MindLift: AI-powered mental health assessment for students

MindLift: AI-powered mental health assessment for students

This study introduces MindLift, a student-specific AI-powered mental health assessment and intervention platform. The goal of this research is to create a real-time, multimodal system that can assess mental health through the use of behavioral pattern tracking, audio tone analysis, facial expression...

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Bibliographic Details
Main Authors: Shanky Goyal, RishiRaj Dutta, Saurabh Dev, Kola Narasimha Raju, Mohammed Wasim Bhatt
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Neuroscience Informatics
Subjects:
Artificial intelligence
Mental health assessment
Machine learning
Sentiment analysis
Facial expression recognition
Chatbot-based therapy
Online Access:http://www.sciencedirect.com/science/article/pii/S2772528625000238
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Summary:This study introduces MindLift, a student-specific AI-powered mental health assessment and intervention platform. The goal of this research is to create a real-time, multimodal system that can assess mental health through the use of behavioral pattern tracking, audio tone analysis, facial expression recognition, and text sentiment interpretation. By integrating convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based natural language processing (NLP) models, MindLift provides a comprehensive emotional analysis. Through evidence-based techniques like Cognitive Behavioral Therapy (CBT), an intelligent chatbot built into the system provides individualized mental health support. Responses and interventions are customized using important parameters like sentiment polarity, mood detection, and behavioral abnormalities. MindLift emphasizes ethical AI deployment, with strong safeguards for privacy, consent, and fairness. Preliminary studies show a notable increase in student engagement, emotional control, and willingness to seek help. Future developments include deeper personalization, wearable device integration, and wider deployment across educational institutions. The system is evaluated using metrics including accuracy, precision, recall, and F1-score across several modalities.
ISSN:2772-5286

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