Design and Challenges of Mental Health Assessment Tools Based on Natural Language Interaction

TL;DR

This paper explores the design and challenges of using conversational AI and large language models for non-invasive mental health assessments, highlighting potential benefits and key obstacles such as privacy and bias.

Contribution

It presents an interactive prototype system and provides an empirical analysis of design considerations and challenges in developing AI-based mental health assessment tools.

Findings

AI-driven adaptive questioning may improve data reliability

Privacy, bias, and cultural issues are critical challenges

Empirical insights from mental health professionals' feedback

Abstract

Mental health assessments are of central importance to individuals' well-being. Conventional assessment methodologies predominantly depend on clinical interviews and standardised self-report questionnaires. Nevertheless, the efficacy of these methodologies is frequently impeded by factors such as subjectivity, recall bias, and accessibility issues. Furthermore, concerns regarding bias and privacy may result in misreporting in data collected through self-reporting in mental health research. The present study examined the design opportunities and challenges inherent in the development of a mental health assessment tool based on natural language interaction with large language models (LLMs). An interactive prototype system was developed using conversational AI for non-invasive mental health assessment, and was evaluated through semi-structured interviews with 11 mental health professionals (six counsellors and five psychiatrists). The analysis identified key design considerations for future development, highlighting how AI-driven adaptive questioning could potentially enhance the reliability of self-reported data while identifying critical challenges, including privacy protection, algorithmic bias, and cross-cultural applicability. This study provides an empirical foundation for mental health technology innovation by demonstrating the potential and limitations of natural language interaction in mental health assessment.