Bridging Motivation and AI in Education: An Activity Theory Perspective

Abstract

After the pandemic, research on Artificial Intelligence (AI) in the field of education has seen a significant increase globally. Although this topic is not new to education, very few studies conducted before the pandemic addressed the problem of supporting internal motivation in students, crucial for the quality of learning and knowledge retention. This study explores the extent to which this topic is covered in recent research by conducting a cross-disciplinary literature review within the theoretical framework of Activity Theory (AT). It aims to identify the extent of coverage 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 till present. The results demonstrate coverage of only some relationships: Subject-Tools (students interaction with AI technology), Tools-Object (development of AI technologies), Tools-Community (adapting AI within an educational community). The central relationship between Subject and Object remains unexplored. Practical implications involve developing and integrating AI tools to stimulate intrinsic motivation by emphasizing personal meaning. Theoretical outcomes involve exploring how to foster and sustain students' intrinsic motivation in relation to AI.