Using Generative Artificial Intelligence to Improve Chinese College Student Learning Outcomes: Self-determination Theory and Self- Efficiency Theory Perspectives

Generative Artificial Intelligence (GAI) tools have shocked the world with their unprecedented functionality and have created a huge buzz in the field of education. Due to the personalized and real-time interactive nature of GAI tools, we believe that students' interactions with GAI will influence their learning outcomes through the impact of motivational factors. Despite the advantages of these tools in the field of higher education, there is still a lack of research on the specific mechanisms of integrating this technology into the learning process. Therefore, this study used self-determination theory and self-efficacy theory to investigate the relationship between GAI interactions and student learning outcomes to fill this gap. The results of this study, through an empirical study of college students in a second-tier city in China, suggest that GAI interactions can positively influence student learning outcomes through academic self-efficacy (ASE), creative self-efficacy (CSE), creativity, and motivation to learn. This study creatively added the creativity factor into the integration of GAI and learning, providing students with empirical evidence for using GAI tools to improve their academic performance, and informing the further integration of GAI into academic life in the future.