Designing Theory-Driven GenAI Health Coach Chatbots: A Tutorial in Conceptualization, Development, and Evaluation (Preprint)

Authors:
Preprint
Publisher website
Google Scholar
Abstract
UNSTRUCTURED Abstract Background: Generative artificial intelligence (GenAI) chatbots are increasingly being developed to deliver personalized, scalable, and interactive support for health behavior change. Although these technologies have considerable potential, there is little practical guidance for researchers and clinicians on how to design GenAI health coach chatbots that can function as health behavior change coaches. Purpose: This tutorial presents a practical framework for developing theory-driven GenAI health coach chatbots using widely available large language models (LLMs) without requiring programming expertise. Drawing on behavioral theory, ethics, digital intervention development, and recent advances in LLMs, the tutorial describes how to develop a chatbot that is grounded in behavior change theory, informed by evidence, and developed with attention to safety, privacy, and equity. The six-step framework presented here spans conceptualization, instruction writing, knowledge base curation, LLM integration, pre-release testing, and post-release monitoring. Tutorial Framework: The tutorial provides a historical overview of the development of health coach chatbots. It then conceptualizes how theory and ethics can inform chatbot development. Finally, it introduces a six-step framework for health coach chatbot development that guides developers through (1) conceptualizing the chatbot, (2) developing chatbot instructions with attention to theory, behavior change techniques, and ethical considerations, (3) identifying materials for the knowledge base, (4) integrating the instructions and knowledge base into an LLM, (5) conducting pre-release validation and safety testing, and (6) releasing the chatbot and implementing post-release monitoring and refinement. Practical recommendations are provided throughout, including approaches to prompt engineering, use of retrieval-augmented generation, theory-based personalization, red-teaming, and evaluation of chatbot fidelity and safety. Conclusions: By integrating behavioral theory and ethics with contemporary GenAI development practices, this tutorial provides researchers, clinicians, and digital health developers with a structured, replicable framework for building and evaluating GenAI health coach chatbots. As AI-assisted behavioral interventions become more widespread, theory-driven design and rigorous validation will be essential for developing chatbots that are effective, safe, equitable, and suitable for research and practice.

This publication has 54 references indexed in Scilit: