The role of AI in reducing information overload in Enugu State University of Science and Technology Library, Enugu State
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- NGN 5000
Background of the study
Information overload is a significant challenge in modern libraries, where vast amounts of data can overwhelm users. AI is emerging as a solution to mitigate this issue by filtering and prioritizing information based on relevance and user context. At Enugu State University of Science and Technology Library, AI systems are employed to curate search results, suggest relevant resources, and streamline digital content presentation. These tools utilize machine learning and data analytics to manage large datasets, ensuring that users receive only the most pertinent information (Ibekwe, 2023). By reducing cognitive load, AI not only enhances the user experience but also improves academic productivity. Additionally, these systems support personalized recommendations, making it easier for users to navigate complex digital environments. Despite these advantages, challenges such as algorithmic bias and the need for continual system refinement remain. This study investigates the impact of AI in reducing information overload, exploring its benefits in improving user experience and academic efficiency at Enugu State University of Science and Technology Library (Chukwu, 2024).
Statement of the problem
While AI offers promising solutions to reduce information overload, Enugu State University of Science and Technology Library encounters challenges that hinder its full implementation. Issues such as algorithmic bias, occasional inaccuracies in resource filtering, and resistance from users accustomed to traditional search methods limit the effectiveness of AI tools. These challenges result in suboptimal information retrieval and increased cognitive load, affecting overall user satisfaction. The study seeks to identify these challenges and propose solutions to optimize AI-driven information management (Olu, 2024).
Objectives of the study
To assess the impact of AI on reducing information overload.
To identify challenges in AI-driven information filtering.
To recommend strategies for optimizing AI systems to enhance user experience.
Research questions
How does AI reduce information overload in library settings?
What challenges affect the performance of AI in managing information?
What measures can improve the efficiency of AI-based filtering systems?
Significance of the study
This study is significant as it evaluates the role of AI in mitigating information overload, a critical challenge in modern libraries. The findings will provide actionable insights for enhancing digital resource management and user satisfaction at Enugu State University of Science and Technology Library, ultimately supporting improved academic performance (Adebayo, 2024).
Scope and limitations of the study
Limited to the topic only.
Definitions of terms
Information Overload: The state of being overwhelmed by excessive amounts of information.
Machine Learning: AI technology that enables systems to learn and adapt from data.
Data Analytics: The process of examining data to draw meaningful insights.
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