ARTIFICIAL INTELLIGENCE TOOLS IN EDUCATION SYSTEMS: A STRUCTURED REVIEW OF APPLICATIONS, CHALLENGES, AND IMPLICATIONS

Frowin  Rabanus Kifaru

Frowin Rabanus Kifaru Faculty of Business and Information Sciences, Moshi Cooperative University

Keywords: Artificial intelligence, Machine Learning, Higher education systems, Generative AI, Academic integrity, Security science, Data protection, Systematic review, Cybersecurity

Abstract

Original Research Article

DOI: https://doi.org/10.37458/ssj.7.1.8

Artificial intelligence (AI) is increasingly transforming higher education through applications in teaching, assessment, research, and institutional management. However, existing studies remain fragmented and often overlook governance, security risks, and ethical implications. This study presents a systematic review of AI tools in higher education from a security science perspective. Using PRISMA 2020 guidelines, peer-reviewed studies published between 2020 and 2025 were analyzed through thematic synthesis. The findings identify four major categories of AI tools: generative AI, learning analytics systems, intelligent tutoring systems, and administrative decision-support tools. While these technologies enhance efficiency and personalization, they introduce risks related to academic integrity, data privacy, algorithmic opacity, and system dependency. To address this gap, the study proposes a weighted Confidentiality–Integrity–Availability (CIA) model for quantifying AI-related risks, alongside a governance framework for institutional risk management. The results emphasize that effective AI adoption requires robust governance, ethical safeguards, and human-centered oversight. The study contributes a structured and measurable approach to evaluating AI risks in higher education systems.

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