Authors: Ritesh Kumar
Abstract: The integration of Artificial Intelligence (AI) in education has transformed traditional instructional methods by enabling real-time data-driven personalization of learning. This qualitative case study investigates the implementation of an AI-powered personalized learning platform within a blended learning environment at a private secondary school in Bengaluru, India. The study aims to explore how AI supports personalized learning in practice, the experiences of students and teachers using the system, and the broader implications for pedagogy, curriculum, and educational equity. Blended learning—combining face-to-face instruction with digital platforms—has gained traction in recent years, especially with the rise of hybrid learning post-COVID-19. Within this context, AI promises a transformative potential to analyze individual learning patterns and provide customized pathways for student progress. However, the successful integration of AI tools into everyday teaching remains a challenge, particularly in diverse educational contexts. This study adopts a qualitative case study design to provide in-depth insight into how AI can both support and complicate the goals of personalized learning. Data were collected through semi-structured interviews with six secondary school students, three teachers, and one administrator; classroom observations during AI-facilitated sessions; and analysis of related documents such as lesson plans and platform analytics. Thematic analysis was used to code and interpret qualitative data, focusing on key themes such as learner engagement, teacher adaptation, infrastructural readiness, and ethical concerns around data use. Findings indicate that AI facilitated adaptive learning, increased learner autonomy, and allowed for differentiated instruction that better met the needs of both high-achieving and struggling students. Teachers reported a shift in their roles—from content deliverers to learning facilitators—which many found empowering but also challenging due to limited professional development. While students appreciated the gamified and interactive nature of the platform, some experienced anxiety when faced with continuous feedback or algorithm-driven performance tracking. Several barriers to effective implementation were identified, including inconsistent access to digital devices, unreliable internet connectivity, and concerns over student data privacy. Furthermore, the importance of aligning AI outputs with curriculum objectives and local pedagogical practices was emphasized. Ethical considerations, particularly the opaque nature of algorithmic decisions and the lack of digital literacy among students, emerged as critical areas needing attention. This study concludes that while AI can significantly enhance personalized learning within blended environments, it is not a one-size-fits-all solution. The findings offer valuable implications for educators, policymakers, and ed-tech developers committed to responsible and inclusive use of AI in education.
DOI: http://doi.org/10.5281/zenodo.17814800
