Investigating the motivational and knowledge affordances of conversational AI using induction, concretization and exemplification in math learning

A promising approach to support students' math learning effectively, automatically and at scale within existing learning environments is conversational artificial intelligence (ConvAI). Although previous studies have suggested ConvAI's potential to guide, facilitate and enhance learning, its effects on students' conceptual change and academic motivation—the latter a crucial moderator of conceptual change—in math education remain understudied. Our study expands understanding of how ConvAI can be used to support Algebra learning from a conceptual change perspective. Using a between-subjects, pre- and posttest design, we conducted an experimental study with 151 participants, with the experimental group accessing ConvAI developed with induction, concretization and exemplification teaching strategies. Results showed that participants in the ConvAI group exhibited higher mastery goal orientation and self-efficacy compared with the control group post-intervention. The frequency of visiting recommended learning resources by ConvAI significantly predicted participants' motivation changes, with increased visits correlating with higher motivation. Additionally, although there was no significant main effect on misconceptions between ConvAI and no-AI participants, significant interaction effects on misconceptions emerged between treatment conditions and student motivation. Our findings, revealed through open-sourced implementations, provide support and implications for educational practitioners and researchers to design and develop pedagogically meaningful ConvAI for math learning.