Development of Quantum-Inspired AI for Student Performance Prediction in Bayero University, Kano, Kano State
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- 1-5 Chapters
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- NGN 5000
Background of the Study
Artificial Intelligence (AI) has become a transformative technology in various fields, including education. AI techniques, such as machine learning, have been applied to predict student performance, enabling educational institutions to identify students who may require additional support. However, classical AI algorithms can be limited by their processing power and the complexity of the data they handle. Quantum-inspired AI, which leverages principles of quantum mechanics to enhance traditional machine learning models, offers the potential to overcome these limitations. Quantum-inspired AI algorithms can process data more efficiently and uncover patterns that may not be detectable by classical algorithms.
Bayero University in Kano State is exploring innovative ways to improve student performance prediction through advanced AI models. By adopting quantum-inspired AI techniques, the university can enhance the accuracy of performance predictions, allowing for early intervention and personalized academic support for students. This study aims to develop a quantum-inspired AI model for predicting student performance, using quantum-enhanced machine learning algorithms to analyze academic data and predict future outcomes.
Statement of the Problem
Student performance prediction is a crucial aspect of modern educational systems, as it helps institutions to proactively address academic challenges and support students in need. However, the accuracy of traditional AI models in predicting student performance is often limited by the complexity of the data and the inefficiency of classical algorithms. Quantum-inspired AI, by mimicking quantum computational methods, has the potential to provide more accurate and robust predictions. This study addresses the problem of improving student performance prediction at Bayero University, Kano, by developing a quantum-inspired AI system capable of processing complex academic data and making more precise predictions.
Objectives of the Study
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To develop a quantum-inspired AI model for predicting student performance at Bayero University.
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To evaluate the accuracy and efficiency of quantum-inspired AI compared to traditional machine learning models in predicting student outcomes.
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To explore the potential benefits of quantum-inspired AI for improving academic support and student retention rates.
Research Questions
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How can quantum-inspired AI improve the accuracy of student performance predictions?
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What are the advantages of using quantum-inspired AI over traditional machine learning techniques in the educational context?
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What challenges might Bayero University face in implementing quantum-inspired AI models for student performance prediction?
Significance of the Study
This study is significant because it explores the potential of quantum-inspired AI to enhance student performance prediction, which could lead to more effective academic support systems and improved student outcomes. By developing a more accurate prediction model, the study aims to contribute to the advancement of AI in education, providing valuable insights into how quantum technologies can be applied to solve real-world problems in the academic sector.
Scope and Limitations of the Study
The study will focus on the development and evaluation of a quantum-inspired AI model for predicting student performance at Bayero University, Kano. Limitations include the availability of quantum computing infrastructure and the challenges in adapting quantum-inspired AI algorithms to the specific academic data and requirements of the university.
Definitions of Terms
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Quantum-Inspired AI: AI algorithms that are influenced by quantum computing principles, utilizing enhanced computational methods to improve the performance and efficiency of classical machine learning models.
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Student Performance Prediction: The use of machine learning or AI models to predict students' future academic performance based on historical data and various academic factors.
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Machine Learning: A subset of AI that allows computers to learn from data without being explicitly programmed, often used for prediction and classification tasks.
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