The increasing demand for mental health services has highlighted the need for innovative solutions, particularly in the realm of psychological conversational AI, where the availability of sensitive data is scarce. In this work, we explored the development of a system tailored for mental health support with a novel approach to psychological assessment based on explainable emotional profiles in combination with empathetic conversational models, offering a promising tool for augmenting traditional care, particularly where immediate expertise is unavailable. Our work can be divided into two main parts, intrinsecaly connected to each other. First, we present RACLETTE, a conversational system that demonstrates superior emotional accuracy compared to considered benchmarks in both understanding users’ emotional states and generating empathetic responses during conversations, while progressively building an emotional profile of the user through their interactions. Second, we show how the emotional profiles of a user can be used as interpretable markers for mental health assessment. These profiles can be compared with characteristic emotional patterns associated with different mental disorders, providing a novel approach to preliminary screening and support.

Large Language Models are increasingly used in the medical field, particularly in psychiatry where language plays a fundamental role in diagnosis. This study explores the use of open-source LLMs within the MIND framework. Specifically, we implemented a mixed-methods approach for the CLPsych 2025 shared task: (1) we used a combination of retrieval and few-shot learning approaches to highlight evidence of mental states within the text and to generate comprehensive summaries for post-level and timeline-level analysis, allowing for effective tracking of psychological state fluctuations over time (2) we developed different types of ensemble methods for well-being score prediction, combining Machine Learning and Optimization approaches on top of zero-shot LLMs predictions. Notably, for the latter task, our approach demonstrated the best performance within the competition.