A CRITICAL ANALYSIS OF AI-POWERED MOBILE LEARNING SYSTEMS FOR ENHANCED EDUCATIONAL OUTCOMES IN NIGERIAN UNIVERSITIES

Abstract

This paper critically analyzes the proposition of implementing Artificial Intelligence-Powered Mobile Learning Systems (AI-PMLS) as a solution for enhancing educational outcomes within Nigerian universities, addressing challenges like massification and pedagogical lag. While acknowledging the potential benefits—specifically AI-driven personalization and the ubiquity of student-owned mobile devices—the analysis argues that this technological solution is fundamentally threatened by systemic operational deficits. The critique focuses on three major areas: Infrastructure and Economic Reality (data poverty, network instability, and the energy/battery conundrum leading to technological exclusion), Ethical and Policy Challenges (algorithmic bias, the Black Box problem, and data sovereignty concerns), and Institutional Capacity (faculty skill gaps and the risk of pedagogical deskilling). The paper utilizes visual evidence of personal devices to ground the discussion in current student access realities. It concludes that without mandatory Offline-First Design, the establishment of a Digital Equity Fund to mitigate data costs, and Rigorous Ethical Auditing of algorithms, AI-PMLS will likely exacerbate existing digital and socio-economic disparities, ultimately failing to deliver equitable educational enhancement.