Who should be my teammates: Using a conversational agent to understand individuals and help teaming

Ziang Xiao, Michelle X. Zhou, Wat Tat Fu

Research output: Contribution to conferencePaper

Abstract

We are building an intelligent agent to help teaming efforts. In this paper, we investigate the real-world use of such an agent to understand students deeply and help student team formation in a large university class involving about 200 students and 40 teams. Specifically, the agent interacted with each student in a text-based conversation at the beginning and end of the class. We show how the intelligent agent was able to elicit in-depth information from the students, infer the students' personality traits, and reveal the complex relationships between team personality compositions and team results. We also report on the students' behavior with and impression of the agent. We discuss the benefits and limitations of such an intelligent agent in helping team formation, and the design considerations for creating intelligent agents for aiding in teaming efforts.

Original languageEnglish (US)
Pages437-447
Number of pages11
DOIs
StatePublished - 2019
Event24th ACM International Conference on Intelligent User Interfaces, IUI 2019 - Marina del Ray, United States
Duration: Mar 17 2019Mar 20 2019

Conference

Conference24th ACM International Conference on Intelligent User Interfaces, IUI 2019
CountryUnited States
CityMarina del Ray
Period3/17/193/20/19

Keywords

  • Chatbot
  • Conversational Agent
  • Personality Inference
  • Team Companion
  • Team Formation
  • Teaming

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction

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  • Cite this

    Xiao, Z., Zhou, M. X., & Fu, W. T. (2019). Who should be my teammates: Using a conversational agent to understand individuals and help teaming. 437-447. Paper presented at 24th ACM International Conference on Intelligent User Interfaces, IUI 2019, Marina del Ray, United States. https://doi.org/10.1145/3301275.3302264