AI Misuse in Education and Academic Integrity Risks: Scoping Review with ☸️SAIMSARA

Name: SAIMSARA Evidence Object digital::AI_MISUSE_SS
Creator: SAIMSARA
License: https://saimsara.com/license/

SAIMSARA

doi:10.62487/saimsara44361107

SAIMSARA Journal

Machine-Readable Science • ISSN 3054-3991

AI Misuse in Education and Academic Integrity Risks: Scoping Review with ☸️SAIMSARA

Digital Health & Biotech

Issue 3, Volume 1, 2026

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DOI: 10.62487/saimsara44361107

Editorial note

• Last update: 2026-11-06 08:07:07

What is this paper about

How serious is AI-assisted cheating in education, and can it be reliably detected? This scoping review examines the evidence on academic misconduct, the limitations of AI detection tools, and practical strategies to protect academic integrity. Discover why redesigning assessments may be more effective than trying to catch students using AI.

World ID verified human editor review This editorial review was submitted from a SAIMSARA account verified as a unique human by World ID.

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Video summary generated from this ☸️SAIMSARA evidence map. Full reference-linked paper and evidence JSON are available after purchase.

Abstract: This scoping review aims to characterize the scope, determinants, and consequences of AI misuse in educational settings, and to synthesize the evidence on detection, prevention, and pedagogical countermeasures across diverse learner populations, disciplines, and institutional contexts. The review uses 127 references and builds its evidence map from 228 original studies with 144869 total participants/sample observations (topic-deduplicated ΣN). AI misuse in education appears to be less an isolated individual failing than a structural vulnerability, arising where highly capable tools, student pressure and rationalization, and weak institutional guidance converge. AI systems answered at least 65.8% of examination questions correctly across technical and natural science courses, highlighting how conventional unsupervised assessments can be completed without genuine learning. Detection tools showed accuracy ranging from 55.29% to 98% across tools, which suggests that detection alone cannot safeguard assessment validity and supports a role for assessment redesign, AI literacy, and explicit institutional policy. Because the evidence is dominated by cross-sectional, self-reported surveys, these associations should be interpreted cautiously rather than as causal effects. Future longitudinal and controlled studies should test whether redesigned assessments and clear AI-use policies reduce observed misuse while preserving students' critical thinking over time.

Keywords: AI misuse; academic integrity; AI-assisted cheating; generative AI in education; academic dishonesty; AI plagiarism; AI detection tools; ethical AI use; assessment redesign; critical thinking

Review Stats

Final search date and database lock: 2026-11-04 14:59:18 CET
Plan: Pro (expanded craft tokens; source: Semantic Scholar)
Source: Semantic Scholar
Total Abstracts/Papers: 1072462
Downloaded Abstracts/Papers: 849
Included original and non-original Abstracts/Papers (all): 526
Included original Abstracts/Papers (Vote counting by direction of effect): 228
Reference Index (links used in paper): 127
Total participants/sample observations (topic deduplicated ΣN): 144869

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Reference Index (127)

[1] Structured Development of Learning and Assessment Tasks to Prevent Generative AI Misuse and Enhance AI Literacy in the Faculty in Physiotherapy Education — https://doi.org/10.1177/23821205251378794
[2] Prevention of Artificial Intelligence (AI) Misuse in Online Medical Education — https://doi.org/10.62487/8ny3zh09
[3] A learning tool or hazard? Concerns related to AI misuse in social work courses — https://doi.org/10.1080/02615479.2025.2508510
[4] Examining The Determinants of AI Misuse Among Students in Indonesia: An Analysis Through The Fraud Diamond Framework — https://doi.org/10.33603/jka.v9i1.10442
[5] AI Chatbot Misuse in Education: Ethical and Pedagogical Risks — https://doi.org/10.56278/asten.vi.3283
[6] Detection of GPT-4 Generated Text in Higher Education: Combining Academic Judgement and Software to Identify Generative AI Tool Misuse — https://doi.org/10.1007/s10805-023-09492-6
[7] Behavioral Intent, Usage, and Misuse of AI Chatbots among College Students — https://doi.org/10.1109/waie67422.2025.11381054
[8] "You're not learning": A layered formative intervention to turn AI misuse into a learning opportunity — https://doi.org/10.1145/3786228.3786244

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