Vero: A Method for Remotely Studying Human-AI Collaboration Hohenstein, Jess Larson, Lindsay E. Hou, Yoyo Tsung-Yu Harris, Alexa M. Schecter, Aaron Dechurch, Leslie Contractor, Noshir Jung, Malte F. 2021-12-24T17:17:51Z 2021-12-24T17:17:51Z 2022-01-04
dc.description.abstract Despite the recognized need in the IS community to prepare for a future of human-AI collaboration, the technical skills necessary to develop and deploy AI systems are considerable, making such research difficult to perform without specialized knowledge. To make human-AI collaboration research more accessible, we developed a novel experimental method that combines a video conferencing platform, controlled content, and Wizard of Oz methods to simulate a group interaction with an AI teammate. Through a case study, we demonstrate the flexibility and ease of deployment of this approach. We also provide evidence that the method creates a highly believable experience of interacting with an AI agent. By detailing this method, we hope that multidisciplinary researchers can replicate it to more easily answer questions that will inform the design and development of future human-AI collaboration technologies.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.030
dc.identifier.isbn 978-0-9981331-5-7
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Applications of Human-AI Collaboration: Insights from Theory and Practice
dc.subject ai
dc.subject human-ai collaboration
dc.subject method
dc.subject remote
dc.subject teams
dc.title Vero: A Method for Remotely Studying Human-AI Collaboration
dc.type.dcmi text
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
524.09 KB
Adobe Portable Document Format