From disengaged to self-determined: A latent transition analysis of students’ AI learning motivation

Abstract As AI education expands, understanding whether and how students sustain their engagement in AI learning has become increasingly important. Current research has predominantly focused on students’ AI literacy over shorter timeframes, with limited attention to their motivational development. Grounded in Self-Determination Theory (SDT), this study conceptualizes AI learning motivation through students’ perceived needs satisfaction for competence (confidence and AI knowledge mastery), relatedness (AI for social good and ethical engagement), and autonomy (behavioral intention to continue learning AI). A total of 2,086 secondary students across 53 schools participated in a year-long AI curriculum and completed both pre- and post-tests. Latent Transition Analysis was used to identify motivational profiles and examine transitions over time. Three motivational profiles emerged consistently at both time points: Disengaged, Developing, and Self-Determined, representing low, moderate, and high levels of need satisfaction. Although profile configurations remained similar, transitions revealed developmental patterns, with most students maintaining or moving toward higher motivational profiles. Female students and those with more prior AI learning experience were more likely to transition to higher motivational profiles. Furthermore, students who transitioned into or remained in the Self-Determined profile showed the greatest improvements in AI literacy. Findings extend SDT to emerging AI learning contexts and underscore the importance of supporting students’ psychological needs to foster sustained engagement and AI literacy development.