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

Abstract

Artificial Intelligence (AI) driven educational platforms are transforming education towards personalized learning. Despite the affordances of AI-driven education platforms, concerns about data privacy, ethical issues in data handling, and regulatory compliance limit their widespread adoption. Adding to this is the limited literature that comprehensively explains the types of AI-driven educational platforms, their challenges, and the strategies for ensuring data privacy in them. This study presents findings from a Systematic Literature Review (SLR), guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) model. Included in this study were 27 journal articles drawn from Science Direct, IEEE, Springer Nature Link, and Google Scholar. The results of this study categorized the AI-driven educational platforms into Learning Management Systems (LMSs), adaptive learning, intelligent tutoring systems, learning analytics, and AI-personalized learning platforms, AI-enabled educational tools and automated scoring systems, and general AI education systems. Furthermore, several challenges of these AI-driven educational platforms were identified, which include data privacy, data breaches, bias, and the implementation of the AI-driven educational platforms. The strategies for ensuring data privacy include data encryption, user authentication, regular audits, adherence to the General Data Protection Regulation (GDPR), and differential privacy. These results facilitate the development of policies for ensuring that the AI-based educational platforms are secure, considering the large volumes of data that are collected by these and used in systems.