Human-GenAI interaction for active learning in STEM education: State-of-the-art and future directions

This systematic state-of-the-art review synthesizes findings from 50 studies examining the integration of GenAI into active learning models (such as problem-based learning, collaborative learning, and inquiry-based learning) within STEM education from high school to graduate levels. The analysis identifies five overarching categories of Human–GenAI interaction: Tutoring, Co-creating, Processing, Coaching, and Simulating, primarily leveraged to support individual learners in developing problem-solving, critical thinking, and computational thinking skills. While the findings highlight GenAI’s potential to support constructivist active learning, its application remains largely individual in scope. Moreover, challenges related to algorithmic bias, information reliability, privacy, and limited domain specificity constrain the orchestration of Human–GenAI interaction synergy, placing significant cognitive and pedagogical demands on both educators and learners when interacting with GenAI-powered applications. Future research should explore how hybrid forms of human– AI intelligence can support more equitable, collaborative, and context-sensitive learning environments. This includes supporting students in developing the competencies necessary to engage with GenAI tools reflectively, purposefully, and meaningfully in ways that enhance active learning. - The analysis identifies five overarching categories of Human–GenAI interaction: Tutoring, Co-creating, Processing, Coaching, and Simulating, primarily leveraged to support individual learners in developing problem-solving, critical thinking, and computational thinking skills - Challenges related to algorithmic bias, information reliability, privacy, and limited domain specificity constrain the orchestration of Human–GenAI interaction synergy - Collaborative applications of GenAI for active learning in STEM education are underexplored, with a focus on individual use