Artificial Intelligence and Learning Gaps: Evaluating the Effectiveness of Personalized Pathways

Abstract

The integration of Generative AI (GAI) in education has opened new possibilities for personalized learning, yet its effectiveness in mitigating learning gaps remains underexplored. This study examines the impact of Personalized Learning Pathways (PLPs), generated through AI models (Gemini 2.5 Pro, ChatGPT 5), on secondary school students’ learning outcomes. Using a short-term longitudinal panel design, the research compares homogeneous instructional strategies with AI-driven personalized learning to assess differences in knowledge acquisition and cognitive skill development. Findings indicate that AI-generated PLPs significantly reduce lower-order learning gaps, though higher-order skills remain challenging. The study also reveals that learning styles influence student engagement with AI-driven education, suggesting that hybrid models combining AI and teacher mediation may optimize outcomes. These findings contribute to the ongoing discourse on AI in education, emphasizing the need for equitable, adaptive, and ethical AI applications in learning environments.

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