Large-Scale User Behavior Analysis in Multimodal AI-Assisted Manual Task Execution

Abstract:Conversational Task Assistants (CTAs) are multimodal dialogue systems that support users in complex real-world tasks such as cooking and DIY through voice, text, image, and video interactions. Prior user studies have focused on controlled settings, leaving limited understanding of real-world CTA usage at scale. In this work, we present a large-scale study of CTA usage based on thousands of users in-the-wild. Our large-scale real-world data analysis unveils new understandings of (i) user-CTA interaction flows, (ii) user intents, (iii) user conversational traits, and (iv) behavioral factors associated with user satisfaction. Our findings reveal key opportunities for future research in CTAs, particularly in user interaction design and task engagement, concluding with concrete design guidelines.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.07594 [cs.HC]

(or arXiv:2609.07594v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.07594 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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