Investigation into the Use of AI for Personalized Learning in Bayero University, Kano, Kano State
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- NGN 5000
Background of the Study
Personalized learning has become an essential aspect of modern education, allowing instructional content to be tailored to the unique needs, abilities, and learning styles of individual students. At Bayero University, Kano, Kano State, traditional one-size-fits-all teaching approaches often fail to address the diverse academic backgrounds of students, resulting in suboptimal educational outcomes. Artificial intelligence (AI) offers a promising solution by enabling the development of personalized learning systems that dynamically adapt to student performance and preferences (Ibrahim, 2023). AI-based systems leverage machine learning algorithms to analyze data from various sources such as academic records, interaction logs, and assessment results. This analysis can identify learning gaps, predict future performance, and recommend customized learning resources to enhance comprehension (Chinwe, 2024). Personalized learning platforms can incorporate adaptive feedback mechanisms, ensuring that students receive timely support and are challenged at an appropriate level. Moreover, these systems facilitate the continuous monitoring of student progress, enabling educators to intervene proactively when necessary. The integration of AI into personalized learning not only improves academic outcomes but also increases student engagement and motivation by making learning experiences more relevant and interactive. Despite the clear advantages, implementing AI-based personalized learning systems presents challenges such as data privacy, the need for extensive training datasets, and potential biases in algorithmic decision-making. This study aims to investigate the use of AI for personalized learning at Bayero University by developing and evaluating an AI-driven platform that tailors educational content to individual student needs (Olufemi, 2025). The research seeks to contribute to the optimization of teaching methodologies and to enhance the overall quality of education through technological innovation.
Statement of the Problem
Bayero University currently relies on traditional teaching methods that offer limited personalization, often resulting in generalized instruction that does not cater to the diverse learning needs of students. This lack of personalized support contributes to uneven academic performance and disengagement among students (Adebola, 2023). Traditional methods are unable to continuously assess individual learning progress or adapt teaching strategies in real time, leaving at-risk students without the targeted intervention they require. Although AI-based personalized learning systems have demonstrated potential in other contexts, their implementation in Bayero University is still in its infancy. Challenges such as data integration, algorithmic bias, and ensuring user privacy hinder the full realization of these systems. The absence of an effective personalized learning platform prevents the university from optimizing educational content, thereby impacting student satisfaction and overall academic success. This study seeks to address these issues by developing an AI-driven personalized learning system that uses real-time data analytics to adapt instructional materials to the individual needs of students. By leveraging machine learning and natural language processing, the system aims to provide tailored learning experiences that enhance comprehension and engagement. The study will evaluate the platform’s performance in terms of student outcomes and user satisfaction, and identify challenges that need to be overcome for widespread adoption.
Objectives of the Study:
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To design and implement an AI-based personalized learning platform.
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To evaluate the effectiveness of the system in enhancing student academic performance.
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To recommend strategies for addressing challenges related to data integration and algorithmic bias.
Research Questions:
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How effective is the AI-based system in providing personalized learning experiences?
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What impact does personalized learning have on student engagement and performance?
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What challenges must be addressed to optimize AI-driven personalized learning at the university?
Significance of the Study
This study is significant as it explores the transformative impact of AI on personalized learning at Bayero University. The research provides a data-driven approach to tailoring educational content to individual student needs, potentially improving academic outcomes and student satisfaction. The findings will offer actionable insights for educators and policymakers, promoting the integration of AI into teaching practices and advancing the digital transformation of higher education (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to the use of AI for personalized learning at Bayero University, Kano, Kano State, and does not extend to other academic interventions or institutions.
Definitions of Terms:
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Personalized Learning: An educational approach that tailors instruction to individual student needs.
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Artificial Intelligence (AI): The simulation of human intelligence in machines using algorithms and machine learning.
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Adaptive Feedback: Real-time responses provided to students based on their performance.
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