AI-driven personalization in library and information services: A systematic review of techniques, user outcomes, and ethical considerations

This article aims to systematically review AI-driven personalization in library services with AI, focusing on techniques, user outcomes, and ethical issues. The study conducted a systematic review in accordance with the 2020 PRISMA guidelines, searching Scopus, Web of Science Core Collection, and IEEE Xplore. From an initial pool of 460 articles, 24 were selected for analysis. The findings indicate that Natural Language Processing (NLP), recommendation systems, and machine learning algorithms are the most commonly used and effective methods for personalizing services. These are applied in reference chatbots for personalized assistance, automated indexing, and generative AI (GenAI) systems for content creation and summarization. These technologies enable more natural interactions and adaptable services, significantly improving user satisfaction and service quality. The deliberate use of GenAI offers a new perspective for developing smart, user-centered libraries and strategically enhancing the human role. The conclusion emphasizes that AI-driven personalization, especially through recommendation systems and 24/7 chatbots, enhances user experience and satisfaction. However, the study notes that most of these systems are limited to laboratory settings. The article systematically reviews the challenges and limitations of these services, providing solutions and recommendations for future research. • The most effective AI methods for personalizing library services are NLP recommendation systems and machine learning algorithms. • AI personalization improves operational efficiency, user satisfaction, and service quality through task automation and 24/7 support. • AI adoption in libraries poses ethical challenges, such as privacy concerns, algorithmic bias, and lack of transparency in "black box" models. • AI is transforming libraries from traditional collections into smart, user-centered spaces, marking a move toward digital innovation. • A sustainable AI future in libraries needs a balanced approach that enhances, not replaces, the essential human role.