arXiv:2605.27393v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent dialogue, but prior works lack situational grounding, dynamic strategy control, and evaluation aligned with clinical standards in motivational interviewing (MI). We introduce StoryMI, a multi-LLM agent framework for controllable MI dialogue generation, where questionnaire-based client profiles are expanded into situational stories that provide narrative context for the dialogue. Therapist and client agents generate MI-coded utterances guided by MI codes selected by the interaction agent, while an
The rapid advancement of large language models (LLMs) is enabling increasingly sophisticated applications in specialized domains like therapeutic dialogue, previously considered too nuanced for AI.
This development indicates a significant step towards autonomous AI systems capable of handling complex, sensitive human interactions, potentially transforming professional services and mental health support.
AI is moving beyond simple conversational agents to multi-agent frameworks that can dynamically adapt strategies and ground dialogues in specific situational contexts, mirroring human therapeutic approaches.
- · Mental health tech companies
- · Healthcare providers
- · AI platform developers
- · Patients seeking accessible therapy
- · Traditional therapy models reliant solely on human interaction
- · Companies offering rudimentary chatbot solutions
Therapeutic dialogues become more scalable and accessible through AI-driven platforms.
The integration of AI agents could lead to new models of care, blending human oversight with AI delivery, affecting workforce structures in healthcare.
Ethical and regulatory frameworks will need to rapidly evolve to govern autonomous therapeutic AI, addressing issues of responsibility, bias, and patient safety.
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Read at arXiv cs.AI