The impact of AI based indexing on search efficiency in library databases in Federal University Lokoja, Lokoja LGA, Kogi State
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Background of the Study
AI‑based indexing systems are revolutionizing how libraries manage and retrieve information. At Federal University Lokoja, the incorporation of artificial intelligence into library database indexing aims to streamline search processes and improve the precision of search outcomes. By automating the categorization and classification of vast arrays of digital resources, AI enables faster retrieval and more accurate results (Okoro, 2023). Improved indexing enhances user experience by reducing search time and simplifying the navigation of complex databases. However, challenges such as algorithmic bias, data inconsistency, and integration issues with legacy systems may compromise efficiency. This study critically examines the impact of AI‑based indexing on search efficiency, comparing traditional methods with modern AI approaches. The research evaluates technical performance, user satisfaction, and system reliability, providing insights into the effectiveness of AI in enhancing the overall library service experience (Adebayo, 2024).
Statement of the Problem
Although AI‑based indexing holds promise for transforming database searches, users at Federal University Lokoja have reported mixed experiences. Some indicate improved search results, while others encounter issues such as irrelevant results and occasional system glitches (Okoro, 2023). These discrepancies may be due to algorithm biases or insufficient training data, which negatively affect search efficiency. This study seeks to evaluate these operational issues, assess the gap between expected and actual performance, and identify the factors that limit the effectiveness of AI‑based indexing, thereby impacting overall user satisfaction (Adebayo, 2024).
Objectives of the Study:
To assess the effectiveness of AI‑based indexing in library databases.
To identify issues limiting search efficiency.
To recommend improvements for optimal AI integration.
Research Questions:
How does AI‑based indexing improve search efficiency?
What technical challenges affect its performance?
What strategies can enhance its effectiveness?
Significance of the Study
This study is significant as it evaluates the transformative role of AI‑based indexing in enhancing search efficiency. The results will guide future technological improvements, ensuring more accurate and rapid information retrieval, which in turn supports academic research and user satisfaction (Okoro, 2023).
Scope and Limitations of the Study:
The study is confined to the evaluation of AI‑based indexing in the library databases of Federal University Lokoja.
Definitions of Terms:
AI‑based Indexing: The use of artificial intelligence to categorize and organize digital resources.
Search Efficiency: The speed and accuracy with which relevant information is retrieved.
Database: A structured digital repository of academic resources.
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