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All Journal Journal of Loomingulisus ja Innovatsioon Research of Scientia Naturalis Scientechno: Journal of Science and Technology Lingeduca: Journal of Language and Education Studies Journal of World Future Medicine, Health and Nursing Journal Emerging Technologies in Education
Ardi Azhar Nampira
Institut Teknologi Sepuluh November
Religion Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Environmental Science Law, Crime, Criminology & Criminal Justice Physics Other

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AI-Driven Personalized Learning Paths: Opportunities and Challenges for Inclusive Education Ardi Azhar Nampira; Chak Sothy; Sokha Dara
Journal Emerging Technologies in Education Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jete.v3i2.3412

Abstract

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.
Co-Authors Ava Lee Chak Sothy Clara Mendes Dorji Wangchuk Ira Wulan Sari Lucas Lima Marcus Tan Ming Pong Ninik Sri Rahayu Wilujeng Nurul Huda Omar Khan Rit Som Roya Zahir Sanjay Sharma Siri Lek Siti Shofiah Sokha Dara Som Chai Thiago Rocha Tiago Costa Utary Rustam Wang Jing Zhang Li