research-article

Examining AI Methods for Micro-Coaching Dialogs

Authors:

Elliot Mitchell,

Noemie Elhadad,

Lena MamykinaAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Article No.: 440, Pages 1 - 24

https://doi.org/10.1145/3491102.3501886

Published: 29 April 2022 Publication History

Abstract

Conversational interaction, for example through chatbots, is well-suited to enable automated health coaching tools to support self-management and prevention of chronic diseases. However, chatbots in health are predominantly scripted or rule-based, which can result in a stagnant and repetitive user experience in contrast with more dynamic, data-driven chatbots in other domains. Consequently, little is known about the tradeoffs of pursuing data-driven approaches for health chatbots. We examined multiple artificial intelligence (AI) approaches to enable micro-coaching dialogs in nutrition — brief coaching conversations related to specific meals, to support achievement of nutrition goals — and compared, reinforcement learning (RL), rule-based, and scripted approaches for dialog management. While the data-driven RL chatbot succeeded in shorter, more efficient dialogs, surprisingly the simplest, scripted chatbot was rated as higher quality, despite not fulfilling its task as consistently. These results highlight tensions between scripted and more complex, data-driven approaches for chatbots in health.

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Examining AI Methods for Micro-Coaching Dialogs

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CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

10459 pages

ISBN:9781450391573

DOI:10.1145/3491102

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Simone Barbosa
PUC-Rio, Brazil
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Cliff Lampe
University of Michigan, USA
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Caroline Appert
Université Paris-Saclay, France
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David A. Shamma
Toyota Research Institute, USA
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Steven Drucker
Microsoft Research, USA
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Julie Williamson
University of Glasgow, UK
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Koji Yatani
University of Tokyo, Japan

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Arakawa RYakura H(2024)Coaching Copilot: Blended Form of an LLM-Powered Chatbot and a Human Coach to Effectively Support Self-Reflection for Leadership GrowthProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665549(1-14)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665549
Plotkina LSri Ramalu S(2024)Unearthing AI coaching chatbots capabilities for professional coaching: a systematic literature reviewJournal of Management Development10.1108/JMD-06-2024-018243:6(833-848)Online publication date: 26-Sep-2024
https://doi.org/10.1108/JMD-06-2024-0182
Kirk Dvan Eijnatten ECamps G(2023)Comparison of Answers between ChatGPT and Human Dieticians to Common Nutrition QuestionsJournal of Nutrition and Metabolism10.1155/2023/55486842023(1-9)Online publication date: 7-Nov-2023
https://doi.org/10.1155/2023/5548684
Huang ERees Lewis DGaudani SEasterday MGerber E(2023)Intelligent Coaching Systems: Understanding One-to-many Coaching for Ill-defined Problem SolvingProceedings of the ACM on Human-Computer Interaction10.1145/35796147:CSCW1(1-24)Online publication date: 16-Apr-2023
https://dl.acm.org/doi/10.1145/3579614
Stawarz KKatz DAyobi AMarshall PYamagata TSantos-Rodriguez RFlach PO’Kane A(2023)Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-makingInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103003173:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103003

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