Bridging Motivation and AI in Education: An Activity Theory Perspective

After the pandemic, research on Artificial Intelligence (AI) in the field of education has seen a significant increase globally. However, a few studies conducted before the pandemic addressed the problem of supporting intrinsic motivation in students, crucial for the quality of learning and knowledge retention. This study explores how this topic is covered in recent research, by conducting a cross-disciplinary literature review and critical discourse analysis under the theoretical framework of Activity Theory (AT). It aims to identify the coverage extension of all types of relationships between nodes in the educational activity system, with special attention to Subject (students) and Object, as this central relationship embodies the motive-driven nature of human activity. The analysis incorporated 69 articles from Scopus published from 2020 until now. The results demonstrate the coverage about only some relationships like: Subject-Tools (students' interaction with AI technology), Tools-Object (AI technologies development), and Tools-Community (adapting AI within an educational community). The Subject-Object relationship remains unexplored. Practical implications include refocusing on intrinsic motivation, emphasising epistemological needs, meaning, and choice. This involves evaluating the benefits and risks of AI in specific educational cases. Theoretical implications involve exploring how to sustain students' intrinsic motivation in the context of AI implementation.