Future Trends in AI and ML Applications for Library Collection Development: A Roadmap

Abstract

The fast growth of academic material, limited library budgets, and changing user needs have made it even more important to develop new ways to build library collections. This study aims to look at how Artificial Intelligence (AI) and Machine Learning (ML) are being used now and in the future in academic and research libraries. The study will look for trends that might change how libraries work, technologies that make these changes possible, and ethical issues that need to be considered. This paper reviewed academic publications, case studies, and technical reports published between 2014 and 2025. These articles came from Scopus, Web of Science, and Google Scholar. The key findings show that AI and ML technologies are being used successfully for things like predicting demand, performing automated reviews, doing predictive analytics, helping with decisions, automat ing acquisitions, and giving personalised service to customers. These technologies make collection development procedures more scalable, accurate, and responsive. However, using them comes with many ethical problems, such as algorithmic bias, a lack of transparency, data privacy threats, and the possibility that curation would lose its human touch. The study concludes that AI/ML may significantly enhance the process of developing collections. Still, libraries must use them carefully to make sure that people are in charge, that everyone has the same access, and that they are responsible. A strategic roadmap is suggested to help with the responsible use of AI technology. It stresses ethical governance, phased experimentation, and participatory design. This study presents practical implications for library professionals, administrators, and policy-makers aiming to improve their collections while protecting the essential ideals of librarianship.