A Position Paper: Exploring the Self-Regulated Learning in AI-Human Collaborative Classrooms: Applications, Challenges, and Future Research Directions

The rapid integration of artificial intelligence (AI) into educational environments has given rise to AI-Human collaborative classrooms, where AI tools complement human instruction to enhance learning outcomes. This position paper explores the role of self-regulated learning (SRL) in these hybrid settings, emphasizing its potential to empower learners amid technological advancements. Drawing on prominent SRL models, such as Zimmerman's cyclical phases and Pintrich's framework, the paper examines applications of SRL in AI-enhanced education, including chatbot integration and teacher-guided instruction. It highlights benefits like personalized feedback and increased accessibility, while addressing challenges such as data privacy, algorithmic bias, and over-reliance on AI. Two primary approaches to embedding SRL are discussed: adaptive chatbots that scaffold all phases of regulation and teacher-delivered strategies that foster human-AI synergy. Finally, future directions are proposed across chatbot development, educational practices, and research, advocating for multi-functional AI tools, comprehensive teacher training, and empirical studies on SRL efficacy. This work posits that SRL is essential for realizing equitable, effective AI-Human collaboration in classrooms, paving the way for resilient, learner-centered education.