TY - GEN
T1 - How to Guide Task-oriented Chatbot Users, and When
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
AU - Yeh, Su Fang
AU - Wu, Meng Hsin
AU - Chen, Tze Yu
AU - Lin, Yen Chun
AU - Chang, Xi Jing
AU - Chiang, You Hsuan
AU - Chang, Yung Ju
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/4/29
Y1 - 2022/4/29
N2 - The popularity of task-oriented chatbots is constantly growing, but smooth conversational progress with them remains profoundly challenging. In recent years, researchers have argued that chatbot systems should include guidance for users on how to converse with them. Nevertheless, empirical evidence about what to place in such guidance, and when to deliver it, has been lacking. Using a mixed-methods approach that integrates results from a between-subjects experiment and a reflection session, this paper compares the effectiveness of eight combinations of two guidance types (example-based and rule-based) at four guidance timings (service-onboarding, task-intro, after-failure, and upon-request), as measured by users' task performance, improvement on subsequent tasks, and subjective experience. It establishes that each guidance type and timing has particular strengths and weaknesses, thus that each type/timing combination has a unique impact on performance metrics, learning outcomes, and user experience. On that basis, it presents guidance-design recommendations for future task-oriented chatbots.
AB - The popularity of task-oriented chatbots is constantly growing, but smooth conversational progress with them remains profoundly challenging. In recent years, researchers have argued that chatbot systems should include guidance for users on how to converse with them. Nevertheless, empirical evidence about what to place in such guidance, and when to deliver it, has been lacking. Using a mixed-methods approach that integrates results from a between-subjects experiment and a reflection session, this paper compares the effectiveness of eight combinations of two guidance types (example-based and rule-based) at four guidance timings (service-onboarding, task-intro, after-failure, and upon-request), as measured by users' task performance, improvement on subsequent tasks, and subjective experience. It establishes that each guidance type and timing has particular strengths and weaknesses, thus that each type/timing combination has a unique impact on performance metrics, learning outcomes, and user experience. On that basis, it presents guidance-design recommendations for future task-oriented chatbots.
KW - chatbot
KW - guidance
KW - lab study
KW - non-progress
UR - http://www.scopus.com/inward/record.url?scp=85130555956&partnerID=8YFLogxK
U2 - 10.1145/3491102.3501941
DO - 10.1145/3491102.3501941
M3 - Conference contribution
AN - SCOPUS:85130555956
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 30 April 2022 through 5 May 2022
ER -