Design of AI-Based Student Well-being and Mental Health Monitoring Systems in Bama LGA, Borno State
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- NGN 5000
Background of the Study
Mental health issues among students in higher education have become increasingly prominent, with rising concerns about stress, anxiety, depression, and other conditions. Traditional methods of monitoring student well-being, such as surveys or face-to-face counseling, often fail to capture real-time data or identify students in distress before a crisis occurs. Artificial Intelligence (AI) has the potential to revolutionize mental health monitoring by providing continuous, real-time assessment through various data sources, such as social media activity, academic performance, and self-reported data.
In Bama LGA, Borno State, there is an urgent need to address the mental health and well-being of students, especially considering the region's socio-political challenges. This study proposes the development of an AI-based student well-being and mental health monitoring system that can detect signs of mental health issues early, providing timely interventions and support.
Statement of the Problem
In Bama LGA, Borno State, mental health challenges among students are often overlooked, and there is limited access to mental health services due to social stigma, lack of resources, and logistical issues. Traditional mental health monitoring methods are not always effective in identifying students at risk or providing real-time interventions. An AI-based system that monitors students’ mental health and well-being based on their behaviors and academic patterns could significantly improve early detection and support. However, the feasibility of implementing such a system in the region remains largely unexplored.
Objectives of the Study
1. To design and implement an AI-based system for monitoring student well-being and mental health in Bama LGA, Borno State.
2. To evaluate the effectiveness of the system in identifying students at risk of mental health issues.
3. To assess the challenges and opportunities of implementing AI-based mental health monitoring systems in Nigerian schools.
Research Questions
1. How effective is the AI-based system in identifying students at risk of mental health issues based on their behavioral and academic patterns?
2. What are students’ and counselors’ perceptions of the usefulness of the AI-based mental health monitoring system?
3. What challenges are faced in implementing AI-based mental health monitoring systems in educational institutions?
Research Hypotheses
1. The AI-based monitoring system is more effective in identifying students at risk of mental health issues than traditional methods.
2. Students and counselors perceive the AI-based mental health monitoring system as a useful tool for early intervention.
3. The implementation of the AI-based system faces challenges related to data privacy, system accuracy, and student engagement.
Significance of the Study
This study will contribute to improving the mental health and well-being of students in Bama LGA, Borno State, by introducing an innovative AI-based monitoring system. The findings will provide a model for other educational institutions to adopt AI solutions for mental health support, improving student well-being and academic performance.
Scope and Limitations of the Study
The study will focus on the design, implementation, and evaluation of the AI-based mental health monitoring system in Bama LGA, Borno State. Limitations include the need for proper data privacy management, potential resistance from students or staff, and limited access to necessary technology.
Definitions of Terms
• AI-Based Mental Health Monitoring System: A system that uses artificial intelligence to track students’ well-being based on behavioral data and academic patterns.
• Mental Health: Psychological well-being, including the absence of mental health issues such as anxiety, depression, and stress.
• Early Intervention: The process of identifying and addressing mental health issues before they develop into more severe problems.
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