APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN ACADEMIC LIBRARIES

Abstract

There are several obstacles that have prevented libraries from implementing Artificial Intelligence (AI) and Machine Learning (ML). These include organizational opposition to change, a lack of technical skills among library workers, inadequate financing, and inadequate technology infrastructure. The implementation of AI-driven solutions is further hampered by environmental variables like erratic electrical and internet connectivity. Targeted interventions including infrastructure expenditures, capacity-building initiatives, and stakeholder collaboration are crucial to overcoming these challenges and accelerating the adoption of new technologies. Notwithstanding these obstacles, there is no denying that AI and ML's revolutionary promise in libraries. Through sophisticated search features, efficient resource management, and tailored recommendations, these technologies have improved user experiences. By making sure that enormous amounts of data are arranged, accessible, and useful, they have also made it possible for libraries to handle the challenges posed by big data. Library personnel need to develop interdisciplinary skills in fields like data analytics, ML, and digital literacy to fully benefit from these breakthroughs. To educate librarians for the changing demands of a data-driven environment, this emphasizes the significance of ongoing professional growth as well as the integration of AI and ML in educational and training programs. To enhance librarians' comprehension and perspectives on AI and ML applications, the study emphasizes on the necessity of pre-service and in-service training. To handle growing user demands, complicated datasets, and a variety of information sources, public libraries must give priority to investments in AI technologies. Future workers will be more equipped to create creative, organization-specific solutions if AI and ML are taught in library schools and other training facilities.