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Examining AI Methods for Micro-Coaching Dialogs.

Elliot G Mitchell1,2, Noémie Elhadad1, Lena Mamykina1

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Summary
This summary is machine-generated.

Data-driven health chatbots using reinforcement learning (RL) offered efficient nutrition micro-coaching. However, simpler scripted chatbots were surprisingly rated higher quality by users, highlighting tradeoffs in AI health coaching.

Keywords:
Health coachingchatbotsconversational agentsreinforcement learningself-management

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence in Healthcare
  • Digital Health Coaching

Background:

  • Conversational AI, like chatbots, can support chronic disease self-management and prevention.
  • Current health chatbots are often scripted or rule-based, leading to repetitive user experiences.
  • The trade-offs of data-driven approaches for health chatbots remain largely unexplored.

Purpose of the Study:

  • To investigate artificial intelligence (AI) approaches for automated nutrition micro-coaching dialogs.
  • To compare reinforcement learning (RL), rule-based, and scripted dialog management strategies.
  • To understand user experience trade-offs between scripted and data-driven health chatbots.

Main Methods:

  • Examined AI approaches including reinforcement learning (RL), rule-based, and scripted methods.
  • Focused on micro-coaching dialogs for nutrition goal achievement, centered on specific meals.
  • Compared the efficiency and user-rated quality of different chatbot dialog management systems.

Main Results:

  • The data-driven RL chatbot achieved shorter and more efficient dialogs.
  • Surprisingly, the simplest scripted chatbot received higher quality ratings from users.
  • The scripted chatbot's task completion was less consistent than the RL chatbot.

Conclusions:

  • Significant tensions exist between scripted and complex data-driven approaches in health chatbots.
  • User-perceived quality in health coaching may not solely depend on dialog efficiency or task consistency.
  • Further research is needed to optimize AI for engaging and effective health behavior support.