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LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devices

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arxiv 2403.10779 v1 pith:TVXKEZYW submitted 2024-03-16 cs.CL

LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devices

classification cs.CL
keywords caitipsychotherapeuticconversationconversationalfunctioninginterventionsmentalpsychotherapists
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Despite the global mental health crisis, access to screenings, professionals, and treatments remains high. In collaboration with licensed psychotherapists, we propose a Conversational AI Therapist with psychotherapeutic Interventions (CaiTI), a platform that leverages large language models (LLM)s and smart devices to enable better mental health self-care. CaiTI can screen the day-to-day functioning using natural and psychotherapeutic conversations. CaiTI leverages reinforcement learning to provide personalized conversation flow. CaiTI can accurately understand and interpret user responses. When the user needs further attention during the conversation, CaiTI can provide conversational psychotherapeutic interventions, including cognitive behavioral therapy (CBT) and motivational interviewing (MI). Leveraging the datasets prepared by the licensed psychotherapists, we experiment and microbenchmark various LLMs' performance in tasks along CaiTI's conversation flow and discuss their strengths and weaknesses. With the psychotherapists, we implement CaiTI and conduct 14-day and 24-week studies. The study results, validated by therapists, demonstrate that CaiTI can converse with users naturally, accurately understand and interpret user responses, and provide psychotherapeutic interventions appropriately and effectively. We showcase the potential of CaiTI LLMs to assist the mental therapy diagnosis and treatment and improve day-to-day functioning screening and precautionary psychotherapeutic intervention systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Survey of Large Language Models for Perception and Measurement of Human Psychology

    cs.CY 2026-05 unverdicted novelty 5.0

    A survey proposing a three-pillar framework to evaluate LLMs as tools for measuring latent psychological constructs and reviewing applications in personality and mental health.

  2. Tell Me: An LLM-powered Mental Well-being Assistant with RAG, Synthetic Dialogue Generation, and Agentic Planning

    cs.CL 2025-11 conditional novelty 4.0

    An open mental-well-being chatbot demo with retrieval-augmented responses, synthetic dialogue generation, and agentic self-care planning reports a small human study favoring its retrieval-augmented version.