AI-Augmented Student-Centered Learning: Personalization and Agency - Kuok Ho Daniel Tang (Acta Pedagogia Asiana, 2026)
Artificial intelligence (AI) is increasingly integrated into educational environments and is widely recognized as a transformative technology for advancing Student-Centered Learning (SCL). By enabling adaptive instruction, real-time feedback, and learning analytics, AI systems can personalize learning experiences and address diverse learners’ needs. This review synthesizes current research on how AI contributes to key dimensions of SCL, including adaptive content delivery, data-driven feedback, learner agency, and human–AI collaboration. The literature indicates that AI-powered educational technologies can enhance engagement, facilitate individualized learning pathways, and support self-regulated learning by providing timely insights into performance, progress, and learning strategies. Learning analytics and intelligent tutoring systems further allow instructors to better understand learners’ behavior and tailor instructional support, strengthening alignment between teaching practices and students’ needs. However, integrating AI into SCL environments also presents several challenges. Concerns have emerged regarding cognitive offloading and overreliance on AI systems, which may reduce learners’ active problem-solving and critical thinking if not carefully managed. Issues related to algorithmic transparency, data privacy, and equitable access also remain important considerations as educational institutions increasingly depend on data-driven technologies. Moreover, educators continue to play a critical role in guiding the effective use of AI and ensuring that technology enhances rather than replaces meaningful learning processes. By and large, AI has substantial potential to strengthen SCL when implemented as a transparent, supportive pedagogical tool. Effective integration requires balancing algorithmic guidance with learner autonomy and maintaining strong human oversight. Future research should examine long-term impacts on learner agency and self-regulation and develop pedagogical frameworks that support responsible human–AI collaboration in student-centered education.