Related Data for: Does the teacher matter? University students’ physiological responses in online interactions with an AI-chatbot and a human teacher

As generative AI (Gen AI) chatbots become more common as learning partners, questions remain about students’ emotional and physiological responses to them. This study used a multimodal design to compare university students’ experiences during a 25‑minute brainstorming session with either a human teacher or Gen AI chatbot. Thirty participants wore EmbracePlus sensors to record heart rate, electrodermal activity (EDA), and skin temperature while completing the task, and completed mood questionnaires before and after brainstorming. Analyses compared mood change scores (controlling for age and gender) and examined physiological data for both temporal patterns and total activation (area-under-the-curve; AUC). While both groups reported improved mood, students brainstorming with a human teacher showed greater gains in positive mood, whereas the chatbot group reported increased stress and discouragement, and exhibited higher cumulative cardiovascular activation. Although physiological change trajectories did not differ by condition, specific AUC measures were associated with mood: higher pulse AUC was linked to negative moods, and higher skin temperature AUC to positive moods. These findings suggest that while human facilitation produces stronger emotional benefits, GenAI chatbots can sustain comparable physiological engagement and serve as valuable complementary tools. Physiological signals also reveal distinctive patterns between bodily states and learning experiences, underscoring the value of integrating multimodal data into research on AI‑mediated education.