It's Not How They Talk, It's Who They Are: Learning Style, Not Interaction Modality, Predicts AI Newbies’ Engagement with Educational Chatbots

Abstract

Educational AI chatbot (EAIC) research has focused heavily on interaction modality, reflecting the assumption that modality drives engagement. This study challenges that assumption. We examined how students with no prior AI experience (“AI Newbies”, n = 96) engaged with an EAIC during a ten-week course. Contrary to expectations, interaction modality did not significantly affect engagement behaviors. Instead, learning style emerged as a stronger predictor. Verbal group students gravitated toward critical analysis questions. Solitary group toward conceptual exploration. Social group toward assessment guidance. Students also predominantly used mobile devices despite limited mobile support, and 77% migrated to general-purpose chatbots by the end of the course, preferring comprehensiveness over the controlled accuracy of curriculum-aligned systems. These findings suggest that designing for AI Newbies requires attending to individual learning differences rather than assuming a one-size-fits-all approach. We propose the Optimize, Personalize, Integrate (OPI) framework to guide EAIC development for this underserved population.