University of Illinois Urbana-Champaign

Gender identity and influence in human-machine communication: A mixed-methods research program

Liu, Weizi

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https://hdl.handle.net/2142/125696

Description

Title
Gender identity and influence in human-machine communication: A mixed-methods research program
Author(s)
Liu, Weizi
Issue Date
2024-07-08
Director of Research (if dissertation) or Advisor (if thesis)
Yao, Mike Z
Doctoral Committee Chair(s)
Yao, Mike Z
Committee Member(s)
  • Huang, Yun
  • Maslowska, Ewa H
  • Xu, Kun
Department of Study
Informatics
Discipline
Informatics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Human-Computer Interaction
  • human-machine communication
  • gender
  • social identities
  • mixed-methods
Abstract
The advancement of conversational technologies stimulates new research agendas on the patterns, norms, and social impacts of human-machine communication (HMC) as a novel process. Conversational agents (CAs), a prevalent example of machines that communicate with users directly, are usually depicted as females in assisting roles. I propose a research program to explore empirical evidence of how “gendered” technologies might influence HMC and potentially reinforce gender stereotyping in interpersonal communication. In Studies 1 and 2 as preliminary studies, I applied a mixed-methods approach to explore users’ language use and evaluations toward gendered CAs to understand the issue comprehensively. First, I observed unrestricted interactions between 36 human participants and Amazon Alexa in a laboratory and qualitatively analyzed the transcripts to detect gendered communication cues. I then conducted an online experiment where 250 participants interacted with a “gendered” chatbot. Results revealed that participants' responses varied significantly across different gender pairings of humans and CAs, including emotions and tones, level of engagement, (non)accommodation behaviors, and evaluations of the CAs' credibility, attractiveness, and likeability. Informed by the preliminary studies, I conducted two more experiments using the protocol established in Study 2. In Studies 3 and 4, I further explored the patterns of HMC in user evaluations and language use from expectancy violations and social scripts perspectives. I designed updated gendered CAs with more vivid virtual human images that were powered by the GPT models and analyzed user evaluations and responses of these CAs. However, the patterns discovered in the first two studies were hardly replicated. Based on the findings, I discuss 1) the importance of integrating interpersonal communication theories and methods into HMC research, 2) the potential shift in the nature of HMC with generative AI; and 3) design implications for humanlike CAs with social identities.
Graduation Semester
2024-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/125696
Copyright and License Information
Copyright 2024 Weizi Liu

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