Exploring AI-Based Student Stress Level Detection Using Facial Expressions in Gashua LGA, Yobe State
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- NGN 5000
Background of the Study
The mental health of students is a crucial factor in their academic success and overall well-being. One of the significant contributors to poor academic performance and mental health issues is stress, which affects students' cognitive abilities, concentration, and social interactions. Traditional methods of identifying stress in students rely on self-reporting, which can be inaccurate due to students' reluctance to openly discuss their mental health. Recent advancements in artificial intelligence (AI) and facial recognition technology have made it possible to detect stress levels through facial expressions, providing a non-intrusive and efficient way to monitor students' mental states. This study explores the use of AI-based systems for detecting student stress levels through facial expressions in Gashua LGA, Yobe State, to provide early intervention and support mechanisms to students experiencing stress.
Statement of the Problem
In educational environments like Gashua LGA, Yobe State, students often face various academic, social, and personal challenges that lead to stress. Traditional methods of identifying stress, such as surveys or counseling sessions, often miss students who are reluctant to seek help or report their struggles. Additionally, the ongoing stress faced by students has become a growing concern, especially in the context of increased academic demands. AI-based facial expression recognition offers a promising solution to detect stress without relying on verbal communication, but its application in this setting has not been explored. There is a need to develop an AI system that can accurately assess student stress levels using facial expressions and provide timely interventions.
Objectives of the Study
1. To design and implement an AI-based facial expression recognition system to detect stress levels in students at schools in Gashua LGA.
2. To evaluate the effectiveness of AI-based stress detection systems compared to traditional methods.
3. To assess the feasibility of implementing AI-based stress detection for supporting student mental health in Gashua LGA.
Research Questions
1. How accurate is the AI-based system in detecting student stress levels through facial expressions?
2. How does AI-based stress detection compare with traditional methods of identifying student stress?
3. What is the potential impact of using AI for stress detection on student mental health support systems?
Research Hypotheses
1. The AI-based facial expression recognition system will accurately detect student stress levels.
2. The AI-based system will be more efficient in detecting student stress compared to traditional self-reporting methods.
3. The implementation of AI-based stress detection will lead to improved student support and mental health interventions.
Significance of the Study
This study will enhance the understanding of how AI technologies, particularly facial expression recognition, can be used to monitor and manage student stress in educational settings. By providing a tool for early stress detection, it may contribute to the overall well-being of students and improve academic outcomes in Gashua LGA, Yobe State.
Scope and Limitations of the Study
The study will focus on exploring the use of AI-based facial expression recognition to detect student stress in secondary schools within Gashua LGA, Yobe State. Limitations include the availability and quality of facial expression data, as well as the potential resistance from students or staff to new AI technologies.
Definitions of Terms
• AI-Based Facial Expression Recognition: The use of artificial intelligence algorithms to analyze facial expressions and interpret emotions, such as stress.
• Stress Detection: The identification of students experiencing mental or emotional stress, typically indicated by physiological and behavioral signs.
• Mental Health Support: Programs or interventions designed to help students manage mental health challenges, including stress.
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