Design of an AI-Based Mental Health Monitoring System for University Students: A Case Study of Federal University, Birnin Kebbi (Birnin Kebbi LGA, Kebbi State)
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Background of the Study
Mental health is an increasingly critical concern for university students, as they face academic pressures, social challenges, and personal struggles that can impact their well-being. At Federal University, Birnin Kebbi, located in Birnin Kebbi LGA, Kebbi State, there has been a noticeable rise in mental health issues among students, affecting their academic performance and overall life satisfaction. Traditionally, universities rely on counselors and periodic surveys to monitor student mental health, but these methods are often reactive rather than proactive.
Artificial Intelligence (AI) offers a promising solution for monitoring mental health, particularly by detecting early signs of mental health issues and providing personalized support. AI-based systems, particularly those using machine learning and natural language processing (NLP), can analyze students’ online interactions, behaviors, and responses to detect potential mental health concerns. Such systems can help identify students at risk, provide timely interventions, and guide counselors in offering personalized care. This study aims to design and evaluate an AI-based mental health monitoring system that utilizes real-time data to assess and track the mental well-being of university students at Federal University, Birnin Kebbi.
Statement of the Problem
At Federal University, Birnin Kebbi, there is an increasing need for a proactive approach to mental health monitoring, as the current methods are often insufficient to detect early signs of mental health problems. The lack of a continuous monitoring system means that students are not always provided with timely support. AI-based solutions, which can track and analyze students' behaviors and mental health indicators in real-time, could significantly improve the university's ability to address this issue, but the implementation of such a system is currently uncharted territory at the university.
Objectives of the Study
1. To design an AI-based mental health monitoring system tailored for university students at Federal University, Birnin Kebbi.
2. To assess the effectiveness of the AI-based system in detecting mental health issues among students.
3. To evaluate the impact of AI-based monitoring on the timely intervention and overall mental health support provided to students.
Research Questions
1. How can AI-based systems be designed to monitor and detect mental health concerns in university students?
2. How effective is the AI-based mental health monitoring system in identifying students at risk for mental health issues?
3. How does the implementation of an AI-based mental health monitoring system impact the intervention process and student well-being?
Research Hypotheses
1. The AI-based mental health monitoring system will effectively detect early signs of mental health issues among university students.
2. The implementation of the AI-based system will result in timely intervention and improved student well-being.
3. Students will report greater satisfaction with mental health support services after the implementation of the AI-based monitoring system.
Significance of the Study
This study will contribute to the development of an AI-based system for monitoring mental health in university settings, providing a proactive approach to mental health management. The findings will help Federal University, Birnin Kebbi, improve the mental health support services for students and may serve as a model for other universities aiming to address student mental health concerns.
Scope and Limitations of the Study
The study will focus on the design, development, and evaluation of an AI-based mental health monitoring system for students at Federal University, Birnin Kebbi, located in Birnin Kebbi LGA, Kebbi State. The study will be limited to the mental health monitoring aspect and will not cover the complete mental health services provided by the university.
Definitions of Terms
• AI-Based Mental Health Monitoring System: A system that uses artificial intelligence techniques to analyze student behaviors and indicators to detect mental health concerns.
• Machine Learning: A type of AI that allows systems to learn and improve from experience without explicit programming.
• Natural Language Processing (NLP): A subfield of AI that enables machines to interpret and respond to human language.
• Mental Health Monitoring: The ongoing tracking of students’ mental well-being through behavioral and psychological indicators.
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