BigBlueBot: teaching strategies for successful human-agent interactions
By Justin D. Weisz, Mohit Jain, N. Joshi, James M. Johnson, Ingrid Lange. 2019
43 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Safety Research, Social Psychology
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Citations in the corpus
- In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes (2019, methodology): “Crowdsourcing, while popular for collecting ground truth labels used to train AI systems, has also been used as a means of sampling opinions on new technologies [39] or of gaining preliminary feedback on demonstrations of new technology [41].”