RT Journal Article
T1 Talking to machines: how communication style shapes student engagement with AI tutors
A1 Labrado Antolín, María Isabel
AB As artificial intelligence (AI) chatbots become integral to higher education, this qualitative study explores how undergraduate students interact with them during business strategy tasks. Grounded in the Value-Based Adoption Model and utilizing ATLAS.ti for content and co-occurrence analysis, this study analyzes emotional tone and cognitive strategies in 15 student–AI conversations. Students who used a relational tone and followed up with questions demonstrated deeper critical thinking, whereas those who employed neutral tones and passive inquiries showed lower engagement. Co-occurrence analysis highlighted key patterns, such as neutral tone and simple inquiries. Findings suggest that socio-affective alignment in human–AI interaction fosters higher-order thinking, providing pedagogical insights into how AI integration can enhance both cognitive depth and emotional engagement in learning environments.
PB Star Scholar Press
SN 3069-0072
YR 2026
FD 2026-01-01
LK https://hdl.handle.net/20.500.14352/128499
UL https://hdl.handle.net/20.500.14352/128499
LA eng
NO Labrado, M. (2026). Talking to machines: How communication style shapes student engagement with AI tutors. American Journal of STEM Education, 19, 37-58. https://doi.org/10.32674/t2qnzc90
DS Docta Complutense
RD 14 may 2026