An analysis of AI-driven automatic indexing in Federal University, Lafia Library, Nasarawa State
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Background of the study
Automatic indexing is essential for efficient information retrieval in large library collections, and AI is playing a transformative role in this domain. At Federal University, Lafia Library, AI-driven systems utilize advanced algorithms to automatically generate indexes by analyzing the content of documents. These systems extract keywords, identify thematic patterns, and assign index terms, thereby enhancing the accuracy and speed of resource retrieval (Okeke, 2023). The implementation of AI in indexing reduces manual labor, minimizes human error, and allows for the continuous updating of indexes as new materials are acquired. Furthermore, the system’s ability to learn from previous indexing decisions improves its performance over time, ensuring that search results remain relevant and up-to-date. However, challenges such as data quality issues, the need for high computational resources, and potential algorithmic biases may limit the effectiveness of AI-driven indexing. This study analyzes the impact of these systems on library operations, focusing on the improvements in search efficiency and overall user satisfaction at Federal University, Lafia Library (Ibrahim, 2024).
Statement of the problem
Although AI-driven automatic indexing offers significant improvements in organizing library resources, Federal University, Lafia Library faces challenges such as data inconsistencies, high resource requirements, and occasional inaccuracies in indexing. These issues can lead to reduced search effectiveness and lower user satisfaction. The study aims to identify these challenges and propose strategies to optimize AI-based indexing, ensuring that the system fully supports efficient information retrieval (Chinwe, 2024).
Objectives of the study
To evaluate the effectiveness of AI-driven automatic indexing.
To identify challenges associated with the indexing process.
To recommend strategies for improving indexing accuracy.
Research questions
How effective is AI in automating the indexing process?
What challenges hinder the performance of AI-driven indexing systems?
What improvements can enhance the accuracy and efficiency of automatic indexing?
Significance of the study
This study is significant as it examines the role of AI in enhancing the indexing process, thereby improving resource discoverability and user satisfaction. The insights gained will help library administrators optimize indexing systems and streamline information retrieval at Federal University, Lafia Library, ultimately contributing to better academic outcomes (Balogun, 2024).
Scope and limitations of the study
Limited to the topic only.
Definitions of terms
Automatic Indexing: The process of generating indexes using AI without human intervention.
Index Terms: Keywords or phrases assigned to documents for categorization.
Algorithmic Bias: Systematic errors in AI outputs due to biased training data.
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