Chatbots for reference services in academic libraries: Applications and trends

As academic libraries adapt to evolving to meet changing user expectations and digital service models, chatbots have emerged as a promising tool for enhancing reference support. This study investigates the current landscape of chatbot applications in academic library settings by examining both rule-based and AI-powered systems deployed across institutions around the world. Through a comprehensive environmental scan and literature review, 31 library chatbots were selected representing a diverse range of service models and technological maturity. The study assesses the chatbot performance, responsiveness, and ethical transparency through a set of structured queries, including service-related questions, information retrieval tasks, and conversational prompts. It reveals a generally low global adoption rate of chatbots in academic libraries, with development varying significantly across regions. Notably, 94 % of the chatbots were AI-powered or hybrid systems, and 42 % offered multilingual support. Most chatbots performed reliably on basic service queries, yet only some of them demonstrated strategic messaging or extended service capabilities. Significant variation was observed in semantic interpretation and recommendation quality, with many chatbots struggling to deliver contextually relevant or metadata-rich responses. Critically, only 13 % disclosed privacy policies which reveals the widespread gaps in ethical transparency and user awareness. These findings underscore the need for improved semantic modeling, broader service integration, and clearer documentation to ensure that AI-enabled library systems align with institutional values and meet diverse user needs.