Research Article
Student Motivation and Need Satisfaction in GenAI-Supported Classrooms: A Self-Determination Theory Perspective
Synthesis: Schweder, Hagenauer, and Raufelder (2026) use a cross-sectional sample of 2464 lower-secondary students (grades 7–8) to compare rubric-based self-regulated competency-based learning (CBL) with and without ChatGPT integration and teacher-directed learning, combining person-centered latent profile analysis with variable-centered mean comparisons. ChatGPT use in CBL was associated with higher autonomy support and competence satisfaction but also a greater proportion of low-quality motivational profiles; those profiles nonetheless showed higher intrinsic and identified motivation than comparable profiles elsewhere, while relatedness was less pronounced in the ChatGPT-supported context.
Key Findings
- ChatGPT use in competency-based learning was associated with higher autonomy support and competence satisfaction, but also with a greater proportion of low-quality motivational profiles.
- Low-quality profiles in the ChatGPT-supported context exhibited higher intrinsic and identified motivation than comparable profiles in non-AI CBL and teacher-directed learning.
- Relatedness was less pronounced in the ChatGPT-supported context, helping explain why the overall motivational distribution did not exceed the high baseline of non-AI CBL.
- Learner heterogeneity matters: motivational outcomes of GenAI integration vary systematically across student profiles.
Implications for AI in Education
By combining person-centered and variable-centered methods on a large authentic sample, the study shows that GenAI integration in autonomy-supportive classrooms produces heterogeneous motivational outcomes rather than a uniform effect. The finding that ChatGPT contexts can raise autonomy and competence satisfaction yet weaken relatedness and increase low-quality profiles suggests educators should attend to the social-relational costs of AI-supported learning. It informs Self-Determination Theory, Motivation, and Self-Regulated Learning perspectives on GenAI in K-12 classrooms.
Connected Concepts
Connected Articles
- [liang-ai-learning-motivation-sdt-2026] — AI learning motivation from a self-determination perspective
- [students-engagement-with-generative-ai-in-academic-learning-a-self-determination] — student engagement with GenAI through SDT
Citation
Schweder, S., Hagenauer, G., & Raufelder, D. (2026). Student motivation and need satisfaction in GenAI-supported classrooms: A self-determination theory perspective. Computers and Education Open, 10, 100348.