PsychSync: An AI-Powered Mental Health Self-Assessment and Monitoring Portal

Muhammad Hafis, Bindu B

Abstract: Mental health evaluation has traditionally relied on static, standardized questionnaires that fail to capture the nuanced emotional context of a patient. This paper introduces PsychSync, an AI-powered, web- based portal designed to facilitate mental health self-assessment, emotional tracking, and personalized resource recommendation. By integrating clinically validated tools (such as the PHQ-9 and GAD-7) with Generative Artificial Intelligence (Google Gemini API), the system provides empathetic, context-aware feedback based on severity scores. Furthermore, the platform utilizes Natural Language Processing (NLP) to extract mindset tags and environmental triggers from unstructured digital journal entries. These tags power a Machine Learning (ML) recommendation engine utilizing Cosine Similarity to match users with curated psychological resources. The proposed system effectively bridges the gap between static clinical evaluation and proactive, personalized mental health management while maintaining strict data privacy through an anonymous tracking mode.

Keywords: Mental Health Application, Generative AI, Natural Language Processing, Machine Learning, Self-Assessment, Recommender Systems

How to Cite?: Muhammad Hafis, Bindu B, "PsychSync: An AI-Powered Mental Health Self-Assessment and Monitoring Portal", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 1445-1448, https://www.ijsr.net/getabstract.php?paperid=SR26422163513, DOI: https://dx.doi.org/10.21275/SR26422163513

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