A qualitative case study of socioaffective communication tone and cognitive engagement in student – AI tutor interactions in higher education

As AI chatbots become integral to higher education, understanding student interaction during cognitively demanding tasks is critical. This qualitative case study examines socioaffective communication and cognitive engagement in undergraduate student–AI interactions in an international business strategy course. Informed by socioaffective human–AI interaction and VAM, it analyzes 10 conversation logs from a 45-minute task using qualitative content and co-occurrence analysis. Results show that relational communication and follow-up questioning are linked to higher-order strategic inquiry and collaborative engagement, while passive prompting and factual requests are linked to lower-complexity analysis. Neutral tone was most prevalent. This study contributes to research on socioaffective human–AI interaction by showing how conversational styles shape cognitive engagement in AI-supported learning. Implications for AI-integrated instructional design are discussed.