Advancing library operations with AI: data-driven insights for academic resource management

Abstract

Introduction. The rapid evolution of artificial intelligence technologies affords opportunities and challenges for libraries. The study analyses the application of artificial intelligence tools for business intelligence purposes in a university library.

Method. This study used artificial neural networks to extract metadata from a syllabus corpus and then applied a string-matching model to integrate the extracted data with the loan library database. Finally clustering algorithms were employed to analyse the results, providing valuable insights into resource usage patterns. Data was extracted from faculty databases from one university in Colombia.

Results. This study identified the potential of integrating artificial intelligence with business intelligence tools to enhance resource management and optimise university library operations, facilitating a better alignment between academic syllabi and available materials.

Conclusions. The study found that artificial intelligence tools are valuable for university libraries in optimising processes based on data analysis. This suggests that libraries should design and implement business intelligence initiatives to automate manual tasks, providing valuable information to managers and academic directors for decision-making in administrative and academic contexts.