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

2025-10-20

AI Summary

Traditional mental health assessments suffer from subjectivity, recall bias, limited accessibility, and privacy concerns or response biases associated with self-reporting. To address these limitations, this study proposes a large language model (LLM)-based, natural language–driven interactive assessment paradigm that enables non-invasive, dynamic, and adaptive probing of psychological states via conversational AI. The system dynamically adjusts its questioning strategy in real time based on user responses, mitigating subjective bias and enhancing data reliability. Integrating clinical expertise, we design a technical framework emphasizing privacy preservation, algorithmic fairness, and cross-cultural applicability, and implement a functional prototype. Validation through interviews with 11 mental health professionals confirms feasibility and yields six core design principles for clinical deployment. This work provides both a methodological foundation and a practical implementation pathway for AI-augmented mental health assessment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: AI for Accessibility
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Developing mental health assessment tools using natural language interaction with LLMs
• Addressing subjectivity and recall bias in traditional self-report methodologies
• Overcoming privacy concerns and algorithmic bias in AI-driven assessments

Innovation

• Natural language interaction with LLMs for mental health assessment
• Conversational AI prototype for non-invasive evaluation
• AI-driven adaptive questioning to enhance data reliability