Authors :
Dr. Anka Setty K.; Sudha H. T.
Volume/Issue :
Volume 10 - 2025, Issue 8 - August
Google Scholar :
https://tinyurl.com/5xb5xwkp
Scribd :
https://tinyurl.com/yc2xytxy
DOI :
https://doi.org/10.38124/ijisrt/25aug243
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
AI-Integrated Research Information Management Systems (RIMS) are increasingly pivotal in transforming how
academic institutions organize, manage, and leverage scholarly information. This study, titled AI-Integrated Research
Information Management: A Comparative Institutional Analysis, investigates the impact of Artificial Intelligence on
enhancing RIMS functions such as metadata accuracy, researcher profiling, publication tracking, and overall research
visibility. Adopting a mixed-method approach, the research combines qualitative insights from structured interviews with
library professionals and system administrators, along with quantitative data drawn from institutional repositories, system
usage reports, and RIMS documentation. The comparative analysis evaluates key parameters including system architecture,
AI-driven functionalities, user engagement, and alignment with global indexing standards. Results reveal that institutions
employing AI-enabled RIMS experience significant gains in automated metadata enrichment, efficient workflow
management, and real-time data analytics, leading to improved discoverability and institutional research performance.
However, the study also identifies persistent challenges such as limited technical infrastructure, integration complexities,
and skill gaps among staff. These barriers impede the broader adoption of AI in research management. To address these
issues, the study recommends actionable strategies such as fostering AI readiness, implementing targeted training programs,
and developing supportive institutional policies. These measures are essential for sustainable and impactful integration of
AI within RIMS across diverse academic contexts.
Keywords :
AI-Integrated RIMS; Research Information Management; Metadata Accuracy; Institutional Repositories; Research Visibility; Academic Libraries; Comparative Study.
References :
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- Cox, A. M., Kennan, M. A., Lyon, L., & Pinfield, S. (2017). Research data management in libraries: An exploratory study. Journal of Information Science, 43(2), 166–191.
- Council of Europe. (2019). Digital transformation and libraries. https://www.coe.int
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- Sahoo, J., & Swain, D. K. (2021). Role of machine learning in research information systems. DESIDOC Journal of Library & Information Technology, 41(4), 301–307.
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- Vidyanidhi. (2021). Digital Library and Metadata Harvesting for Indian Universities. https://www.vidyanidhi.org.in
AI-Integrated Research Information Management Systems (RIMS) are increasingly pivotal in transforming how
academic institutions organize, manage, and leverage scholarly information. This study, titled AI-Integrated Research
Information Management: A Comparative Institutional Analysis, investigates the impact of Artificial Intelligence on
enhancing RIMS functions such as metadata accuracy, researcher profiling, publication tracking, and overall research
visibility. Adopting a mixed-method approach, the research combines qualitative insights from structured interviews with
library professionals and system administrators, along with quantitative data drawn from institutional repositories, system
usage reports, and RIMS documentation. The comparative analysis evaluates key parameters including system architecture,
AI-driven functionalities, user engagement, and alignment with global indexing standards. Results reveal that institutions
employing AI-enabled RIMS experience significant gains in automated metadata enrichment, efficient workflow
management, and real-time data analytics, leading to improved discoverability and institutional research performance.
However, the study also identifies persistent challenges such as limited technical infrastructure, integration complexities,
and skill gaps among staff. These barriers impede the broader adoption of AI in research management. To address these
issues, the study recommends actionable strategies such as fostering AI readiness, implementing targeted training programs,
and developing supportive institutional policies. These measures are essential for sustainable and impactful integration of
AI within RIMS across diverse academic contexts.
Keywords :
AI-Integrated RIMS; Research Information Management; Metadata Accuracy; Institutional Repositories; Research Visibility; Academic Libraries; Comparative Study.