[ PAPER ID: 28453 ] AI-BASED PSYCHOLOGICAL ASSESSMENT TOOLS: TRANSFORMING STUDENT MENTAL HEALTH MONITORING IN EDUCATIONAL INSTITUTIONS

ARTICLE INFO: Date of Submission: Nov 30, 2025, Revised: Dec 22, 2025, Accepted: Dec 26, 2025, CrossRef d.o.i. : https://doi.org/10.56815/ijmrr.v4i4.2025.271-279, How To Cite: Gawali. K. C (2025). AI-Based Psychological Assessment Tools: Transforming Student Mental Health Monitoring in Educational Institutions. International Journal of Multidisciplinary Research & Reviews, 4(4), 271-279.

Authors

  • Prof. (Dr.) Kranti Chandrashekhar Gawali Professor and Head, Department of Psychology, Bhavan's College Autonomous Andheri west, India.

Abstract

The prevalence of mental health concerns among students has risen significantly in recent years, driven by academic pressure, social challenges, and increased digital engagement. Educational institutions are increasingly tasked with identifying and addressing issues such as anxiety, depression, and stress in a timely and effective manner. However, traditional psychological assessment methods primarily based on self-report questionnaires and clinical interviews are often constrained by limited scalability, subjective bias, delayed intervention, and insufficient access to trained professionals. In response, artificial intelligence (AI)-based psychological assessment tools have emerged as a transformative approach to student mental health monitoring. This review paper examines the evolving landscape of AI-driven tools, including machine learning models, natural language processing techniques, and affective computing systems, which enable continuous, datadriven, and scalable assessment of psychological well-being. The paper further explores their applications within educational institutions, such as early detection of at-risk students, real-time monitoring, and personalized intervention strategies. Additionally, this review critically evaluates the ethical, methodological, and practical challenges associated with AI integration, including concerns related to data privacy, algorithmic bias, transparency, and the interpretability of automated decisions. While findings suggest that AI-based tools significantly enhance early identification and broaden access to mental health support, they also underscore the need for responsible implementation. Overall, AI holds substantial promise in transforming student mental health monitoring; however, its effectiveness depends on a balanced integration with human oversight, ethical safeguards, and context-sensitive deployment.

Keywords:

Artificial Intelligence (Ai), Psychological Assessment, Student Mental Health, Machine Learning (Ml), Natural Language Processing (Nlp), Affective Computing

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