AI-Integrated Research Information Management: A Comparative Institutional Analysis

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.