Interactive Artificial Intelligence-based System for Depression

the presented work offers an innovative, reproducible, and explainable approach to the use of reinforcement learning in the context of student mental health. Realistic case simulation and the agent's ability to learn optimal policies based on previous experiences represent a step towards the creation of technological tools that can complement traditional clinical strategies, especially in settings where human resources are limited. The integration of AI in this type of application does not seek to replace the role of health professionals but to offer additional support that allows intervention in an earlier, more personalized, and efficient way. This contributes to the consolidation of interdisciplinary approaches, where technology becomes an ally in improving students’ emotional and cognitive well-being.