Evaluating and Informing the Design of Chatbots
By Mohit Jain, Pratyush Kumar, Ramachandra Kota, Shwetak N. Patel. Conference on Designing Interactive Systems, 2018
352 citations recorded by the source corpus.
Text messaging-based conversational agents (CAs), popularly called chatbots, received significant attention in the last two years. However, chatbots are still in their nascent stage: They have a low penetration rate as 84% of the Internet users have not used a chatbot yet. Hence, understanding the usage patterns of first-time users can potentially inform and guide the design of future chatbots. In this paper, we report the findings of a study with 16 first-time chatbot users interacting with eight chatbots over multiple sessions on the Facebook Messenger platform. Analysis of chat logs and user interviews revealed that users preferred chatbots that provided either a 'human-like' natural language conversation ability, or an engaging experience that exploited the benefits of the familiar turn-based messaging interface. We conclude with implications to evolve the design of chatbots, such as: clarify chatbot capabilities, sustain conversation context, handle dialog failures, and end conversations gracefully.
Subfields: Artificial Intelligence, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/2551991cd5194ea6f580816336c8e5e1e3ae809e
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Citations in the corpus
- BigBlueBot: teaching strategies for successful human-agent interactions (2019, background): “” (P74, F, Shopping) This capability is similar to the ‘diagnostic’ strategy adopted by many chatbots, wherein the chatbot explicitly states its capabilities upfront or when queried [15, 40].”
- How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Design (2019, methodology): “Therefore, conversational Intelligence refers to the ability of a chatbot to effectively converse beyond the technical capability of achieving a conversational goal [Jain et al. 2018b].”