International Research Journal of Education and Technology

Disorders of mental health, such as stress, anxiety, and depression, are rising exponentially due to a drastic shift in lifestyle habits. Early intervention is currently a major issue in the identification of these problems since the most conventional method used in the field of mental healthisself-assessmentofone'semotionalstate.Therefore,toovercomesuchaproblem, an"AI- Driven Multimodal Mental Health Risk Prediction & Personalized Intervention System" is introduced in this research. The proposed system relies on multimodal information, such as text data (user chats/diaries) inputs, speech signals, and facial expressiveness, to encode an all- encompassing view of the mental state of an individual. State-of-the-art Natural Language Processing tools are used to extract features related to emotions as well as semantics from text, whereas audio and visual models, built using Deep Learning, are used for the detection of stress signs conveyed through speech as well as facial expressions. Depending on the risk category anticipated, it recommends interventions like stress management skills, mindfulness, and professional advice. In addition to this, the incorporation of Explainable AI enhances user understanding and trust. This happens through the explanations it provides on the predictions made. The experimental outcome shows that the proposed system.

KEYWORDS

ArtificialIntelligence,Mental HealthPrediction,MultimodalLearning,EmotionRecognition, Natural Language Processing, Deep Learning.