Stanford CS547 HCI Seminar | Spring 2026 | Promoting Agency in Human-AI Interaction

Summary

This seminar explores designing AI systems that promote user agency when LLMs are used as personal advisers (coaches/counselors), rather than mere assistants. The core thesis is that successful health behavior change requires systems to elicit qualitative context and navigate uncertainty to provide non-prescriptive support. Practical implementations, such as the GPT coach chatbot and the Bloom iOS application, demonstrate how integrating motivational interviewing strategies with wearable data can improve user mindset and sense of control. Algorithmically, the work proposes 'zero-shot Bayesian Adaptive Planning' using LLMs to strategically balance asking informative questions versus acting on known information by modeling latent uncertainty over the user's state.

Technical details

• GPT coach System Architecture 490s

  The system is an LLM-based physical activity coaching chatbot that implements a validated coaching program. It uses three specialized prompt chains: 1) Dialogue State Chain (maintains context over long conversations); 2) Motivational Interviewing Chain (guides communication using established counseling strategies); and 3) Tool Use Chain (evaluates whether to augment responses with wearable sensor data).

• Bloom Application Integration 650s

  Bloom integrates the GPT coach agent into an iOS application, combining LLM-elicited qualitative context with existing behavior change interactions (e.g., collaborative goal setting). This allows for personalized downstream interventions.

• Zero-Shot Bayesian Adaptive Planning 900s

  This advanced RL technique models the conversation history as a state and optimizes over a family of MDPs, each indexed by a latent variable $ heta$ (user state). The process involves three steps: 1) Eliciting beliefs (posterior distribution over $ heta$) using LLMs; 2) Using these beliefs to rank high-level strategies (e.g., ask question vs. make recommendation); and 3) Generating the final utterance based on both belief and ranking.

Mentioned resources

• Apple Health Kit API (API/Integration)
• Stanford HCI Seminar Schedule (Website)

Channel & topics

• Stanford Online
• Human-AI Interaction
• Behavior Change Theory
• Large Language Models (LLMs)
• Reinforcement Learning (RL)
• Bayesian Inference
• Health Technology
• Apple Health Kit API
• Stanford HCI Seminar Schedule