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: 15439 →
Screen: 14535 →
Eligible: 2202 →
Included: 69
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 = 69 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 | 13688 | 2026-11-03 04:05 |
ref — reference lists of read works | 1724 | 2026-11-02 14:23 |
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 | 13688 | 2026-10-06 | 2026-11-03 |
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 | 7206 |
gemini-3.5-flash-lite | 6827 |
— | 568 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 139 |
gemini-3.8-flash | v2.1 | 101 |
gemini-3.7-flash | v2.1 | 94 |
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) | 14137 |
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): 12360 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 | 7998 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3328 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1034 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1199 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 949 | 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)
6.63 / 10
Analyzed papers: 599
Quality Stratification
🟢 High rigor (M ≥ 7.0): 315
🟡 Moderate rigor (4.0 ≤ M < 7.0): 267
🔴 Low rigor (M < 4.0): 17
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 231 |
other | 42 |
theoretical | 26 |
rct | 13 |
assessment_design | 156 |
natural_experiment | 4 |
quasi_experiment | 53 |
detector_evaluation | 39 |
survey | 7 |
review | 16 |
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=319, 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 (99)
#Psychometrics (55)
#Item Response Theory (45)
#academic integrity (45)
#item response theory (37)
#assessment validity (33)
#Assessment Validity (32)
#higher education (32)
#Automated Essay Scoring (27)
#Generative AI (25)
#construct validity (25)
#IRT (24)
#automated essay scoring (22)
#Construct Validity (21)
#generative AI (19)
#Academic Integrity (18)
#LLM as rater (18)
#Automated Scoring (16)
#Higher Education (16)
#Large Language Models (16)
#assessment redesign (16)
#assessment_design (16)
#LLM Evaluation (15)
#AI-resilient assessment (13)
#assessment design (13)
#automated assessment (13)
#automated scoring (13)
#large language models (13)
#validity (13)
#LLM as Rater (12)
#academic_integrity (12)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#differential item functioning (11)
#Assessment Redesign (10)
#ChatGPT (10)
#GPT-4 (10)
#LLM-as-a-judge (10)
#LLMs as raters (10)
#Prompt Engineering (10)
#item_response_theory (10)
#prompt engineering (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)
#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)
#higher_education (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 501 | scored |
AI as a conditional accessibility resource in deaf EFL higher education: instructor perceptions from an Iranian university context
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1951268 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 502 | Screened Out |
An AI-Assisted Interactive Video-Based System (AI-LabEyes) for Enhancing Visualization, Formative Feedback, and Collaborative Learning in Chemistry Laboratory Instruction
Journal of Chemical Education (2026) · doi:10.1021/acs.jchemed.6c00294 · v1.0 |
4.0 (gemini-3.5-flash-lite) | — |
| 503 | No Fulltext |
Fostering Foreign Language Pre-service Teachers’ Lesson Design Competence through AI-Self-Assessment Coordination Loop
Foreign Languages Education (2026) · doi:10.15334/fle.2026.33.3.129 · v1.0 |
5.6 (gemini-3.5-flash-lite) | — |
| 504 | scored |
Gender and AI: An Ethical Assessment of Why Female Users’ Trust Matters
KADEM Kadın Araştırmaları Dergisi (2026) · doi:10.21798/kadem.2026.217 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 505 | Screened Out |
An inquiry-based model to support writing instruction in the context of generative AI
Journal of Learning Development in Higher Education (2026) · doi:10.47408/jldhe.vi41.1920 · v1.0 |
2.8 (gemini-3.5-flash-lite) | — |
| 506 | scored |
The role of artificial intelligence in improving student mathematics instruction
THE TIDINGS of the Baltic State Fishing Fleet Academy Psychological and pedagogical sciences (Theory and methods of professional education) (2026) · doi:10.46845/2071-5331-2026-3-77-236-242 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 507 | scored |
From Model Assessment to Process Fit: Designing Benchmark Dimensions for LLMs in Business Contexts
IFIP advances in information and communication technology (2026) · doi:10.1007/978-3-032-38614-4_41 · v1.0 |
0.0 (Flash Lite) | — |
| 508 | scored |
Digitalization of Higher Education: Enhancing Professional Training in Physical Education and Sports
Revista Romaneasca pentru Educatie Multidimensionala (2026) · doi:10.66388/rrem/18.3/60 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 509 | scored |
From digital documentation to AI-supported decision making in architectural heritage conservation: knowledge structure, technological convergence, and an integrated research roadmap
Journal of Asian Architecture and Building Engineering (2026) · doi:10.1080/13467581.2026.2739676 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 510 | Not Article |
Dataset for the paper Socratic Agents for Improper Integrals: A Case Study
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23055197 · v1.0 |
- | — |
| 511 | scored |
Reclaiming pedagogical architecture in adaptive gamification: Design-time scenario-level adaptivity beyond algorithmic personalization
Journal of Educational Technology and Online Learning (2026) · doi:10.31681/jetol.1915276 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 512 | Screened Out |
Over-Reliance on Generative AI and Its Impact on Critical Thinking and Learning Outcomes among Management Students: Evidence from Jodhpur
GLS KALP: Journal of Multidisciplinary Studies. (2026) · doi:10.69974/glsuj.2026.06.01.02 · v1.0 |
2.4 (gemini-3.5-flash-lite) | — |
| 513 | scored |
Perspective Chapter: Augmented Humanity – Balancing Artificial and Emotional Intelligence in Surgical Education
IntechOpen eBooks (2026) · doi:10.5772/intechopen.1017898 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 514 | scored |
Polyphonic Aesthetics: Towards a Technopoetry and Sound Poetry Within Expanded Art
EPISTÉMÈ (2026) · doi:10.38119/cacs.2026.39.5 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 515 | scored |
Peer Discourse in Process-Based Physics Tasks: Discursive Moves and Meaning-Making in Asynchronous Learning
Science education international (2026) · doi:10.33828/sei.v37.i4.9 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 516 | scored |
Trustworthy Explainable Artificial Intelligence for Clinical Decision Support Systems: A Systematic Review of Methods, Validation, and Real-World Deployment Challenges
Open Science Framework (2026) · doi:10.17605/osf.io/3gmqb · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 517 | Screened Out |
Approaches to Automated Scoring of Speaking for K–12 English Language Proficiency Assessments
ETS Research Report Series (2017) · doi:10.1002/ets2.12147 · v1.0 |
4.1 (gemini-3.1-flash-lite) | — |
| 518 | scored |
Differential Item Functioning
Sage eBooks (2009) · doi:10.4135/9781412993913 · v1.0 |
0.0 (Flash Lite) | — |
| 519 | scored |
Evaluating Mathematical Reasoning Across Large Language Models: A Fine-Grained Approach
arXiv (Cornell University) (2025) · doi:10.48550/arxiv.2503.10573 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 520 | scored |
MM-Eval: A Multilingual Meta-Evaluation Benchmark for LLM-as-a-Judge and Reward Models
arXiv (Cornell University) (2024) · doi:10.48550/arxiv.2410.17578 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 521 | scored |
MathChat: Converse to Tackle Challenging Math Problems with LLM Agents
arXiv (Cornell University) (2023) · doi:10.48550/arxiv.2306.01337 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 522 | scored |
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
arXiv (Cornell University) (2023) · doi:10.48550/arxiv.2308.07201 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 523 | Screened Out |
Effectiveness of AI in solving math problems at the secondary school level
UNION Jurnal Ilmiah Pendidikan Matematika (2024) · doi:10.30738/union.v12i2.17548 · v1.0 |
2.4 (gemini-3.1-flash-lite) | — |
| 524 | scored |
UTILIZING ARTIFICIAL INTELLIGENCE IN EDUCATION TO ENHANCE TEACHING EFFECTIVENESS
Proceedings of International Conference on Education (2024) · doi:10.32672/pice.v2i1.1367 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 525 | Screened Out |
ChatGPT’s performance in university admissions tests in mathematics
International Electronic Journal of Mathematics Education (2024) · doi:10.29333/iejme/15517 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 526 | scored |
Learning English as a second language with artificial intelligence for prospective teachers: a systematic review
Frontiers in Education (2024) · doi:10.3389/feduc.2024.1490067 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 527 | Screened Out |
Evaluating chatGPT-4 and chatGPT-4o: performance insights from NAEP mathematics problem solving
Frontiers in Education (2024) · doi:10.3389/feduc.2024.1452570 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 528 | Screened Out |
Using large language models to support pre-service teachers mathematical reasoning—an exploratory study on ChatGPT as an instrument for creating mathematical proofs in geometry
Frontiers in Artificial Intelligence (2024) · doi:10.3389/frai.2024.1460337 · v1.0 |
2.2 (gemini-3.1-flash-lite) | — |
| 529 | Screened Out |
Chatbots Put to the Test in Math and Logic Problems: A Comparison and Assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard
AI (2023) · doi:10.3390/ai4040048 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 530 | scored |
Exploring the potential of artificial intelligence tools in educational measurement and assessment
Eurasia Journal of Mathematics Science and Technology Education (2023) · doi:10.29333/ejmste/13428 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 531 | scored |
Optimalisasi Penggunaan Mendeley sebagai Tools Manajemen Referensi Penulisan Karya Tulis Ilmiah Mahasiswa
ServeInnovate Journal of Applied Community Engagement (SJACE) (2026) · doi:10.67219/2362b358 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 532 | scored |
Implementasi Algoritma Winnoming Untuk Penilaian Otomatis Jawaban Esai Pada Universitas Alifa Padang Berbasis Web
Jurnal Sains Informatika Terapan (2026) · doi:10.62357/jsit.v5i2.1170 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 533 | scored |
The Role of Artificial Intelligence in Reshaping Teaching and Learning Structures in Medical Education: A Narrative Review
Journal of Medical Education and Development (2026) · doi:10.18502/jmed.v20i4.21732 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 534 | scored |
Efisiensi Platforms Digital dalam Meningkatkan Kualitas Evaluasi Pembelajaran Pendidikan Agama Islam pada Kurikulum Merdeka
Jurnal Pendidikan Sains Geologi dan Geofisika (GeoScienceEd Journal) (2026) · doi:10.29303/goescienceed.v7i3.2018 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 535 | Screened Out |
Authentic assessment in higher education: conceptual evolution, key debates and implications for practice
Assessment & Evaluation in Higher Education (2026) · doi:10.1080/02602938.2026.2684019 · v1.0 |
3.9 (gemini-3.5-flash-lite) | — |
| 536 | Not Article |
ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE EDUCATION: OPPORTUNITIES, CHALLENGES, AND PROSPECTS
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20599607 · v1.0 |
- | — |
| 537 | Screened Out |
THE IMPACT OF ACADEMIC INTEGRITY INSTRUCTION ON STUDENTS' ETHICAL USE OF ARTIFICIAL INTELLIGENCE IN ACADEMIC WRITING
Journal of Multidisciplinary Sciences and Innovations (2026) · doi:10.5281/zenodo.20590044 · v1.0 |
3.9 (gemini-3.5-flash-lite) | — |
| 538 | Screened Out |
Crisis of Algorithmic Filtering in Academic Communication: Why Scientometrics 2.0 Becomes Structurally Necessary
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20596381 · v1.0 |
4.5 (gemini-3.5-flash-lite) | — |
| 539 | Not Article |
RETRACTION NOTICE TO “DIGITALISATION OF ADMINISTRATIVE PROCEDURES IN THE FIELD OF ECONOMIC COMPETITION CONTROL: CHALLENGES AND OPPORTUNITIES FOR UKRAINE” [Baltic Journal of Economic Studies, 11(5), 299-306]
Baltic Journal of Economic Studies (2026) · doi:10.30525/2256-0742/2026-12-2-469 · v1.0 |
- | — |
| 540 | Screened Out |
AI-Based Exam Automation System for Smart Online Examination Management
International Scientific Journal of Engineering and Management (2026) · doi:10.55041/isjem07846 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 541 | Screened Out |
THE ROLE OF ARTIFICIAL INTELLIGENCE TOOLS IN DEVELOPING ACADEMIC WRITING SKILLS IN EFL EDUCATION
International Multidisciplinary Journal for Research & Development (2026) · doi:10.5281/zenodo.20594813 · v1.0 |
2.2 (gemini-3.5-flash-lite) | — |
| 542 | scored |
Trend Analysis of Crime Against Women and Violent Crimes in Kerala and Uttar Pradesh
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20593299 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 543 | Not Article |
ADVANTAGES AND DISADVANTAGES OF TEACHING WRITING SKILLS USING AI IN THE ENGLISH LANGUAGE CLASSROOM
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.20600530 · v1.0 |
- | — |
| 544 | Screened Out |
Academic AI Ethics: The Normative Limits of Generative Artificial Intelligence in Higher Education
Temaşa Erciyes Üniversitesi Felsefe Bölümü Dergisi (2026) · doi:10.55256/temasa.1911850 · v1.0 |
2.5 (gemini-3.5-flash-lite) | — |
| 545 | scored |
An Analysis on the Reconstruction of "Teaching" and "Learning" in Primary and Secondary Schools by Generative AI
Lecture Notes in Education Psychology and Public Media (2026) · doi:10.54254/2753-7048/2026.ld34332 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 546 | scored |
Artificial Intelligence in Japan’s Language Learning Classrooms
Candlin & Mynard ePublishing Limited eBooks (2026) · doi:10.47908/45 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 547 | Screened Out |
Students’ Perceptions of Generative AI Use and Academic Integrity in Translation Learning
DEIKTIS Jurnal Pendidikan Bahasa dan Sastra (2026) · doi:10.53769/deiktis.v6i2.3657 · v1.0 |
2.5 (gemini-3.5-flash-lite) | — |
| 548 | scored |
Introduction to This Volume
Candlin & Mynard ePublishing Limited eBooks (2026) · doi:10.47908/45/i · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 549 | Screened Out |
Explaining reported generative AI engagement in higher education: an extended TAM with ethical compatibility and reliance-based trust
Scientific Reports (2026) · doi:10.1038/s41598-026-56912-9 · v1.0 |
2.6 (gemini-3.5-flash-lite) | — |
| 550 | Screened Out |
Mapping the impact of generative AI in higher education: a scoping review of psychological and equity dimensions
Frontiers in Psychology (2026) · doi:10.3389/fpsyg.2026.1856854 · v1.0 |
3.5 (gemini-3.5-flash-lite) | — |