Stanford CS547 HCI Seminar | Spring 2026 | Promoting Agency in Human-AI Interaction
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.
Key takeaways
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Shift from Assistant to Adviser
2:00
LLM usage is shifting toward deeply personal advice (e.g., health, relationships), requiring a design paradigm that augments the user rather than automating tasks. This necessitates non-prescriptive support.
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The Role of Qualitative Context
4:00
Effective coaching relies on eliciting qualitative context (goals, values, motivations) over quantitative data (step count, heart rate). This aligns with principles from Motivational Interviewing.
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GPT coach Implementation
8:10
The GPT coach chatbot uses three prompt chains—Dialogue State Chain, Motivational Interviewing Chain, and Tool Use Chain—to integrate qualitative coaching strategies with quantitative data from the Apple Health Kit API.
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Algorithmic Solution: Bayesian Adaptive Planning
17:55
To improve strategic decision-making, the proposed algorithm uses Reinforcement Learning (RL) theory to model latent uncertainty ($ heta$) over the user's state. The agent must learn to strategically trade off asking informative questions against acting on current knowledge.