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: 13659 →
Screen: 12853 →
Eligible: 2028 →
Included: 57
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 = 57 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 | 11975 | 2026-10-30 04:06 |
ref — reference lists of read works | 1657 | 2026-10-29 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 | 11975 | 2026-10-06 | 2026-10-30 |
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 | 6461 |
gemini-3.5-flash-lite | 5985 |
— | 473 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 104 |
gemini-3.8-flash | v2.1 | 87 |
gemini-3.7-flash | v2.1 | 80 |
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) | 12455 |
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): 10852 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 | 6933 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 2981 | 0 (too few) | 0 | — |
| 4.0–5.0 | 938 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1095 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 882 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 27 | 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)
6.72 / 10
Analyzed papers: 536
Quality Stratification
🟢 High rigor (M ≥ 7.0): 294
🟡 Moderate rigor (4.0 ≤ M < 7.0): 230
🔴 Low rigor (M < 4.0): 12
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 218 |
other | 36 |
theoretical | 25 |
rct | 13 |
assessment_design | 139 |
natural_experiment | 4 |
quasi_experiment | 42 |
detector_evaluation | 36 |
review | 11 |
survey | 3 |
experimental | 1 |
qualitative | 8 |
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=279, 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 (96)
#Psychometrics (52)
#Item Response Theory (39)
#academic integrity (38)
#item response theory (33)
#assessment validity (28)
#Assessment Validity (25)
#higher education (25)
#IRT (24)
#construct validity (23)
#Automated Essay Scoring (21)
#automated essay scoring (20)
#Generative AI (19)
#Construct Validity (18)
#generative AI (17)
#LLM as rater (16)
#assessment_design (16)
#Academic Integrity (14)
#Automated Scoring (14)
#Higher Education (14)
#LLM Evaluation (13)
#Large Language Models (13)
#assessment design (13)
#automated assessment (13)
#AI-resilient assessment (11)
#academic_integrity (11)
#assessment redesign (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#large language models (11)
#LLM-as-a-judge (10)
#automated scoring (10)
#item_response_theory (10)
#validity (10)
#ChatGPT (9)
#DIF (9)
#GPT-4 (9)
#Inter-Rater Reliability (9)
#LLM as Rater (9)
#LLM evaluation (9)
#LLMs as Test Takers (9)
#concept drift (9)
#differential item functioning (9)
#AI-resistant assessment (8)
#Assessment Design (8)
#Knowledge Tracing (8)
#LLMs as raters (8)
#Prompt Engineering (8)
#construct_validity (8)
#prompt engineering (8)
#rater drift (8)
#AI-Resilient Assessment (7)
#Assessment Redesign (7)
#Automated Item Generation (7)
#Computer Science Education (7)
#Computerized Adaptive Testing (7)
#Validity (7)
#computerized adaptive testing (7)
#higher_education (7)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 1 | scored |
Evaluating the Effectiveness of ChatGPT Versus Human Proctors in Grading Medical Students' Post-OSCE Notes.
Family Medicine (2025) · doi:10.22454/fammed.2025.954255 · v1.0 |
6.0 (gemini-3.5-flash-lite) | — |
| 2 | Screened Out |
Adapting to Online and Remote Learning: Examining the Educational Assessment Experiences of U.S. College Students Amidst COVID-19
IEEE Internet Computing (2024) · doi:10.1109/mic.2023.3334025 · v1.0 |
3.5 (gemini-3.5-flash-lite) | — |
| 3 | Screened Out |
Students’ Perceptions of Online Course Design: Insights into Cheating, Proctoring, and Learning Tools
American Journal of Distance Education (2025) · doi:10.1080/08923647.2025.2555642 · v1.0 |
3.3 (gemini-3.5-flash-lite) | — |
| 4 | Screened Out |
Blockchain-Powered Trust Framework for Securing Online Examination Integrity and Authenticity Through Smart Validation
2025 International Conference on Sustainable Communication Networks and Application (ICSCN) (2025) · doi:10.1109/icscn67106.2025.11308576 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 5 | Screened Out |
Watchfull-Eyes: A Gaze Based Tracking System for Exam Integrity Using Computer Vision and Machine Learning
International Journal of Scientific Research in Engineering and Management (2025) · doi:10.55041/ijsrem55208 · v1.0 |
3.8 (gemini-3.5-flash-lite) | — |
| 6 | Screened Out |
Comparison of biomedical science students’ perceptions of online versus paper-based examinations
Frontiers in Education (2024) · doi:10.3389/feduc.2023.1321206 · v1.0 |
2.7 (gemini-3.5-flash-lite) | — |
| 7 | Screened Out |
Remote versus onsite proctored exams: comparing students’ results in physical fitness testing
Physical Education of Students (2023) · doi:10.15561/20755279.2023.0605 · v1.0 |
3.6 (gemini-3.5-flash-lite) | — |
| 8 | Screened Out |
AI-Based Online Exam Proctoring System: A Real-Time Cheating Detection Framework using Computer Vision, Deep Learning, and Browser-Based Behavioral Analysis
International Journal of Creative and Open Research in Engineering and Management (2026) · doi:10.55041/ijcope.v2i4.1045 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 9 | Screened Out |
Artificial Intelligence, Cybersecurity and Digital Learning Resilience in Higher Education: An Integrative Review and Capacity-Sensitive Framework for Resource-Constrained Universities
Asian Journal of Research in Computer Science (2026) · doi:10.9734/ajrcos/2026/v19i10925 · v1.0 |
2.9 (gemini-3.5-flash-lite) | — |
| 10 | scored |
Spatiotemporal Rainfall Trends, Variability and Transition Dynamics for Climate-Resilient Crop Planning in West Bengal, India
Agriculture (2026) · doi:10.3390/agriculture16202280 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 11 | scored |
SELF-CALIBRATING NANO-NEURAL INTERFACES AND REFERENCE LINEAGE AT THE LIMIT Built-In References × Drift Attribution × Independent Functional Requalification STARLI / Institute XXIII / Research Flagship 17 PUBLICATION DESCRIPTION When a long-running neural interface changes, is the cause the sensor, the surrounding tissue, or the decoder? An adaptive algorithm can restore predictive accuracy while obscuring the physical origin of its own correction. This book proposes a rigorous, falsifiable architecture for self-calibrating nano-neural interfaces that must retain the scientific history of every reference and every update. Its central axiom is that self-calibration does not imply self-certification: measurement claims, permission to change, electrode safety and functional benefit demand independent evidence. The first coupling is the existence of physically traceable internal references that can be challenged against electrically and temporally independent standards. The second is a source-aware drift-diagnostic process that distinguishes electronics gain, electrode-tissue transfer, tissue-like surrogate changes, changing source signals and model/feature drift, explicitly recording instances of nonidentifiability. The third is external functional requalification, which can reject an apparently successful update after hidden-domain testing, material or reference replacement, loss of selectivity, missing raw observations, or inability to reproduce the frozen predecessor. This research monograph is divided into seven causal layers and 82 chapters. Five major subsections of each chapter are assigned independent pages, yielding 410 separately paginated technical essays. Thirty gold mathematical derivations are linked from chapter-opening equations to discriminating experiments. The mathematics includes augmented state-space observability, nonlinear observation functions, source-separation Jacobians, Kalman innovations, impedance spectroscopy, covariance propagation, change-point statistics, conformal coverage, selective risk, hard-veto constrained update eligibility, reference-bridge covariance, exact rollback and cross-laboratory requalification. A displayed formula is a candidate analytical instrument with stated dimensions and assumptions, not a discovered biological law. Two hundred detailed Research Gates are preserved at the rear: 82 interrogate source identifiability, another 82 challenge independent physical and evidential hard vetoes, and 36 require third-party reconstruction of a frozen reference history. Thirty-three Phoenix Answer Embryos propose revisable architectures, beginning with the Neural Interface Calibration Passport. The Passport ties an interface's physical identity to the raw analog and digitized record, calibrated unit chain, hardware reference ancestors, model and feature checksums, reasons for changing any reference, failed updates, revoked permissions, blinded requalification and rollback. These are proposed evidence structures, not commercially validated devices. The DOI bibliography contains 18 unique external research records covering chronic intracortical signal instability, low-dimensional BCI stabilization, decoder adaptation, long-term electrode-tissue behavior, complex impedance metrology, electrochemical reference limits, neural interface materials and conformal uncertainty under covariate shift. Selected publisher and PubMed pages were consulted for source metadata. Their findings cannot establish efficacy of the new self-calibration architecture; source-specific claims and independent replication would be necessary.
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23439262 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 12 | scored |
Privacy-Preserving Artificial Intelligence in Digital Learning: A Systematic Review of Federated Learning, Differential Privacy, Security, and Educational Utility in Higher Education
Research Square (2026) · doi:10.21203/rs.3.rs-10922594/v1 · v1.0 |
0.0 (Flash Lite) | — |
| 13 | Not Article |
Data for: Multi-Index Assessment of Long-Term Vegetation Dynamics and Hydroclimatic Controls Using Harmonized Landsat–Sentinel Observations
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23492738 · v1.0 |
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| 14 | scored |
Remaining useful life prediction for power MOSFETs based on degradation perception
International Conference on Algorithms, Machine Learning, and Image Processing (AMLIP) (2026) · doi:10.1117/12.3132467 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 15 | scored |
Linking Error Estimation for Mean-Geometric Mean and Pairwise Haberman Linking Across Multiple Time Points in the 2PL Model
arXiv/Crossref (2026) · doi:10.31234/osf.io/ct4my_v1 · v1.0 |
5.6 (gemini-3.5-flash-lite) | — |
| 16 | scored |
"Artificial Intelligence in Banking Fraud Detection: A Systematic Review using the Prisma Approach"
International Journal of Drug Delivery Technology (2026) · doi:10.25258/ijddt.16.74s.194 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 17 | Not Article |
Reproducibility package: Robust warming rates, marginal evidence of acceleration and unstable extrapolation: a reproducible reanalysis of statistical trend models for the Berkeley Earth global temperature record
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23512340 · v1.0 |
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| 18 | scored |
Which Loss Prices the English Share? What Target-Language Validation Loss Can and Cannot See in Continued Pretraining for Language Adaptation, Why Two Training Regimes Disagree About What English Buys, and the Instruments a Mixture Choice Needs
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23501040 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 19 | scored |
Reading the Sun from the Ground: A Mixed-Methods Voltage-Signature Framework for Season-Aware Micro-Siting of Small-Scale Solar Photovoltaics in District Jamshoro, Sindh, Pakistan
TAMSAAL (2026) · doi:10.59075/tamsaal.v4i10.241 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 20 | scored |
Smart Disaster Response Drone for Rapid Relief Operation
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.23534241 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 21 | Screened Out |
Mapping Teledentistry Encounters to Demographic and Diagnostic Groups: A Retrospective Evaluation of Claims Data in a U.S. Dental Institution
Telemedicine Journal and e-Health (2026) · doi:10.1177/15305627261499084 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 22 | scored |
Weannet enables parameter-efficient transfer through decaying lateral connections in progressive networks
Discover Artificial Intelligence (2026) · doi:10.1007/s44163-026-02490-8 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 23 | scored |
From Nebraska to Bondi: Is epistemic violence inherent to Christian-Muslim interfaith? In response to: CMCS “Blog #3.9 – From Badr to Bondi: Is Islam Inherently Violent?”
SOAS Research Online (SOAS University of London) (2026) · doi:10.25501/soas.1092087 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 24 | scored |
The Impact of Next-Generation AI Chatbots on Academic Writing Skills and Academic Integrity Among English-Majored Students at HUIT: An Experimental Study
The International Journal of Language Studies (2026) · doi:10.60087/ijls.v3.n2.004 · v1.0 |
6.0 (gemini-3.1-flash-lite) | — |
| 25 | Screened Out |
THE INFLUENCE OF PREDICTOR FACTORS ON BEHAVIORAL INTENTION TO IMPLEMENT AI-PROCTORED ONLINE ASSESSMENT
International Journal of Modern Education (2026) · doi:10.35631/ijmoe.830037 · v1.0 |
3.7 (gemini-3.1-flash-lite) | — |
| 26 | Screened Out |
Dampak Ketergantungan Penggunaan Artificial Intellegence terhadap Kemandirian Berpikir Kritis pada Mahasiswa
Jurnal Pustaka Cendekia Pendidikan (2026) · doi:10.70292/jpcp.v4i1.383 · v1.0 |
4.2 (gemini-3.1-flash-lite) | — |
| 27 | Screened Out |
Challenges, opportunities and a conceptual framework for language education in the AI era: Research directions and research design strategies
Artificial Intelligence in Language Education (2026) · doi:10.29140/aile.2026.104622 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 28 | Screened Out |
Reassessing The Fraud Hexagon Model: Evidence on Its Selective Significance in Explaining Academic Fraud
Jurnal Ekonomi Manajemen dan Bisnis (2026) · doi:10.70716/emis.v4i2.616 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 29 | Screened Out |
When AI Meets Academic Integrity: Chatgpt Use and Plagiarism Awareness in a Pesantren-Based University
Arus Jurnal Psikologi dan Pendidikan. (2026) · doi:10.57250/ajpp.v5i2.2910 · v1.0 |
3.0 (gemini-3.1-flash-lite) | — |
| 30 | scored |
Artificial intelligence, scientific production, and human responsibility: The position of the Belize Journal of Medicine
Belize Journal of Medicine (2026) · doi:10.61997/bjm.v15i2.507 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 31 | scored |
GenAI in higher education: learning, relearning and unlearning
Journal of Learning Development in Higher Education (2026) · doi:10.47408/jldhe.vi40.2065 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 32 | Screened Out |
Linguistic Equity or Forced Assimilation: A Review of How AI Writing Tools Shape the International Student Experience
International Journal of Innovative Science and Research Technology (IJISRT) (2026) · doi:10.38124/ijisrt/26jun849 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 33 | Not Article |
Preface
Oxford University Press eBooks (2026) · doi:10.1093/oxfordhb/9780197769034.002.0006 · v1.0 |
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| 34 | scored |
Introduction
CRC Press eBooks (2026) · doi:10.1201/9781003676188-1 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 35 | Screened Out |
THE FUTURE OF LITERACY: EXPLORING CHATGPT'S IMPACT ON THE WRITING PROCESS
JURNAL PENDIDIKAN GLASSER (2026) · doi:10.32529/glasser.v10i1.4921 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 36 | Screened Out |
Examinations of Online Exam Scores in Higher Education Before and After the Artificial Intelligence (AI) Tools Made Publicly Available
Global Journal of Business Pedagogy (2026) · doi:10.47177/g2c1wt12 · v1.0 |
4.2 (gemini-3.1-flash-lite) | — |
| 37 | scored |
PlagiAct: Measurement Instrument of STEM Student Attitudes Toward Academic Dishonesty in the AI Era
IEEE Transactions on Education (2026) · doi:10.1109/te.2026.3694297 · v1.0 |
0.0 (Flash Lite) | — |
| 38 | Screened Out |
Understanding University Students' Perspectives on Generative AI
CRC Press eBooks (2026) · doi:10.1201/9781003676188-10 · v1.0 |
4.2 (gemini-3.1-flash-lite) | — |
| 39 | Screened Out |
Development of a machine learning-based online test system with a hierarchical structure for students
Journal of Al-Qadisiyah for Computer Science and Mathematics (2026) · doi:10.29304/jqcsm.2026.18.22844 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 40 | scored |
Reseña del libro: Desafíos éticos de la inteligencia artificial en la academia: impacto e implicaciones para la educación superior
Revista de Estudios e Investigación en Psicología y Educación (2026) · doi:10.17979/reipe.2026.13.1.13684 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 41 | scored |
ARTIFICIAL INTELLIGENCE AS A RESEARCH ASSISTANT: A FRAMEWORK FOR ETHICAL USE AMONG UNIVERSITY STUDENTS
International Journal of Progressive Research in Engineering Management and Science (2026) · doi:10.58257/ijprems54009 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 42 | scored |
MANAGEMENT OF INNOVATIVE DEVELOPMENT OF EDUCATION IN THE CONTEXT OF GLOBALIZATION CHALLENGES
Іnternational Science Group eBooks (2026) · doi:10.46299/979-8-90553-928-2 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 43 | scored |
Collaborator or Accomplice? A National Examination of Artificial Intelligence (AI) in School District Discipline Policy
Educational Policy (2026) · doi:10.1177/08959048261461456 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 44 | scored |
Faith Integration, Ethical Orientation, and AI Adoption in a Faith-Based University
IGI Global eBooks (2026) · doi:10.4018/979-8-3373-8192-3.ch002 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 45 | scored |
Psychological Research Methods Based on Natural Language Processing Technology
Psychology of China (2026) · doi:10.35534/pc.0801005 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 46 | scored |
Self-motivation and its contribution to the dimensions of artificial intelligence among fourth-year students in the college of physical education and sports sciences
International Journal of Physiology Sports and Physical Education (2026) · doi:10.33545/26647710.2026.v8.i1a.202 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 47 | scored |
Fine-Tuned Large Language Models for Inclusive Education Athlete Support: Performance Evaluation and Benchmarking
Lecture notes in networks and systems (2026) · doi:10.1007/978-3-032-04160-9_1 · v1.0 |
0.0 (Flash Lite) | — |
| 48 | scored |
Governing Generative AI in Higher Education: From Crisis Management to Strategic Integration (2026-2030)
SSRN Electronic Journal (2026) · doi:10.2139/ssrn.6007354 · v1.0 |
0.0 (Flash Lite) | — |
| 49 | scored |
The Use of Artificial Intelligence Technology to Empower Teaching in Psychometrics and Assessment
Vocational Education (2026) · doi:10.12677/ve.2026.152070 · v1.0 |
0.0 (Flash Lite) | — |
| 50 | scored |
Artificial intelligence in education: A comprehensive review of personalized learning, knowledge tracing, and adaptive instructional technologies
International Journal of Communication and Information Technology (2026) · doi:10.33545/2707661x.2026.v7.i1b.183 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |