From disengaged to self-determined: a latent transition analysis of students' AI learning motivation

From disengaged to self-determined — a year-long study of 2,086 secondary students across 53 schools in an AI curriculum, using Latent Transition Analysis to map students' AI learning motivation over time. Grounded in Self-Determination Theory, it identifies three motivational profiles (Disengaged, Developing, Self-Determined) at both pre- and post-test, shows most students maintaining or advancing toward higher profiles, and finds that those who reached or stayed in the Self-Determined profile showed the greatest gains in AI literacy.

Liang, Chiu, Yau, Meng, Yam, Chai & King (2026) extend Self-Determination Theory to the emerging context of AI education, moving beyond short-term AI-literacy snapshots to study how students' motivation develops across a sustained curriculum. The central message is that supporting students' psychological needs — competence, relatedness, and autonomy — drives sustained engagement and stronger AI-learning outcomes.

Method

• Design: Year-long AI curriculum with pre- and post-tests; Latent Transition Analysis to identify motivational profiles and transitions.
• Sample: 2,086 secondary students across 53 schools.
• Framework: Self-Determination Theory, conceptualizing AI learning motivation through perceived need satisfaction: competence (confidence and AI knowledge mastery), relatedness (AI for social good and ethical engagement), and autonomy (behavioral intention to continue learning AI).

Key Findings

• Three stable motivational profiles: Disengaged (low), Developing (moderate), and Self-Determined (high) need satisfaction, consistent at both time points.
• Developmental transitions: Most students maintained or moved toward higher motivational profiles over the year.
• Equity pattern: Female students and those with more prior AI learning experience were more likely to transition to higher motivational profiles.
• Motivation → AI literacy: Students who transitioned into or remained in the Self-Determined profile showed the greatest improvements in AI literacy.

Implications

• For AI literacy educators: motivation is not a fixed trait — supporting students' competence, relatedness, and autonomy can move them toward self-determined profiles with measurably better outcomes.
• For K-12 and AI curriculum design: sustain psychological-need support across the full curriculum rather than relying on short-term AI-literacy instruction; attend to the differential starting points of female students and those with less prior AI experience.
• For SDT research: extends need-satisfaction theory to AI learning, showing the motivational profiles replicate in this emerging domain and predict learning gains.

Connected Concepts

• Self Determination Theory
• Motivation
• AI Literacy
• K 12
• Student Engagement
• Self Efficacy
• Self Regulated Learning
• AI Education

Connected Articles

• Niri Steam AI Literacy Review 2026 — AI literacy review across STEAM education
• Liu AI Literacy Interventions Meta Analysis 2026 — Meta-analysis of AI literacy interventions
• AI Literacy Assessment Misalignment — Self-reported vs. performance-based AI literacy
• Students Engagement With Generative AI In Academic Learning A Self Determination — SDT and student engagement with GenAI

Citation

Liang, S., Yau, K. W., Meng, H., Chiu, T. K. F., Yam, Y., Chai, C. S., & King, I. (2026). From disengaged to self-determined: a latent transition analysis of students' AI learning motivation. Education and Information Technologies.