A Comparative Analysis of AI-Driven Personalized Learning Impact on Academic Performance and Engagement Among Nigerian Undergraduates

The adoption of Artificial Intelligence (AI) for personalized learning (PL) is rapidly transforming global higher education. However, institutional type often dictates resource allocation and technological infrastructure, potentially leading to disparate impacts on student outcomes. This study conducted a comparative analysis of the perceived impact of AI-driven personalized learning (AI/PL) on academic performance and academic engagement of undergraduates, measured as student-perceived outcomes, across Nigeria's tripartite higher education structure, namely federal, state, and private universities. Amidst persistent infrastructural and policy disparities in the region, the research sought to determine the relationship between AI utilization and student outcomes and assess whether institutional type significantly mediates this impact. A quantitative descriptive survey research design was employed using a stratified sample of 150 undergraduates (50 from each of the federal, state, and private universities) from Oyo State, Nigeria. Data were collected using a questionnaire, utilizing a 5-point Likert scale. Statistical analysis confirmed a positive and statistically significant correlation between AI utilization and both perceived academic performance (r=0.58, p < 0.01) and academic engagement (r=0.63, p < 0.01). Significantly, One-Way Analysis of Variance (ANOVA) revealed a highly significant difference in the perceived impact scores across the three institutional types for both academic performance (F=9.87, p < 0.001) and academic engagement (F=11.24, p < 0.001). Private universities reported the highest mean scores, followed by federal and then state institutions, demonstrating that the positive effect of AI/PL is heavily moderated by institutional resource availability and infrastructural reliability. The study concludes that AI/PL holds transformative potential, but its successful and equitable realization is contingent upon overcoming structural barriers, particularly inconsistent technological infrastructure in public institutions. It is recommended that national policy mandates robust ICT infrastructure standards and ethical governance frameworks, prioritizing reliable digital access in public institutions for equitable AI integration.