The impact of AI-driven citation analysis in academic research libraries in Federal University, Lokoja Library, Kogi State
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Background of the study
Citation analysis is a pivotal process in academic research, enabling the evaluation of research impact and scholarly communication. At Federal University, Lokoja Library, Kogi State, AI-driven citation analysis is transforming how libraries assess research quality. By leveraging machine learning and data mining, AI systems analyze citation patterns, identify influential research, and provide insights into academic trends (Brown, 2025). This technology facilitates comprehensive evaluations of research outputs, aiding librarians and researchers in identifying key literature and emerging topics. The automation of citation analysis reduces the time and effort required for manual evaluation, thereby increasing the accuracy and efficiency of research assessments. Despite these advantages, challenges such as data standardization, integration with diverse databases, and potential algorithmic biases persist (Adams, 2024). This study examines the impact of AI-driven citation analysis in enhancing research support and decision-making within academic libraries, aiming to improve the quality of scholarly communication and resource management (Smith, 2023).
Statement of the problem
Although AI-driven citation analysis offers promising improvements in evaluating academic research, Federal University, Lokoja Library experiences challenges related to data inconsistency and algorithmic bias. These issues affect the accuracy of citation metrics and hinder the ability to draw reliable conclusions about research impact, thereby limiting the system’s effectiveness in supporting academic research (Brown, 2025).
Objectives of the study
To evaluate the effectiveness of AI-driven citation analysis in academic research.
To identify challenges related to data integration and algorithmic bias.
To recommend strategies for improving citation analysis accuracy.
Research questions
How effective is AI in analyzing citation patterns?
What challenges hinder the accuracy of citation analysis?
How can citation analysis methods be optimized for better research evaluation?
Significance of the study
This study is significant as it explores the impact of AI-driven citation analysis on enhancing research evaluation processes. The findings will benefit librarians and academic administrators by providing insights to refine research support systems and improve scholarly impact assessments (Brown, 2025; Adams, 2024).
Scope and limitations of the study
The study is confined to AI-driven citation analysis at Federal University, Lokoja Library, Kogi State, and does not cover other research evaluation methods.
Definitions of terms
Citation Analysis: The process of evaluating research impact based on citation data.
Algorithmic Bias: Systematic errors in AI algorithms that lead to skewed results.
Data Mining: The process of discovering patterns in large data sets.
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