The Impact of Conversational Delivery Styles on Perceived Autonomy during Collaborative Ideation with Social Robots
J.H. Seo (TU Delft - Electrical Engineering, Mathematics and Computer Science)
R. Weijers – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
C.R.M.M. Oertel Genannt Bierbach – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
G. Lan – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
As artificial intelligence transitions into physically embodied collaborative roles, systems must be designed to support rather than thwart human autonomy. In a between-subjects experiment (N = 20 dyads), we investigated how the conversational delivery style (Assertive vs. Supportive) of an LLM-driven Pepper robot affected perceived user autonomy and creative output during a live campus-improvement brainstorming task. While both conditions yielded a similar quantity of unique ideas (Assertive M = 5.90, Supportive M = 5.70, t(18) = 0.40, p = 0.697), the Supportive delivery style marginally increased autonomy frustration (M = 2.43) compared to the Assertive style (M = 2.03; t(38) = 2.02, p = 0.050, d = 0.64). Qualitative analysis of the interaction logs revealed that the Supportive robot's conversational pacing frequently triggered interruptions during natural cognitive silences, actively seizing the conversational floor and disrupting the dyads' workflow. These findings demonstrate that in live human-robot interaction, the structural implementation of interaction timing and turn-taking is more critical to preserving perceived user autonomy than empathetic semantic framing.