The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
From Detection to Prevention: A Human-Centered Design (HCD) Approach to Mitigating AI Misuse in Learning
Open accessAbstract
The rapid integration of artificial intelligence (AI) tools in educational settings has created unprecedented challenges for academic integrity, with studies indicating that 89% of students are familiar with ChatGPT and 43% have used AI tools for academic work, while current detection-based approaches have proven inadequate, achieving only 26% accuracy in identifying AI-generated content. This study develops and validates a comprehensive Human-Centered Design (HCD) framework for mitigating AI misuse in educational contexts, shifting from reactive detection strategies to proactive prevention-oriented approaches that prioritize stakeholder engagement and ethical AI integration. Using a two-phase mixed-methods approach combining Nominal Group Technique (NGT) and Fuzzy Delphi Method (FDM) with seven experts, the research achieved exceptional expert consensus with an overall average agreement of 98% across all evaluated components. Eleven core elements were validated and organized into five interconnected dimensions: Core Design Principles (emphasizing learner agency and educator empowerment), Implementation Strategies (featuring progressive disclosure and reflection mechanisms), Assessment & Feedback Systems (incorporating authentic assessment and ethical analytics), Stakeholder Engagement (encompassing co-design processes), and Ethical Safeguards (focusing on bias detection and privacy protection). Four components achieved perfect 100% expert consensus, while the low-low…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Mustapha et al. (2025), taking a human-centered design approach to AI misuse mitigation, argue that the detection paradigm is structurally flawed as a primary integrity strategy because it frames integrity as a technological problem rather than an educational one.”
From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Mustapha et al. (2025)
Citations
Cites
Determinant Factors of Untreated Dental Caries and Lesion Activity in Preschool Children Using ICDAS
Pinto-Sarmento, T. C. D. A., de Abreu, M. H. N. G., Gomes, M. C., Costa, E. M. M. D. B., Martins, C. C., Granville‐Garcia, A. F., & Paiva, S. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150116 · Cited by 55
Bottenberg, P., Jacquet, W., Behrens, C., Stachniss, V., & Jablonski‐Momeni, A. (2016) · BMC Oral Health · doi:10.1186/s12903-016-0291-z · Cited by 26
Cited by
Sorted by citations
GİRAY, L., CANER, H. N., & AMOOZEGAR, A. (2026) · Journal of Narrative and Language Studies · doi:10.59045/nalans.2026.109
Harun, A., & Khairani, A. Z. (2026) · International Journal of Modern Education · doi:10.35631/ijmoe.829056
Metadata
- Authors
- Mustapha, R., Ibrahim, N., Ayub, M. N., Jaafar, A. B., Jusoh, M. K. A., & Mahmud, M.
- Affiliations
- Universiti Teknologi MARA (MY)
- Venue
- International Journal of Basic and Applied Sciences
- Publisher
- Science Publishing Corporation
- Published
- 2025-08-07
- Volume
- 14
- Issue
- 4
- Pages
- 159-167
- ISSN
- 2227-5053 (ISSN-L)
- OpenAlex
- W4413132696
- Cited by
- 1
- References
- 2
- Citations by year
- 2026: 1
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal of Basic and Applied Sciences (publishedVersion)
- Science Publishing Corporation (Science Publishing Corporation Inc.) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- academic integrity, AI-generated content detection, ethical integration of AI, stakeholder engagement, co-design, learner agency, teacher empowerment, progressive disclosure, bias detection, privacy preservation, human-centered AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: From Detection to Prevention: A Human-Centered Design (HCD) Approach to Mitigating AI Misuse in Learning. https://app.cochat.ai/papers/10.14419/3t4ghz09
- The paper
- Mustapha, R., Ibrahim, N., Ayub, M. N., Jaafar, A. B., Jusoh, M. K. A., & Mahmud, M. (2025). From Detection to Prevention: A Human-Centered Design (HCD) Approach to Mitigating AI Misuse in Learning. International Journal of Basic and Applied Sciences. https://doi.org/10.14419/3t4ghz09
