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A scoping review of generative AI chatbots for motivational interviewing reveals they provide MI-consistent interactions with favorable user perceptions, but evidence for sustained behavioral change is limited.
This paper introduces Preference Tree Optimization (PTO), a framework that generates preference data via look-ahead simulations to iteratively improve goal-oriented dialogue agents, with experiments showing gains in Motivational Interviewing settings.
This paper studies how preference optimization shapes LLM counselors' behavior in motivational interviewing, finding that penalizing confrontation trades goal persistence for relational attunement rather than teaching the balanced skill of rolling with resistance.
MIThinker proposes a lightweight reasoning model for motivational interviewing counseling agents, trained via supervised fine-tuning and reinforcement learning to generate therapeutic thoughts, achieving MI competency comparable to state-of-the-art systems with lower computation.