A Systematic Literature Review of Data Privacy in AI-Driven Educational Platforms

Abstract

Artificial Intelligence (AI) driven educational platforms are transforming teaching and learning experiences due to the customization that enhances personalized learning and student engagement. Despite the affordances of AI-driven education platforms, concerns about data privacy, ethical issues on data handling, and regulatory compliance limit the wide adoption of such educational platforms. . Adding to this is limited literature that comprehensively explains the types of AI-driven educational platforms, the challenges of the existing AI- driven educational platforms, and the strategies for ensuring data privacy in the existing AI-driven educational platforms. This study presents findings from a Systematic Literature Review (SLR, guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) model to document the types of AI- driven educational platforms discussed in literature, the key challenges of ensuring data privacy in IA-driven educational platforms as well as the strategies for ensuring the security of data accessed and used in the AI- driven educational platforms. , Included in this study were 27 journal articles drawn from IEEE and Google Scholar. The results of this study categorized the AI-driven edicational platforms into Learning Management System (LMS)-focused studies, Adaptive Learning and Intelligent Tutoring System studies, Learning Analytics and AI-Personalized Learning Platform studies, AI-enabled educational tools and automated scoring system studies and General AI education systems. Furthermore, several challenges of these AI-driven educational platforms were identified, which include data privacy, data breaches, bias in AI-driven platforms and the complexities of implementing the AI-driven educational platforms. The strategies that can be implemented to ensure data privacy observed in the study include data encryption, user authentication, regular audits, adherence to General Data Protection Regulation (GDPR) and differential privacy. These results allow education policy makers to develop policies and guidelines to ensure the responsible and secure use of AI- based educational platforms, since there are few SLRs that have more detailed information about what types of AI-driven educational platforms, challenges of AI-driven educational platforms and strategies for ensuring data privacy in AI-driven educational platforms.

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