurnal Pendidikan dan Pembelajaran (2026) · doi:10.62775/edukasia.v7i1.2125 · v1.0 |
- | — |
| 2570 | failed |
Detecting the Undetectable: A Critical Synthesis of Machine Learning Applications for Academic Dishonesty Identification in Online Learning Environments
Journal of Cyber-Physical Security and Robotics (2026) · doi:10.64235/fmdw2070 · v1.0 |
- | — |
| 2571 | failed |
Editing Software to Improve the Quality of Students’ Scientific Works
Buletin Edukasi Indonesia (2026) · doi:10.56741/iistr.bei.001851 · v1.0 |
- | — |
| 2572 | failed |
Teachers’ AI literacy
E-Structural (2026) · doi:10.33633/es.v9i01.16150 · v1.0 |
- | — |
| 2573 | failed |
Drivers of academic dishonesty and multi-level prevention strategies: an umbrella review
Revista Digital de Investigación en Docencia Universitaria (2026) · doi:10.19083/ridu.2026.2492 · v1.0 |
- | — |
| 2574 | failed |
Unsanctioned Intelligence: Mitigating the Risks of ‘Shadow AI’ and Proprietary Data Leaks in the Remote Workforce
International Journal of Advanced Computer Science and Applications (2026) · doi:10.14569/ijacsa.2026.0170751 · v1.0 |
- | — |
| 2575 | failed |
Calibrating Algorithmic Judgment: A Hybrid Agentic Framework for Subjective Competency Assessment with Human-AI Collaboration
International Journal of Learning and Teaching (2026) · doi:10.18178/ijlt.12.3.221-225 · v1.0 |
- | — |
| 2576 | failed |
Multi-Judge Consensus Scoring for IRT-Based Adaptive LLM Evaluation
arXiv/Crossref (2026) · doi:10.31979/etd.nfqd-nzvn · v1.0 |
- | — |
| 2577 | failed |
Automated Essay Scoring In Educational AI: A Review Of Fairness, Bias, And Generalization
International Journal of Artificial Intelligence and Machine Learning (2026) · doi:10.51483/ijaiml.6.5s.2026.817-840 · v1.0 |
- | — |
| 2578 | scored |
Assessment Governance in the Age of Generative AI A Practical Policy Framework for University Instructors
SSRN Electronic Journal (2026) · doi:10.2139/ssrn.6853218 · v1.0 |
0.0 (Flash Lite) | — |
| 2579 | scored |
Reconceptualizing Outcome-based Education in the Age of Generative Artificial Intelligence: From Bloom's Hierarchy to the Creation-to-Comprehension Spiral
SSRN Electronic Journal (2026) · doi:10.2139/ssrn.6813979 · v1.0 |
0.0 (Flash Lite) | — |
| 2580 | scored |
From Automation to Augmentation: A Human-Centered Framework for Responsible Generative Artificial Intelligence in Education
SSRN Electronic Journal (2026) · doi:10.2139/ssrn.6718960 · v1.0 |
0.0 (Flash Lite) | — |
| 2581 | failed |
Cross‐Cultural Applicability and Application of the Nursing Leaders’ Readiness for Artificial Intelligence Scale: A Cross‐Sectional Study
Journal of Nursing Management (2026) · doi:10.1155/jonm/9637045 · v1.0 |
- | — |
| 2582 | failed |
Assessing the Authenticity of Artificial Intelligence Appraisals of Clinical Psychiatric Scenarios: A Preliminary Investigation
Education in Medicine Journal (2026) · doi:10.21315/eimj2026.18.3.18 · v1.0 |
- | — |
| 2583 | failed |
Architecting Intelligence: Artificial Intelligence and Deep Learning
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23056322 · v1.0 |
- | — |
| 2584 | failed |
Applications of artificial intelligence in neurosurgical practice and residency education: a systematic review
Medical Journal of Indonesia (2026) · doi:10.13181/mji.oa.268320 · v1.0 |
- | — |
| 2585 | Screened Out |
Transformation of the linguistic environment in Kazakhstan's megacities in the era of digitalisation and changing educational paradigms
Bulletin of the Karaganda University Philology series (2026) · doi:10.31489/2026phi3(123)/15-29 · v1.0 |
2.9 (gemini-3.5-flash-lite) | — |
| 2586 | scored |
A Novel AI-Driven Markov Chain Approach to Dementia Detection
IISE Annual Conference & Expo (2026) · doi:10.21872/annual2025_8487 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 2587 | In Buffer |
AI-supported formative assessment of conceptual understanding in mathematics: LLM-based pre-coding of open student responses
Research Square (2026) · doi:10.21203/rs.3.rs-10200491/v1 · v1.0 |
6.1 (gemini-3.6-flash) | — |
| 2588 | scored |
Use of AI for knowledge engineering.
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21626088 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 2589 | Screened Out |
Responsible Innovation and Technology Assessment Frameworks
Advances in computational intelligence and robotics book series (2026) · doi:10.4018/979-8-2600-0403-6.ch005 · v1.0 |
2.5 (gemini-3.5-flash-lite) | — |
| 2590 | scored |
Exploring University Students’ Generative AI Use Patterns: Focusing on Cognitive Extension and Epistemic Agency
Journal of Educational Technology (2026) · doi:10.17232/kset.42.3.885 · v1.0 |
0.0 (Flash Lite) | — |
| 2591 | abstract_only |
Prerequisite-Aware AI Delegation in Mathematics Education: A Graph-Theoretic Framework for Competency-Sensitive Automation
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23055819 · v1.0 |
6.2 (gemini-3.5-flash-lite) | — |
| 2592 | Screened Out |
Brunei and Generative AI: Gender and Readiness
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23053952 · v1.0 |
2.4 (gemini-3.5-flash-lite) | — |
| 2593 | scored |
Algebraic Collapse in Nondeterministic Language Models via Deterministic Manifold Constraint (Amendment Edition v1.1)
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23067854 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 2594 | Screened Out |
Engineer 2030 Requirements
International Multidisciplinary Journal of Emerging Technologies and Applications (2026) · doi:10.67294/zeg05p59 · v1.0 |
2.5 (gemini-3.5-flash-lite) | — |
| 2595 | scored |
The Decision That Has No Architecture: Internal Command for Critical AI Systems and the Liability the Decision Carries
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23051221 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 2596 | scored |
OPPORTUNITIES AND CHALLENGES OF USING ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE EDUCATION
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23052889 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 2597 | scored |
Artificial intelligence in predicting lymphovascular invasion in colorectal cancer imaging: a systematic review and meta-analysis
BMC Medical Imaging (2026) · doi:10.1186/s12880-026-02858-3 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 2598 | Screened Out |
Engineering students’ perceptions of mobile-assisted language learning for English terminological competence in Kazakhstan
Suleyman Demirel University bulletin: pedagogy and teaching methods. (2026) · doi:10.47344/6qtzqg54 · v1.0 |
2.4 (gemini-3.5-flash-lite) | — |
| 2599 | Screened Out |
INDONESIAN UNIVERSITY STUDENTS’ PERCEPTION OF THE USE OF AI TOOLS FOR ENGLISH LANGUAGE LEARNING
Jurnal Pendidikan DEWANTARA Media Komunikasi Kreasi dan Inovasi Ilmiah Pendidikan (2026) · doi:10.55933/jpd.v12i2.1220 · v1.0 |
2.4 (gemini-3.5-flash-lite) | — |
| 2600 | scored |
Digital Challenges to the Protection of Personal Data
Social Reports (2026) · doi:10.66638/w04hpp12 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |