An assessment of AI-based search engines for academic research in Federal University, Dutsin-Ma Library, Katsina State
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- NGN 5000
Background of the study
Academic research increasingly relies on advanced search engines to access a vast array of digital resources. At Federal University, Dutsin-Ma Library, Katsina State, AI-based search engines are being adopted to enhance the retrieval of academic materials. These systems leverage natural language processing, machine learning, and semantic analysis to provide more accurate and contextually relevant search results (Ibrahim, 2023). By improving query interpretation and result ranking, AI search engines facilitate more efficient research processes and support academic productivity. Moreover, they assist in overcoming challenges associated with traditional keyword-based searches, such as irrelevant results and information overload. The implementation of AI search technologies at Dutsin-Ma Library represents a transformative step towards modernizing academic research support services. However, challenges related to system integration, user adaptation, and ongoing maintenance remain critical issues that need to be addressed. This study evaluates the performance of AI-based search engines and examines their impact on the research capabilities of library users, with an emphasis on improving resource accessibility and user satisfaction (Bello, 2024).
Statement of the problem
Although AI-based search engines promise enhanced efficiency and accuracy in academic research, their adoption at Federal University, Dutsin-Ma Library faces several challenges. Issues such as limited user training, technical integration problems, and system reliability have been observed, which can hinder optimal performance. These factors create barriers to the effective utilization of AI search tools and potentially reduce the overall research productivity of users. Addressing these issues is critical to ensure that the benefits of AI in academic research are fully realized (Ibrahim, 2023).
Objectives of the study
To evaluate the performance of AI-based search engines in academic research.
To identify challenges affecting the effective use of these search engines.
To recommend improvements for enhancing search engine performance and user satisfaction.
Research questions
How effective are AI-based search engines in retrieving academic resources?
What challenges do users face when utilizing these tools?
What improvements can be made to optimize the performance of AI search systems?
Significance of the study
This study is significant as it evaluates the impact of AI-based search engines on academic research, offering insights into their effectiveness and identifying areas for improvement. The findings will benefit academic institutions, library staff, and researchers by guiding enhancements in digital search tools and resource management (Ibrahim, 2023; Bello, 2024).
Scope and limitations of the study
The study is limited to the use of AI-based search engines at Federal University, Dutsin-Ma Library, focusing solely on academic research support services.
Definitions of terms
AI-based Search Engine: A digital tool that uses artificial intelligence to retrieve and rank academic resources.
Natural Language Processing (NLP): Technology that enables computers to understand and interpret human language.
Semantic Analysis: The process of deriving meaning from text data using computational methods.
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