🏷️ Concept
Self-Determination Theory
Self-Determination Theory (SDT) — a psychological theory of human motivation positing that intrinsic motivation and well-being depend on satisfying three basic psychological needs: autonomy, competence, and relatedness. In AI in education, SDT provides a framework for designing AI tools and professional development that support rather than undermine learners' and teachers' motivation.
SDT is increasingly used in AI in education research as a theoretical lens for both learner-facing and teacher-facing AI systems. The theory's central claim — that motivation is not simply a quantity learners have but a quality shaped by the social and technological environment — makes it directly relevant to questions about how AI tools affect engagement, persistence, and learning outcomes. The articles in this wiki apply SDT across three main contexts: teacher professional development, AI-mediated learning engagement, and affective computing.
Key research themes
SDT-based teacher professional development applies the theory's need-supportive principles to prepare educators for AI. Chiu et al. studied 382 secondary school teachers, finding that need-supportive professional development grounded in SDT enhances teachers' AI literacy and fosters sustained behavioral engagement in online professional learning communities. Qualitative analysis identified nine design strategies supporting autonomy, competence, and relatedness — bridging the gap between isolated professional development and professional learning communities.
SDT in AI-mediated learning engagement examines how generative AI tools shape student motivation. Isaeva et al. combined SDT with epistemic network analysis to study students' engagement with generative AI in academic learning. AI Availability Student Motivation explores how AI availability affects student motivation and persistence, connecting to Over Reliance concerns about motivation erosion.
SDT in affective computing applies the theory to emotionally intelligent AI agents. Zheng et al. developed EmoAgent, an SDT-based emotional agent that proactively detects students' emotional states and provides emotion regulation strategies. An 8-week quasi-experiment with 173 sixth graders showed the SDT-based approach significantly outperformed conventional agents in academic achievement, engaged students in positive emotional experiences, and moderated the negative effect of negative emotions on success.
Connections to related concepts
SDT connects directly to Motivation as its parent construct, to Affective Computing and Affective Tutoring for emotion-aware AI design, and to Student Experience for how learners experience AI-mediated environments. The theory's emphasis on autonomy connects to Self Regulated Learning, while its competence dimension connects to Self Efficacy Tutoring Learning and Teacher AI Competency. SDT is particularly relevant to Professional Training and Faculty Development because need-supportive design is a transferable principle for preparing educators to use AI.