Design of an AI-Powered System for Early Detection of Mental Health Issues in University Students: A Case Study of Federal University, Dutsin-Ma (Dutsin-Ma LGA, Katsina State)
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- NGN 5000
Background of the Study
Mental health challenges among university students have been on the rise in recent years, with factors such as academic pressure, social isolation, and financial stress contributing to the prevalence of mental health issues (Williams et al., 2023). Early identification and intervention are crucial to addressing these issues and preventing them from affecting students' academic performance, well-being, and overall life satisfaction (Miller & Clark, 2024). However, traditional mental health support services often struggle to reach students in a timely manner, with many students hesitant to seek help due to stigma or lack of awareness (Brown et al., 2023).
AI-powered systems present a promising solution for the early detection of mental health issues. By analyzing a range of data points, including students' academic performance, social media activity, and behavioral patterns, AI systems can identify potential signs of mental health challenges before they become severe (Lee et al., 2025). Federal University, Dutsin-Ma, offers an opportunity to explore how AI technologies can be used to design an early detection system for mental health issues, offering timely support and interventions to students in need.
Statement of the Problem
Mental health issues are often detected late among university students, leading to delays in intervention and worsening conditions (Thompson & George, 2024). At Federal University, Dutsin-Ma, existing mental health support systems are reactive rather than proactive, making it difficult to identify students at risk before issues escalate. This study will investigate the potential of AI-powered systems in detecting early signs of mental health issues in university students, aiming to improve timely intervention and support.
Objectives of the Study
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To design an AI-powered system for early detection of mental health issues among university students at Federal University, Dutsin-Ma.
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To assess the effectiveness of AI in identifying students at risk of mental health challenges.
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To evaluate the potential impact of early mental health detection on student well-being and academic performance at Federal University, Dutsin-Ma.
Research Questions
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How can AI technologies be used for the early detection of mental health issues in university students?
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How effective are AI systems in identifying students at risk of mental health problems at Federal University, Dutsin-Ma?
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What impact can early detection have on the well-being and academic performance of students?
Research Hypotheses
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AI-powered systems will significantly improve the early detection of mental health issues in university students.
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Early identification of mental health issues using AI will lead to better academic performance and overall well-being for students.
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The implementation of AI-powered mental health detection systems will enhance the effectiveness of support services at Federal University, Dutsin-Ma.
Significance of the Study
This study will highlight the potential of AI technologies in improving mental health support services in university settings. By providing early detection of mental health issues, AI-powered systems can help students receive timely interventions, thereby enhancing their academic success and overall quality of life.
Scope and Limitations of the Study
The study will focus on the mental health issues faced by students at Federal University, Dutsin-Ma, in Dutsin-Ma LGA, Katsina State. Limitations include data availability on student behaviors and the challenges associated with ensuring students' privacy and consent in using AI-powered detection systems.
Definitions of Terms
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AI-Powered System: A system that uses artificial intelligence algorithms to process data and make predictions or decisions without human intervention.
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Mental Health Issues: Psychological or emotional conditions that affect an individual's thoughts, feelings, and behaviors, potentially hindering academic performance and personal well-being.
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Early Detection: The process of identifying potential problems or conditions at an early stage, often before they become severe or disruptive.
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