Student motivation and need satisfaction in GenAI-supported classrooms: A self-determination theory perspective

Abstract

This study examines how generative AI (GenAI), specifically ChatGPT, relates to student motivation and basic psychological need satisfaction in lower-secondary education from a self-determination theory perspective. While generative AI is increasingly used in classrooms, it remains unclear how its use is associated with different motivational profiles across instructional contexts. Using a cross-sectional sample of 2,469 students (grades 7–8), we compared two instructional contexts: (a) rubric-based, self-regulated competency-based learning (CBL) implemented with or without ChatGPT integration, and (b) teacher-directed learning (TDL) organized in a traditional 45-minute lesson format with limited self-regulated phases. To capture both overall differences and profile-specific patterns, we combined person-centered latent profile analysis with variable-centered mean comparisons. The results show that ChatGPT use in CBL is associated with higher autonomy support and competence satisfaction, but also with a greater proportion of low-quality motivational profiles compared to non-AI CBL. At the same time, these profiles in the ChatGPT-supported context exhibit higher intrinsic and identified motivation than comparable profiles in both non-AI CBL and TDL. In contrast, relatedness appears less pronounced in the ChatGPT-supported context, which may help explain why the overall motivational distribution does not exceed the already high baseline of non-AI CBL. These findings point to systematic differences in motivational quality associated with ChatGPT use and highlight the importance of considering learner heterogeneity when integrating generative AI into autonomy-supportive instructional settings.

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