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B. Etezadi
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Good Enough to Talk To?
Exploring the Acceptance and Social Presence of AI Chatbots for Human-Centered Task Training in Electrical Engineering Education
Generative AI chatbots are increasingly proposed as simulated stakeholders. These stakeholders let engineering students rehearse human-centered communication without the scheduling and access constraints of real interlocutors. In an engineering education context, how students receive such tools, and whether they engage with them as social partners, remains little studied. This paper examines the acceptance and perceived social presence of an AI-simulated non-technical stakeholder in an electrical engineering setting. Fifteen students each completed a single customer discovery interview with a large language model chatbot, framed around the Value Proposition Canvas. The study follows a convergent mixed-methods design, pairing UTAUT-derived acceptance constructs and an adapted social presence scale with a reflexive thematic analysis of the interaction transcripts and written reflections. Acceptance was high but conditional, and tracked how useful students judged the tool rather than how easy it was to use. Social presence was the lowest and most variable of the measured constructs and moved largely independently of perceived usefulness, so a more socially present chatbot did not register as a more useful one. Students valued the tool as an accessible option when real stakeholders were hard to reach, while noting absent empathy, predictable responses, and a risk of over-reliance. Transcripts show some students got the responses they wanted by engineering their prompts rather than by communicating as they would with a real stakeholder. These self-reported patterns indicate that such chatbots are best positioned as carefully bounded preparation before real stakeholder contact rather than as replacements for it.
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Generative AI chatbots are increasingly proposed as simulated stakeholders. These stakeholders let engineering students rehearse human-centered communication without the scheduling and access constraints of real interlocutors. In an engineering education context, how students receive such tools, and whether they engage with them as social partners, remains little studied. This paper examines the acceptance and perceived social presence of an AI-simulated non-technical stakeholder in an electrical engineering setting. Fifteen students each completed a single customer discovery interview with a large language model chatbot, framed around the Value Proposition Canvas. The study follows a convergent mixed-methods design, pairing UTAUT-derived acceptance constructs and an adapted social presence scale with a reflexive thematic analysis of the interaction transcripts and written reflections. Acceptance was high but conditional, and tracked how useful students judged the tool rather than how easy it was to use. Social presence was the lowest and most variable of the measured constructs and moved largely independently of perceived usefulness, so a more socially present chatbot did not register as a more useful one. Students valued the tool as an accessible option when real stakeholders were hard to reach, while noting absent empathy, predictable responses, and a risk of over-reliance. Transcripts show some students got the responses they wanted by engineering their prompts rather than by communicating as they would with a real stakeholder. These self-reported patterns indicate that such chatbots are best positioned as carefully bounded preparation before real stakeholder contact rather than as replacements for it.