The rise of lifestyle-related, non-communicable diseases such as Type II diabetes, cardiovascular diseases, and depression has prompted the development of various behavior change technologies to promote sustained healthy behaviors. User adherence, however, has remained low. The Collaborative Hybrid Intelligence Platform CHIP is introduced to address adherence challenges by placing the user perspective at the center and facilitating dialogue-based interactions between users and their technical and non-technical support systems—including AI systems, clinicians and caretakers. These interactions aim to uncover barriers to adherence and collaboratively shape personalized lifestyle plans that align with a person’s preferences, values, and context. CHIP is a microservice-based research platform written in Python with modules implemented as Docker containers. Its modularity allows researchers to replace or adapt specific components, such as natural language reasoners, for technical evaluation and domain-specific adaptation.

Behavior change support systems need to take into account individual needs and preferences to provide appropriate support. In this demonstration, we illustrate how this might be achieved through the explicit modeling of user characteristics within knowledge graphs (KG), captured in a dialogue between the system and the user. We demonstrate how up-to-date information enables reasoning for providing personalized support.