The effectiveness of AI-based library user profiling for personalized learning in Kebbi State University of Science and Technology Library, Aliero, Kebbi State
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- 1-5 Chapters
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- Table of Content: Available
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- NGN 5000
Background of the study
AI-based user profiling is redefining personalized learning by tailoring library services to individual user preferences and learning styles. At Kebbi State University of Science and Technology Library, Aliero, advanced AI systems analyze users’ search histories, borrowing patterns, and interaction data to create detailed profiles. These profiles enable the library to offer personalized recommendations, customized study plans, and targeted learning resources that enhance academic performance (Abubakar, 2023). By leveraging machine learning and data analytics, the system continuously refines its understanding of user needs, thereby improving the relevance of personalized content and fostering a more engaging learning environment. The integration of user profiling into library services not only supports individualized learning but also drives better resource utilization. Nevertheless, challenges such as data privacy concerns, algorithmic biases, and the need for continuous data quality improvement remain. This study evaluates the effectiveness of AI-based user profiling in enhancing personalized learning experiences, focusing on its impact on student engagement and academic outcomes (Ibrahim, 2024).
Statement of the problem
Despite the potential of AI-based user profiling to enhance personalized learning, Kebbi State University of Science and Technology Library faces obstacles in its implementation. Issues include data privacy concerns, limited accuracy in capturing user behavior, and challenges related to maintaining up-to-date user profiles. These factors reduce the overall effectiveness of personalized recommendations, thereby impacting student satisfaction and learning outcomes. The study aims to identify these challenges and propose strategies to improve user profiling systems for better personalized learning (Uche, 2024).
Objectives of the study
To assess the impact of AI-based user profiling on personalized learning.
To identify challenges affecting profiling accuracy and data privacy.
To recommend strategies for optimizing personalized learning services.
Research questions
How effective is AI-based user profiling in personalizing library services?
What challenges limit the accuracy and effectiveness of user profiling?
What measures can improve the personalized learning experience?
Significance of the study
This study is significant as it provides insights into how AI can tailor library services to individual learning needs, enhancing academic performance. The findings will guide library administrators in refining profiling systems, ensuring better engagement and more effective personalized learning at Kebbi State University of Science and Technology Library (Adeniyi, 2024).
Scope and limitations of the study
Limited to the topic only.
Definitions of terms
User Profiling: The process of collecting and analyzing user data to tailor services.
Personalized Learning: Education customized to individual student needs.
Machine Learning: AI technology that improves through experience and data.
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