Exploring the Design of Social Robot User Interfaces for Presenting Data-Driven Stories

Tabletop social robots are becoming increasingly common, not only as social companions but as presenters and orators of information. We present an exploration of utilizing robots as a multimodal presentation tool to communicate data-driven facts. Our exploration is inspired by the wealth of research on data videos (DVs) as these have become mainstream sources for swiftly conveying data-driven information to a mass audience. We first analyze 48 DVs that contain visible narrators (presenters who are visible in the video frames) as our source for understanding the techniques used to convey factual information via presenters. Twelve dimensions across four factors (presenter-grounded; narrative-grounded; viewer-engagement-related; and data-visualization-related) were identified. These factors were carefully arranged in designing presenters to engage the audience with the video content. We adapt these findings to the design of an expressive social tabletop robot that can communicate data-driven knowledge to its audience. Supported by four design sessions with expert content creators and designers, we provide nine design implications for designing multimodal presentations with an expressive tabletop social robot. We conclude with the possible application potentials of this unique data presentation modality.

Cite: Anuradha Herath, Samar Sallam, Yumiko Sakamoto, Randy Gomez, and Pourang Irani. 2023. Exploring the Design of Social Robot User Interfaces for Presenting Data-Driven Stories. In Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia (MUM ‘23). Association for Computing Machinery, New York, NY, USA, 321–339. https://doi.org/10.1145/3626705.3627781