Utilizing generative AI to promote high school students’ personal relevance to math and interest in the math class: An intervention

Relevance plays a pivotal role in student motivation and engagement. Generative AI, with its capacity for personalized and real-time dialogue, offers a promising avenue for promoting students’ identity-based relevance with learning content. This mixed-methods quasi-experimental study examined the effects of generative AI-assisted relevance interventions on 218 Chinese high school students’ relevance as identification with math (i.e., connections of math to students’ identity and sense of self) and their interest in the math class. Utilizing MANCOVA and fully-forward latent-variable SEM, the current study assessed both immediate and sustained effects (two weeks) of two intervention conditions: individualized AI interaction and collective AI interaction. Inconsistent with the initial hypothesis, even though individualized AI condition was more personalized, it was collective AI condition that showed significant effects on students’ relevance as identification with math immediately and one week after the intervention. Follow-up interviews revealed that participants in the individualized AI condition perceived the generative AI-generated identity-based connections to math (e.g., connections of math to their ideal career or life goals) as overly abstract and doubted their practical applicability. Demonstrating the connections of math with students’ proximal concerns or the applicability of math across different domains has been indicated as effective ways to promote students’ relevance as identification with math. Implications for integrating generative AI in authentic educational settings to promote students’ personal relevance to the school subject and interest in the learning content were discussed.