Heuristic Evaluation of Conversational Agents

要旨

Conversational interfaces have risen in popularity as businesses and users adopt a range of conversational agents, including chatbots and voice assistants. Although guidelines have been proposed, there is not yet an established set of usability heuristics to guide and evaluate conversational agent design. In this paper, we propose a set of heuristics for conversational agents adapted from Nielsen's heuristics and based on expert feedback. We then validate the heuristics through two rounds of evaluations conducted by participants on two conversational agents, one chatbot and one voice-based personal assistant. We find that, when using our heuristics to evaluate both interfaces, evaluators were able to identify more usability issues than when using Nielsen’s heuristics. We propose that our heuristics successfully identify issues related to dialogue content, interaction design, help and guidance, human-like characteristics, and data privacy.

受賞
Best Paper
著者
Raina Langevin
University of Washington, Seattle, Washington, United States
Ross J. Lordon
Microsoft, Redmond, Washington, United States
Thi Avrahami
Rulai, Mountain View, California, United States
Benjamin R.. Cowan
University College Dublin, Dublin, Ireland
Tad Hirsch
Northeastern University, Boston, Massachusetts, United States
Gary Hsieh
University of Washington, Seattle, Washington, United States
DOI

10.1145/3411764.3445312

論文URL

https://doi.org/10.1145/3411764.3445312

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Design for Public Spaces / VR Memorials / Textiles and Jewelry / Voice and Conversation / New Value Transactions

[A] Paper Room 05, 2021-05-12 17:00:00~2021-05-12 19:00:00 / [B] Paper Room 05, 2021-05-13 01:00:00~2021-05-13 03:00:00 / [C] Paper Room 05, 2021-05-13 09:00:00~2021-05-13 11:00:00
Paper Room 05
13 件の発表
2021-05-12 17:00:00
2021-05-12 19:00:00
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