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Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots

Rawan AlMakinah, Andrea Norcini-Pala, Lindsey Disney, M. Abdullah Canbaz

TL;DR

This approach aims to develop a secure, evidence-based AI chatbot capable of offering trustworthy and empathetic mental health support, thereby advancing AI's role in digital mental health care.

Abstract

Access to mental health support remains limited, particularly in marginalized communities where structural and cultural barriers hinder timely care. This paper explores the potential of AI-enabled chatbots as a scalable solution, focusing on advanced large language models (LLMs)-GPT v4, Mistral Large, and LLama V3.1-and assessing their ability to deliver empathetic, meaningful responses in mental health contexts. While these models show promise in generating structured responses, they fall short in replicating the emotional depth and adaptability of human therapists. Additionally, trustworthiness, bias, and privacy challenges persist due to unreliable datasets and limited collaboration with mental health professionals. To address these limitations, we propose a federated learning framework that ensures data privacy, reduces bias, and integrates continuous validation from clinicians to enhance response quality. This approach aims to develop a secure, evidence-based AI chatbot capable of offering trustworthy, empathetic, and bias-reduced mental health support, advancing AI's role in digital mental health care.

Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots

TL;DR

This approach aims to develop a secure, evidence-based AI chatbot capable of offering trustworthy and empathetic mental health support, thereby advancing AI's role in digital mental health care.

Abstract

Access to mental health support remains limited, particularly in marginalized communities where structural and cultural barriers hinder timely care. This paper explores the potential of AI-enabled chatbots as a scalable solution, focusing on advanced large language models (LLMs)-GPT v4, Mistral Large, and LLama V3.1-and assessing their ability to deliver empathetic, meaningful responses in mental health contexts. While these models show promise in generating structured responses, they fall short in replicating the emotional depth and adaptability of human therapists. Additionally, trustworthiness, bias, and privacy challenges persist due to unreliable datasets and limited collaboration with mental health professionals. To address these limitations, we propose a federated learning framework that ensures data privacy, reduces bias, and integrates continuous validation from clinicians to enhance response quality. This approach aims to develop a secure, evidence-based AI chatbot capable of offering trustworthy, empathetic, and bias-reduced mental health support, advancing AI's role in digital mental health care.
Paper Structure (14 sections, 1 equation, 7 figures)

This paper contains 14 sections, 1 equation, 7 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Knowledge/Comprehension Measurement
  4. Question Set Selection
  5. Analysis of Response Generation and Comparison for Top 10 Questions
  6. Preliminary Qualitative Analysis of Therapist vs LLM Generated Responses
  7. Quantitative Analysis of Therapist vs LLM Generated Responses
  8. Insights and Implications for Human-AI Interaction in Mental Health
  9. Feasibility Analysis of Needs for AI-Enabled First Aid Mental Support
  10. Federated Learning as a Potential Solution
  11. First Aid Mental Health
  12. Conclusion
  13. Future Work
  14. A Sample Prompt for A Patient Question, the Human Therapist's Response along with LLM's Outputs

Figures (7)

  • Figure 1: LLMs Prompt Example
  • Figure 2: Cosine Similarity Analysis of Therapists and LLMs Responses for 10 Questions
  • Figure 3: A Heatmap of Cosine Similarity Analysis of Therapist Compared to LLMs Responses(Mistral on Left, LLamav31 on Right)
  • Figure 4: Cosine Similarity Cross Comparison of LLama v3.1 vs Mistral Responses
  • Figure 5: A summary map of the proposed framework features
  • ...and 2 more figures