A Generative AI-assisted Intervention to Promote High School Students’ Personal Relevance to Math and Interest in the Math Class

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Abstract

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 interview further explored this inconsistency.

Authors

• Zhixing Guo

• Luke Fryer, University of Hong Kong

• Alex Shum, University of Hong Kong