Designing Effective Interview Chatbots: Automatic Chatbot Profiling and Design Suggestion Generation for Chatbot Debugging
Xu Han, Michelle Zhou, Matthew Turner, Tom Yeh

TL;DR
This paper introduces iChatProfile, a tool that helps designers create more effective interview chatbots by providing performance metrics and design suggestions, validated through a user study.
Contribution
The paper presents a novel computational framework and an assistive tool for designing, evaluating, and improving interview chatbots iteratively.
Findings
iChatProfile significantly improved chatbot effectiveness
Enhanced interview quality and user experience
Validated through a comparative user study
Abstract
Recent studies show the effectiveness of interview chatbots for information elicitation. However, designing an effective interview chatbot is non-trivial. Few tools exist to help designers design, evaluate, and improve an interview chatbot iteratively. Based on a formative study and literature reviews, we propose a computational framework for quantifying the performance of interview chatbots. Incorporating the framework, we have developed iChatProfile, an assistive chatbot design tool that can automatically generate a profile of an interview chatbot with quantified performance metrics and offer design suggestions for improving the chatbot based on such metrics. To validate the effectiveness of iChatProfile, we designed and conducted a between-subject study that compared the performance of 10 interview chatbots designed with or without using iChatProfile. Based on the live chats between…
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