Generative AI Chatbots as Co-regulators: Enhancing Self-Regulated Learning Across Learner Personality Profiles

• In Event: Self-Regulated Learning and Motivation in Technology-Enhanced Environments

Abstract

Generative AI chatbots are increasingly adopted in education, yet their interaction with learner personality traits in self-regulated learning (SRL) contexts remains underexplored. This study examines how AI chatbots serve as adaptive co-regulators by investigating the impact of distinct personality profiles on learners’ SRL strategies. Using latent profile analysis (LPA), epistemic network analysis (ENA), and thematic analysis of interviews with 182 undergraduate students, four personality-based learner profiles emerged. Each profile demonstrated unique patterns of SRL strategy shifts influenced by AI interactions. Findings underscore the multifaceted roles of chatbots—enhancing cognitive engagement, metacognitive strategies, and emotional support. This research provides insights for designing personalized educational technologies that optimize SRL outcomes by aligning chatbot interventions with individual learner characteristics.

Authors

• Xiu-Yi Wu, The Chinese University of Hong Kong

• Thomas K.F. Chiu, The Chinese University of Hong Kong