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Recent studies show the effectiveness of interview chatbots for informationelicitation. However, designing an effective interview chatbot is non-trivial.Few tools exist to help designers design, evaluate, and improve an interviewchatbot iteratively. Based on a formative study and literature reviews, wepropose a computational framework for quantifying the performance of interviewchatbots. Incorporating the framework, we have developed iChatProfile, anassistive chatbot design tool that can automatically generate a profile of aninterview chatbot with quantified performance metrics and offer designsuggestions for improving the chatbot based on such metrics. To validate theeffectiveness of iChatProfile, we designed and conducted a between-subjectstudy that compared the performance of 10 interview chatbots designed with orwithout using iChatProfile. Based on the live chats between the 10 chatbots and1349 users, our results show that iChatProfile helped the designers buildsignificantly more effective interview chatbots, improving both interviewquality and user experience.

Designing Effective Interview Chatbots: Automatic Chatbot Profiling and Design Suggestion Generation for Chatbot Debugging