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

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 the 10 chatbots and 1349 users, our results show that iChatProfile helped the designers build significantly more effective interview chatbots, improving both interview quality and user experience.