The mediating effects of needs satisfaction on the relationship between teacher support and student engagement with generative artificial intelligence (GenAI) chatbots from a self-determination theory (SDT) perspective

Thomas K. F. Chiu

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EFL/ESL Teaching and Learning Artificial Intelligence in Education Motivation and Self-Concept in Sports

Abstract

Abstract Generative artificial intelligence (GenAI) chatbots, such as ChatGPT and ERNIE Bot, are documented to influence student learning experience and student engagement. However, factors affecting student engagement in GenAI chatbots learning context are less understood. Self-determination theory (SDT) suggests student basic needs satisfaction–autonomy, competence, and relatedness– are associated with student behavioral, cognitive, emotional and agentic engagement. Teacher support– autonomy, structure, and involvement– derived by the three SDT needs. Hence, this study aims to clarify how factors (i.e., teacher support, student needs satisfaction) affect student engagement in this GenAI context in second language (L2) education. It examines the mediating effect of needs satisfaction on the relationship between teacher support and student engagement with a chatbot Data was collected from 364 university students through a questionnaire. The participants learned English as second language with ERNIE Bot under teacher support for four sessions. Our results revealed that needs satisfaction could partially mediate the relationship between teacher support and student behavioral, cognitive, and agentic engagement. They also suggest that needs satisfaction fully mediates the relationship between teacher support and emotional engagement. These suggests that GenAI chatbots better emotionally engage students in language learning than teachers do. One possible explanation for this is that students found learning with chatbots to be enjoyable and stress-free in L2 education. The results suggest teachers should take AI chatbot’s affordance and student feeling into account for emotional engagement when using GenAI chatbots. We suggest future studies should include additional factors such as personality and peer support.

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References

2000Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being.2014A new criterion for assessing discriminant validity in variance-based structural equation modeling 1998Principles and Practice of Structural Equation Modeling 2006Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge 2020Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions 2003Sense of relatedness as a factor in children's academic engagement and performance.2014Basic psychological need satisfaction, need frustration, and need strength across four cultures 2011Framing student engagement in higher education

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2025When GenAI chatbots meet self-determination: unpacking Chinese EFL learners’ engagement in chatbot-assisted language learning

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