Revisiting habitability in conversational systems

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Conversational systems are inherently disadvantaged when indicating either what capabilities they have or the state they are in. The notion of habitability, the appropriate balancing in design between the language people use and the language a system can accept, emerged out of these early difficulties with conversational systems. This literature review aims to summarize progress in habitability research and explore implications for the design of current AI-enabled conversational systems. We found that i) the definitions of habitability focus mostly on matching between user expectations and system capabilities by employing well-balanced restrictions on language use; ii) there are two comprehensive design perspectives on different domains of habitability; iii) there is one standardized questionnaire with a sub-scale to measure habitability in a limited way. The review has allowed us to propose a working definition of habitability and some design implications that may prove useful for guiding future research and practice in this field.
Original languageEnglish
Title of host publicationCHI EA '20
Subtitle of host publicationExtended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages1-8
Number of pages8
ISBN (Electronic)9781450368193
DOIs
Publication statusPublished - Apr 2020
Event2020 CHI Conference on Human Factors in Computing Systems - Honolulu, United States
Duration: 25 Apr 202030 Apr 2020

Conference

Conference2020 CHI Conference on Human Factors in Computing Systems
CountryUnited States
CityHonolulu
Period25/04/2030/04/20

Keywords

  • Conversational interfaces
  • conversational agents
  • chatbots
  • habitability
  • evaluation
  • design

Cite this

Kocaballi, A. B., Coiera, E., & Berkovsky, S. (2020). Revisiting habitability in conversational systems. In CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-8). [LBW011] New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3334480.3383014