When to AI, and When Not to AI with Eric Hekler

"People are different. Context matters. Things change."

In this episode of the Behavioral Design Podcast, Aline is joined by Eric Hekler, professor at UC San Diego, to explore the nuances of AI in behavioral science and health interventions. Eric’s mantra—emphasizing the importance of individual differences, context, and change—serves as a foundation for the conversation as they discuss when AI enhances behavioral interventions and when human judgment is indispensable.

The discussion explores just-in-time adaptive interventions (JITAI), the efficiency trap of AI, and the jagged frontier of AI adoption—where machine learning excels and where it falls short. Eric shares his expertise on control systems engineering, human-AI collaboration, and the real-world challenges of scaling adaptive health interventions. The episode also explores teachable moments, the importance of domain knowledge, and the need for AI to support rather than replace human decision-making.

The conversation wraps up with a quickfire round, where Eric debates AI’s role in health coaching, mental health interventions, and optimizing human routines.

LINKS:

Eric Hekler:

TIMESTAMPS:
02:01 Introduction and Correction
05:21 The Efficiency Trap of AI
08:02 Human-AI Collaboration
11:04 Conversation with Eric Hekler
14:12 Just-in-Time Adaptive Interventions
15:19 System Identification Experiment
28:27 Control Systems vs. Machine Learning
39:44 Challenges with Classical Machine Learning
43:16 Translating Research to Real-World Applications
49:49 Community-Based Research and Context Matters
59:46 Quickfire Round: To AI or Not to AI
01:08:27 Final Thoughts on AI and Human Evolution

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When to AI, and When Not to AI with Eric Hekler

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