BigBlueBot: Teaching strategies for successful human-agent interactions

Abstract

Chatbots are becoming quite popular, with many brands developing conversational experiences using platforms such as IBM's Watson Assistant and Facebook Messenger. However, previous research reveals that users' expectations of what conversational agents can understand and do far outpace their actual technical capabilities. Our work seeks to bridge the gap between these expectations and reality by designing a fun learning experience with several goals: explaining how chatbots work by mapping utterances to a set of intents, teaching strategies for avoiding conversational breakdowns, and increasing desire to use chatbots by creating feelings of empathy toward them. Our experience, called BigBlueBot, consists of interactions with two chatbots in which breakdowns occur and the user (or chatbot) must recover using one or more repair strategies. In a Mechanical Turk evaluation (N=88), participants learned strategies for having successful human-agent interactions, reported feelings of empathy toward the chatbots, and expressed a desire to interact with chatbots in the future.