# LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devices arXiv: https://arxiv.org/abs/2403.10779 ยท HTML: https://mlsanity.com/paper/2403.10779/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Builds on 14 of its 114 references that are in the map. - First posted on arXiv 16 Mar 2024. ## Builds on (most cited first, 14 of 14) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022) - [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023) - [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023) - [LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks](https://mlsanity.com/paper/2402.01817/) (2024) - [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017) - [Question Decomposition Improves the Faithfulness of Model-Generated Reasoning](https://mlsanity.com/paper/2307.11768/) (2023) - [Penetrative AI: Making LLMs Comprehend the Physical World](https://mlsanity.com/paper/2310.09605/) (2023) - [The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems](https://mlsanity.com/paper/1806.09514/) (2018) - [LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning](https://mlsanity.com/paper/2401.00125/) (2023) - [Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China](https://mlsanity.com/paper/2209.10183/) (2022) - [Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward Reasoning in Math Word Problems](https://mlsanity.com/paper/2310.01991/) (2023) ## Built on by (most cited first, 21 of 21) - [DrHouse: An LLM-empowered Diagnostic Reasoning System through Harnessing Outcomes from Sensor Data and Expert Knowledge](https://mlsanity.com/paper/2405.12541/) (2024) - [MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences](https://mlsanity.com/paper/2409.09570/) (2024) - [Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App](https://mlsanity.com/paper/2404.00487/) (2024) - [A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions](https://mlsanity.com/paper/2502.11095/) (2025) - [PsyGUARD: An Automated System for Suicide Detection and Risk Assessment in Psychological Counseling](https://mlsanity.com/paper/2409.20243/) (2024) - [Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recovery](https://mlsanity.com/paper/2412.11656/) (2024) - [MindGuard: Towards Accessible and Sitgma-free Mental Health First Aid via Edge LLM](https://mlsanity.com/paper/2409.10064/) (2024) - [Empathy Through Multimodality in Conversational Interfaces](https://mlsanity.com/paper/2405.04777/) (2024) - [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025) - [Examining Spanish Counseling with MIDAS: a Motivational Interviewing Dataset in Spanish](https://mlsanity.com/paper/2502.08458/) (2025) - [Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models](https://mlsanity.com/paper/2504.08399/) (2025) - [Seeking Late Night Life Lines: Experiences of Conversational AI Use in Mental Health Crisis](https://mlsanity.com/paper/2512.23859/) (2025) - [Revisiting Disaggregated Large Language Model Serving for Performance and Energy Implications](https://mlsanity.com/paper/2601.08833/) (2025) - [TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health](https://mlsanity.com/paper/2605.21295/) (2026) - [Metareasoning constraints couple narratives, affect and cognition](https://mlsanity.com/paper/2502.09487/) (2025) - [Wearable Meets LLM for Stress Management: A Duoethnographic Study Integrating Wearable-Triggered Stressors and LLM Chatbots for Personalized Interventions](https://mlsanity.com/paper/2502.17650/) (2025) - [AI Safety Training Can be Clinically Harmful](https://mlsanity.com/paper/2604.23445/) (2026) - [When and How to Integrate Multimodal Large Language Models in College Psychotherapy: Perspectives from Multi-stakeholders](https://mlsanity.com/paper/2502.00229/) (2025) - [Are Today's LLMs Ready to Explain Well-Being Concepts?](https://mlsanity.com/paper/2508.03990/) (2025) - [Tell Me: An LLM-powered Mental Well-being Assistant with RAG, Synthetic Dialogue Generation, and Agentic Planning](https://mlsanity.com/paper/2511.14445/) (2025) - [AI Content Moderation in Therapy Conversations](https://mlsanity.com/paper/2605.25454/) (2026) ## Abstract (arXiv) 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.