A Study on the Mechanisms of Enhancing Learning Motivation in Educational Psychology Courses in the Context of Artificial Intelligence

Abstract

This study investigates the mechanisms through which artificial intelligence (AI) enhances learning motivation in Educational Psychology courses. Recognizing that such courses are often characterized by abstract theories and low student engagement, the research adopts a qualitative approach based on Grounded Theory to explore students’ experiences with AI-supported learning. Data were collected through semi-structured interviews with ten undergraduate students majoring in English (Education Track), all of whom had prior experience using AI tools in their studies. The findings reveal that AI influences learning motivation through multiple interrelated pathways. Specifically, AI provides cognitive support by simplifying complex concepts, enhances learning efficiency by organizing knowledge and reducing workload, and fosters interactive engagement through dialogic learning experiences. These factors collectively contribute to the satisfaction of students’ psychological needs for competence, autonomy, and relatedness, thereby promoting intrinsic motivation. However, the study also identifies potential negative effects, including over-reliance on AI leading to superficial learning and reduced independent thinking, as well as issues related to trust in AI-generated content. A theoretical model is proposed to illustrate both the facilitating and inhibiting mechanisms of AI in shaping learning motivation. The results highlight that the impact of AI is dynamic and moderated by factors such as usage strategies, trust levels, and learners’ initial motivation. This study contributes to the literature by providing a process-oriented understanding of AI-supported motivation and offers practical implications for the balanced and pedagogically guided integration of AI in higher education.