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: 20521 →
Screen: 19341 →
Eligible: 2807 →
Included: 105
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 = 105 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 | 18382 | 2026-11-14 16:12 |
ref — reference lists of read works | 2112 | 2026-11-14 14:30 |
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 | 18382 | 2026-10-06 | 2026-11-14 |
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 | 9635 |
gemini-3.5-flash-lite | 9033 |
— | 739 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 275 |
gemini-3.8-flash | v2.1 | 117 |
gemini-3.7-flash | v2.1 | 109 |
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) | 18943 |
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): 16561 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 | 10708 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 4478 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1375 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1549 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1197 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 37 | 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)
6.44 / 10
Analyzed papers: 766
Quality Stratification
🟢 High rigor (M ≥ 7.0): 356
🟡 Moderate rigor (4.0 ≤ M < 7.0): 383
🔴 Low rigor (M < 4.0): 27
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 289 |
other | 55 |
theoretical | 27 |
rct | 17 |
assessment_design | 215 |
quasi_experiment | 62 |
natural_experiment | 5 |
review | 25 |
detector_evaluation | 50 |
survey | 7 |
experimental | 1 |
qualitative | 13 |
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=397, 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 (117)
#Psychometrics (71)
#Item Response Theory (61)
#Assessment Validity (53)
#academic integrity (53)
#assessment validity (53)
#Automated Essay Scoring (46)
#item response theory (46)
#higher education (45)
#automated essay scoring (41)
#Generative AI (33)
#Construct Validity (32)
#LLM as rater (28)
#construct validity (27)
#Large Language Models (26)
#Higher Education (24)
#IRT (24)
#LLM Evaluation (24)
#large language models (24)
#GPT-4 (23)
#generative AI (22)
#assessment redesign (21)
#prompt engineering (21)
#Academic Integrity (20)
#Automated Scoring (20)
#ChatGPT (20)
#LLM as Rater (17)
#Prompt Engineering (17)
#automated scoring (17)
#Assessment Redesign (16)
#assessment design (16)
#assessment_design (16)
#validity (15)
#AI-resilient assessment (14)
#LLMs as raters (14)
#Computer Science Education (13)
#automated assessment (13)
#differential item functioning (13)
#Computerized Adaptive Testing (12)
#LLM-as-a-judge (12)
#academic_integrity (12)
#benchmarking (12)
#formative assessment (12)
#inter-rater reliability (12)
#Automated Item Generation (11)
#Differential Item Functioning (11)
#Knowledge Tracing (11)
#LLMs as Test Takers (11)
#Quadratic Weighted Kappa (11)
#assessment-validity (11)
#assessment_validity (11)
#rater drift (11)
#AI-resistant assessment (10)
#Inter-Rater Reliability (10)
#LLM evaluation (10)
#Medical Education (10)
#automated item generation (10)
#automated_essay_scoring (10)
#computerized adaptive testing (10)
#item_response_theory (10)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 18851 | No Fulltext |
Investigating Item Complexity as a Source of Cross-National DIF in TIMSS Math and Science
Large-scale Assessments in Education (2024) · doi:10.1186/s40536-024-00200-3 · v1.0 |
6.9 (gemini-3.5-flash-lite) | — |
| 18852 | No Fulltext |
Bayesian Adaptive Lasso for the Detection of Differential Item Functioning in Graded Response Models
Journal of Educational and Behavioral Statistics (2025) · doi:10.3102/10769986241233777 · v1.0 |
7.1 (gemini-3.5-flash-lite) | — |
| 18853 | No Fulltext |
Impacts of DIF Item Balance and Effect Size Incorporation with the Rasch Tree
Educational and Psychological Measurement (2026) · doi:10.1177/00131644251370605 · v1.0 |
6.1 (gemini-3.5-flash-lite) | — |
| 18854 | No Fulltext |
A Robust Method for Detecting Item Misfit in Large-Scale Assessments
Educational and Psychological Measurement (2023) · doi:10.1177/00131644221105819 · v1.0 |
6.6 (gemini-3.5-flash-lite) | — |
| 18855 | No Fulltext |
Psychometric Analysis of the Resonance Concept Inventory
Chemistry Education Research and Practice (2025) · doi:10.1039/d4rp00170b · v1.0 |
5.6 (gemini-3.5-flash-lite) | — |
| 18856 | No Fulltext |
Resolving and Re-Scoring Constructed Response Items in Mixed-Format Assessments: An Exploration of Three Approaches
Educational Assessment (2024) · doi:10.1080/10627197.2024.2356745 · v1.0 |
5.9 (gemini-3.5-flash-lite) | — |
| 18857 | scored |
Validation of the Indonesian Version of the Psychological Capital Questionnaire (PCQ) in Higher Education: A Rasch Analysis
Journal of Applied Research in Higher Education (2025) · doi:10.1108/jarhe-10-2023-0480 · v1.0 |
5.2 (gemini-3.5-flash-lite) | — |
| 18858 | Screened Out |
Investigating the Psychometric Features of a Locally Designed Computational Thinking Assessment for Elementary Students
Computer Science Education (2025) · doi:10.1080/08993408.2024.2344400 · v1.0 |
4.2 (gemini-3.5-flash-lite) | — |
| 18859 | No Fulltext |
Exploring the Evidence to Interpret Differential Item Functioning via Response Process Data
Educational and Psychological Measurement (2025) · doi:10.1177/00131644241298975 · v1.0 |
6.6 (gemini-3.5-flash-lite) | — |
| 18860 | No Fulltext |
Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times
Journal of Educational and Behavioral Statistics (2023) · doi:10.3102/10769986231151961 · v1.0 |
6.7 (gemini-3.5-flash-lite) | — |
| 18861 | No Fulltext |
Detecting Differential Item Functioning Using Response Time
Educational and Psychological Measurement (2025) · doi:10.1177/00131644241280400 · v1.0 |
6.1 (gemini-3.5-flash-lite) | — |
| 18862 | No Fulltext |
Generalizing beyond the Test: Permutation-Based Profile Analysis for Explaining DIF Using Item Features
Journal of Educational and Behavioral Statistics (2024) · doi:10.3102/10769986231174927 · v1.0 |
6.5 (gemini-3.5-flash-lite) | — |
| 18863 | Screened Out |
Gender Differences in Classroom Emotional Climate and Attitudes among Students Undertaking Integrated STEM Projects: A Rasch Analysis
Research in Science & Technological Education (2023) · doi:10.1080/02635143.2021.1981852 · v1.0 |
4.0 (gemini-3.5-flash-lite) | — |
| 18864 | No Fulltext |
Cross-Cultural Validation of the Mathematics Construct and Attribute Profiles: A Differential Item Functioning Approach
Applied Measurement in Education (2024) · doi:10.1080/08957347.2024.2438084 · v1.0 |
5.9 (gemini-3.5-flash-lite) | — |
| 18865 | No Fulltext |
An Analysis of DIF and Sources of DIF in Achievement Motivation Items Using Anchoring Vignettes
Educational Assessment (2024) · doi:10.1080/10627197.2024.2374298 · v1.0 |
5.9 (gemini-3.5-flash-lite) | — |
| 18866 | No Fulltext |
Functional Data Analysis and Person Response Functions
Measurement: Interdisciplinary Research and Perspectives (2023) · doi:10.1080/15366367.2022.2054130 · v1.0 |
5.6 (gemini-3.5-flash-lite) | — |
| 18867 | No Fulltext |
Detecting Aberrant Test-Taking Behaviors in Computer-Based Testing Using One-Dimensional Convolutional Neural Networks
Educational Measurement: Issues and Practice (2025) · doi:10.1111/emip.70010 · v1.0 |
6.7 (gemini-3.5-flash-lite) | — |
| 18868 | No Fulltext |
Cultural Sensitivity of Early Childhood Assessments Based on Learning Progressions: A Rasch Person Fit Analysis
Educational Assessment, Evaluation and Accountability (2025) · doi:10.1007/s11092-025-09453-0 · v1.0 |
6.0 (gemini-3.5-flash-lite) | — |
| 18869 | No Fulltext |
Detecting Rater Bias in Mixed-Format Assessments
Measurement: Interdisciplinary Research and Perspectives (2024) · doi:10.1080/15366367.2023.2173468 · v1.0 |
5.9 (gemini-3.5-flash-lite) | — |
| 18870 | abstract_only |
Item Performance in Context: Differential Item Functioning between Pilot and Formal Administration of the Norwegian Language Test
Educational Assessment, Evaluation and Accountability (2025) · doi:10.1007/s11092-025-09467-8 · v1.0 |
6.3 (gemini-3.5-flash-lite) | — |
| 18871 | Screened Out |
Studying Factorial Invariance with Nominal Items: A Note on a Latent Variable Modeling Procedure
Educational and Psychological Measurement (2025) · doi:10.1177/00131644241256626 · v1.0 |
4.7 (gemini-3.1-flash-lite) | — |
| 18872 | Screened Out |
Examining the Influence of Item Exposure and Retrieval Practice Effects on Test Performance in a Large-Scale Workforce Development Training Programme
International Journal of Training and Development (2024) · doi:10.1111/ijtd.12321 · v1.0 |
2.8 (gemini-3.1-flash-lite) | — |
| 18873 | No Fulltext |
Comparing Clustering Algorithms for Collusion Detection Using Mixed-Type Data
Educational Measurement: Issues and Practice (2026) · doi:10.1111/emip.70034 · v1.0 |
6.7 (gemini-3.1-flash-lite) | — |
| 18874 | Screened Out |
Understanding and Promoting Academic Integrity: Findings from a Campus Partnership
Journal of Academic Ethics (2026) · doi:10.1007/s10805-025-09709-w · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 18875 | Screened Out |
SACCIA Communication, Attitudes towards Cheating and Academic Misconduct
Journal of Academic Ethics (2025) · doi:10.1007/s10805-024-09541-8 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 18876 | Screened Out |
COVID-19 and Alternative Assessments in Higher Education: Implications for Academic Integrity among Nursing and Social Science Students
International Journal for Educational Integrity (2023) · doi:10.1007/s40979-023-00129-0 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 18877 | Screened Out |
Academic Dishonesty in Virtual Assessment during the COVID-19 Pandemic: A Cross-Cultural Study
Asia-Pacific Education Researcher (2024) · doi:10.1007/s40299-024-00829-2 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 18878 | Screened Out |
Cheating in Online Assessment: A Qualitative Study on Reasons and Coping Strategies Focusing on EFL Teachers' Perceptions
Language Testing in Asia (2024) · doi:10.1186/s40468-024-00304-1 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 18879 | Screened Out |
The Silent Struggle and Resiliency: Exploring Academic Dishonesty and Statistics Anxiety, Pre-, Peri-, and Post-Pandemic Landscape
Education and Information Technologies (2025) · doi:10.1007/s10639-025-13522-8 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 18880 | No Fulltext |
Effects of Academic Dishonesty Policy Reminder on University Students' Exam Cheating - A Double-Blind Randomized Controlled Experimental Field Study
Studies in Higher Education (2024) · doi:10.1080/03075079.2023.2246073 · v1.0 |
6.4 (gemini-3.5-flash-lite) | — |
| 18881 | Screened Out |
Did Online Education Exacerbate Contract Cheating during COVID-19 in China? Evidence from Sina Weibo
Journal of Information Technology Education: Research (2023) · doi:10.28945/5181 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 18882 | Screened Out |
Collaborative Behavior or Collusion? Investigating Unpermitted Collaboration on Homework among College Students
Journal of College and Character (2023) · doi:10.1080/2194587x.2023.2190122 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 18883 | Screened Out |
Contract Cheating and Student Stress: Insights from a Canadian Community College
Journal of Academic Ethics (2023) · doi:10.1007/s10805-023-09476-6 · v1.0 |
2.7 (gemini-3.5-flash-lite) | — |
| 18884 | Screened Out |
The Plagiarism Pandemic: Inspection of Academic Dishonesty during the COVID-19 Outbreak Using Originality Software
Education and Information Technologies (2024) · doi:10.1007/s10639-023-11967-3 · v1.0 |
4.0 (gemini-3.5-flash-lite) | — |
| 18885 | Screened Out |
Teaching the What, Why, and How of Academic Integrity: Naturalistic Evidence from College Classrooms
Journal of College and Character (2023) · doi:10.1080/2194587x.2023.2224575 · v1.0 |
2.7 (gemini-3.5-flash-lite) | — |
| 18886 | Screened Out |
Crime or Failure of Integrity: What Is the Problem of Contract Cheating Represented to Be in Australia?
Higher Education Policy (2026) · doi:10.1057/s41307-025-00402-6 · v1.0 |
2.4 (gemini-3.5-flash-lite) | — |
| 18887 | Screened Out |
Addressing Cheating in Virtual Proctored Examinations: Toward a Framework of Relevant Mitigation Strategies
Assessment & Evaluation in Higher Education (2023) · doi:10.1080/02602938.2022.2075317 · v1.0 |
3.6 (gemini-3.5-flash-lite) | — |
| 18888 | Screened Out |
Academic Integrity Strategies: Student Insights
Journal of Academic Ethics (2024) · doi:10.1007/s10805-024-09510-1 · v1.0 |
2.4 (gemini-3.5-flash-lite) | — |
| 18889 | Screened Out |
AI-Based Digital Cheating at University, and the Case for New Ethical Pedagogies
Journal of Academic Ethics (2025) · doi:10.1007/s10805-025-09642-y · v1.0 |
4.9 (gemini-3.5-flash-lite) | — |
| 18890 | Screened Out |
Approaches to Learning and Contract Cheating: Exploring the Mediating Roles of Authorship Perceptions
Studies in Higher Education (2025) · doi:10.1080/03075079.2024.2352055 · v1.0 |
3.8 (gemini-3.5-flash-lite) | — |
| 18891 | Screened Out |
Student Performance in Different Modes of Online and In-Person Assessments and Impact on Academic Integrity
Curriculum and Teaching (2023) · doi:10.7459/ct/38.2.06 · v1.0 |
4.6 (gemini-3.5-flash-lite) | — |
| 18892 | Screened Out |
Developing a College Student Feedback Literacy Scale for the GenAI Context
Assessment & Evaluation in Higher Education (2026) · doi:10.1080/02602938.2025.2495048 · v1.0 |
2.6 (gemini-3.1-flash-lite) | — |
| 18893 | No Fulltext |
Exploring Potential Biases in GPT-4o's Ratings of English Language Learners' Essays
Language Testing (2025) · doi:10.1177/02655322251329435 · v1.0 |
6.0 (gemini-3.1-flash-lite) | — |
| 18894 | No Fulltext |
Algorithmic Bias in BERT for Response Accuracy Prediction: A Case Study for Investigating Population Validity
Journal of Educational Measurement (2026) · doi:10.1111/jedm.12420 · v1.0 |
5.9 (gemini-3.1-flash-lite) | — |
| 18895 | Screened Out |
Integrating AI into Computational Thinking: Development and Validation of an Assessment Tool for Higher Education Students
International Journal of STEM Education (2026) · doi:10.1186/s40594-026-00623-0 · v1.0 |
4.3 (gemini-3.1-flash-lite) | — |
| 18896 | Screened Out |
Generative Artificial Intelligence-Supported Self-Regulated Learning (GenAI-SRL) in L2 Writing: Scale Development, Validation, and Short-Form Construction
Metacognition and Learning (2026) · doi:10.1007/s11409-026-09481-1 · v1.0 |
2.9 (gemini-3.1-flash-lite) | — |
| 18897 | No Fulltext |
An Artificial Intelligence-Enhanced Assessment Framework for Analyzing Middle School Science Students' Written Responses
Journal of Educational Measurement (2026) · doi:10.1111/jedm.70044 · v1.0 |
6.7 (gemini-3.1-flash-lite) | — |
| 18898 | Screened Out |
Digital Module 40: Introduction to Machine Learning and Generative AI: From AutoGluon to Amazon Bedrock
Educational Measurement: Issues and Practice (2026) · doi:10.1111/emip.70029 · v1.0 |
4.5 (gemini-3.1-flash-lite) | — |
| 18899 | Screened Out |
Automated Scoring of Short-Answer Questions: A Progress Report
Applied Measurement in Education (2024) · doi:10.1080/08957347.2024.2386945 · v1.0 |
4.8 (gemini-3.1-flash-lite) | — |
| 18900 | No Fulltext |
Automating Creativity Assessment in Engineering Design: A Psychometric Validation of AI-Generated Items of the Design Problem Task
Journal of Engineering Education (2026) · doi:10.1002/jee.70066 · v1.0 |
7.9 (gemini-3.5-flash-lite) | — |