Proposed curriculum for library and information science (LIS) education to meet the emerging trends in artificial intelligence (AI)

Purpose This study aims to propose a Library and Information Science (LIS) curriculum that responds to emerging trends in artificial intelligence (AI). Design/methodology/approach Data were collected through document analysis of Master’s LIS curricula at the University of Zambia and the University of KwaZulu-Natal. Official curriculum documents were systematically reviewed and analysed thematically using Braun and Clarke’s (2006) six-phase framework. A comparative analysis of the two curricula was then conducted to synthesise findings and inform the proposed curriculum. Findings This study revealed no explicit AI components in either programme, underscoring the need for integration. A proposed framework encompasses five areas: Core LIS Principles, AI Fundamentals and Applications, Data Management and Analytics, Technological Proficiency and Ethics and Policy Frameworks. Integrating AI offers opportunities such as enhanced graduate competencies, improved employability, curriculum innovation, library transformation and interdisciplinary collaboration. Challenges include a shortage of qualified staff, limited funding, inadequate infrastructure, ethical concerns, algorithmic bias and implementation difficulties. Research limitations/implications This study was limited to accessible documents, excluding informal aspects of delivery and student experiences that were not known to the researchers. Originality/value To the best of authors’ knowledge, this is the first comparative study of LIS curricula between the University of Zambia and the University of KwaZulu-Natal. It introduces an original 11-themed taxonomy for evaluating AI and related content in LIS education, revealing critical gaps such as data management and analytics. This study provides contextually relevant and actionable recommendations for integrating AI into existing curriculum structures without a complete redesign, offering a replicable methodology for African and global institutions preparing information professionals for the AI era.