Exploring human and AI collaboration in inclusive STEM teacher training: A synergistic approach based on self-determination theory

Abstract

Inclusive STEM teacher training plays a critical role in shaping the future of STEM teaching practices and improving educational outcomes for all students, particularly those from marginalized and underrepresented backgrounds. This study investigates the inclusive collaborative learning framework for enhancing STEM teaching among student teachers, focusing on interpersonal and human-machine (generative artificial intelligence) collaboration. Employing a Self-Determination Theory guided approach, two rounds of exploratory studies were conducted. Study 1 compared the effects of interpersonal collaboration (TSPL: in-Service Teacher-Student Teacher Pair Learning) and human-machine collaboration (CSPL: ChatGPT-Student Teacher Pair Learning). Building on Study 1, Study 2 employed a hybrid inclusive collaborative learning model (iHMCL: integrated Human-Machine Collaborative Learning) with expanded participant demographics, blended course formats, and integrated peer, expert, and AI feedback mechanisms. The two-year iterative empirical research revealed differences in the impact of the three collaborative learning approaches on student teachers' learning. CSPL and iHMCL groups outperformed TSPL in STEM teaching knowledge and cognitive load, while TSPL and iHMCL excelled in STEM teaching ability compared to CSPL. The SDT-based inclusive collaborative learning framework for STEM teacher training proved effective, with noted implications. In the future, the integration of generative artificial intelligence and cross boundary learning in inclusive STEM teacher education will require educators to redefine their roles, emphasizing emotional support, critical thinking, and creativity, ensuring that AI complements rather than replaces hands-on, reality-based learning.