AI-Driven Personalized Learning Paths: Opportunities and Challenges for Inclusive Education - Ardi Azhar Nampira; Chak Sothy; Sokha Dara (Yayasan Adra Karima Hubbi, 2025)
Background. This study addresses the growing integration of artificial intelligence (AI) in education, particularly its potential to design personalized learning paths that respond to diverse learner needs within inclusive education contexts. Despite increasing adoption, critical questions remain regarding equity, accessibility, and pedagogical effectiveness when AI-driven systems are implemented across heterogeneous student populations Purpose. The primary objective of this research is to examine both the opportunities and challenges associated with AI-driven personalized learning paths in supporting inclusive education. Method. The study employs a mixed-methods approach, combining a systematic literature review with qualitative analysis of selected empirical case studies from primary, secondary, and higher education settings. Data were analyzed thematically to identify patterns related to personalization mechanisms, learner inclusion, ethical considerations, and institutional readiness. Results. The findings indicate that AI-driven personalization can enhance learner engagement, adaptive pacing, and differentiated instruction, particularly for students with diverse abilities and learning profiles. However, significant challenges persist, including algorithmic bias, data privacy concerns, unequal access to digital infrastructure, and limited teacher capacity to critically mediate AI-supported learning. Conclusion. The study concludes that while AI-driven personalized learning paths hold substantial promise for advancing inclusive education, their effectiveness depends on transparent algorithm design, strong ethical governance, and sustained professional development for educators to ensure technology serves pedagogical and social inclusion goals rather than exacerbating existing inequalities.