Vero: An accessible method for studying human–AI teamwork

Aaron Schecter, Jess Hohenstein, Lindsay Larson, Alexa Harris, Tsung Yu Hou, Wen Ying Lee, Nina Lauharatanahirun, Leslie DeChurch, Noshir Contractor, Malte Jung

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Despite the recognized need 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 standard video conferencing platform, a set of animations, 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 researchers regardless of background can replicate it to more easily answer questions that will inform the design and development of future human–AI collaboration technologies.

Original languageEnglish (US)
Article number107606
JournalComputers in Human Behavior
Volume141
DOIs
StatePublished - Apr 2023

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • General Psychology

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