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ISSN Approved Journal | | IMPACT FACTOR 8.16 | | eISSN: 2582-5542 | |  Free Crossref DOI 

Fast Publication within 2 days after Acceptance | | Low Article Processing Charges | | Peer Reviewed and Referred Journal

Research and review articles are invited for publication in November 2026 (Volume 28, Issue 2) Submit Paper

AUTOMATED NDPA COMPLIANCE TOOLKIT FOR ARTIFICIAL INTELLIGENCE-DRIVEN STUDENT MANAGEMENT SYSTEMS IN NIGERIAN UNIVERSITIES

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  • AUTOMATED NDPA COMPLIANCE TOOLKIT FOR ARTIFICIAL INTELLIGENCE-DRIVEN STUDENT MANAGEMENT SYSTEMS IN NIGERIAN UNIVERSITIES

Jude Chijindu Chukwuemeka *

Miva Open University, Abuja, Nigeria.
* Corresponding Author
ORCID Details
Jude Chijindu Chukwuemeka: https://orcid.org/0009-0001-8369-9114

Research Article

World Journal of Biology Pharmacy and Health Sciences, 2026, 28(01), 021–025

Article DOI: 10.30574/wjbphs.2026.28.1.0497

DOI url: https://doi.org/10.30574/wjbphs.2026.28.1.0497

Received on 30 August 2026; revised on 05 October 2026; accepted on 07 October 2026

Artificial intelligence has become a central pillar of institutional management in Nigerian higher education, processing massive volumes of student data for admissions, predictive analytics, and attendance management. However, this data-intensive transformation operates under the strict oversight of the Nigeria Data Protection Act (NDPA) of 2023. Despite these legal obligations, empirical evidence reveals a systemic governance deficit, characterized by bundled consent, unlawful cross-border data transfers, and automated decision-making without human oversight. This project aimed to design, develop, and evaluate an Automated NDPA Compliance Toolkit to systematically detect and remediate compliance violations within artificial intelligence-driven Student Management Systems. Design Science Research was adopted as the primary methodology, supported by documentary analysis and comparative technology assessment. The developed toolkit features a three-layer architecture utilizing Python, FastAPI, PostgreSQL, and React.js, operationalizing the NDPA into 25 executable checks across six compliance modules. Testing was conducted using a simulated non-compliant institution and a compliant control system. The toolkit successfully identified all planted violations in the non-compliant system, assigning a compliance health index of 12 percent, while accurately passing all checks for the compliant system with a 100 percent score. The confusion matrix indicated 100 percent detection accuracy and specificity. The findings establish that software can effectively bridge the gap between statutory requirements and practical implementation by translating legal provisions into verifiable technical controls.

Artificial Intelligence; Data Protection; Ndpa 2023; Regulatory Technology; Student Management Systems

https://wjbphs.com/sites/default/files/fulltext_pdf/WJBPHS-2026-0497.pdf

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Jude Chijindu Chukwuemeka. AUTOMATED NDPA COMPLIANCE TOOLKIT FOR ARTIFICIAL INTELLIGENCE-DRIVEN STUDENT MANAGEMENT SYSTEMS IN NIGERIAN UNIVERSITIES. World Journal of Biology Pharmacy and Health Sciences, 2026, 28(01), 021–025. Article DOI: https://doi.org/10.30574/wjbphs.2026.28.1.0497

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