Developing effective healthcare dialog systems requires controlling conversations to offer clear insight into the system’s understanding and to address the lack of patient-oriented conversational datasets. Moreover, evaluating these systems is equally challenging and requires user studies for robust evaluation. These challenges are even more pronounced when addressing the needs of minority populations with low health literacy and numeracy. This thesis proposal focuses on designing conversational architectures that deliver self-care information to African American patients with heart failure.

Neuro-symbolic approaches provide a promising direction by integrating symbolic reasoning with the generative capabilities of Large Language Models (LLMs). In this proposal, we explore various approaches to creating a hybrid dialog model by combining the strengths of task-oriented dialog systems with the integration of neuro-symbolic rules into a Language Model (LM)/LLM-based dialog system, thereby controlling the dialog system. We propose a hybrid conversational system that uses schema graphs to control the flow of dialogue, while leveraging LLMs to generate responses grounded in these schemas. We will also conduct a user study to evaluate the system's effectiveness.