Mobile-based artificial intelligence chatbot for self-regulated learning in a hybrid flipped classroom

Given the importance of self-regulated learning (SRL) in flipped learning in higher education, this study explored the role of a mobile-based artificial intelligence (AI) chatbot in enhancing SRL among university students enrolled in a flipped business course. The chatbot supported students by providing SRL prompts in the forethought, performance, and reflection phases. An explanatory sequential mixed-methods design was employed to examine the effectiveness of the chatbot and students' conversation patterns. Survey data from 43 participants revealed that low prior-SRL students significantly benefited from chatbot interaction, while high prior-SRL students surprisingly exhibited a decrease in their SRL scores. Qualitative analysis of extreme cases revealed evident differences in interaction patterns between students whose SRL scores decreased and increased after chatbot use. The findings contribute valuable insights to the expanding field of mobile-based AI chatbots in flipped learning and emphasize the importance of adaptive and personalized interventions for students according to their prior SRL skills.