The application of artificial intelligence in cataloguing by the City of Tshwane libraries

Abstract

The aim of the study was to explore the adoption of artificial intelligence (AI) in cataloguing by the City of Tshwane libraries (CoT). The objectives of the study were to determine the knowledge of the CoT libraries cataloguers about AI, to explore the attitudes of the CoT libraries cataloguers about AI, to establish the readiness of the CoT libraries cataloguers in adopting AI in cataloguing, to identify the challenges that the CoT libraries cataloguers are experiencing in the adoption of AI and to explore the cataloguing practices using AI at the CoT libraries. The study took place within the cataloguing division of the CoT Community and Social Development Services (CSDS). The study embraced the interpretivism research paradigm and employed qualitative research methodologies, including research design, sampling techniques, defining the study’s population, outlining data collection procedures, delineating data analysis strategies, and establishing quality criteria. The research methodology involved conducting an in-depth interview with six cataloguers from the CoT libraries. Thematic analysis and direct quotations were used to analyse the collected data. The findings indicated that older participants reported limited knowledge of AI, whereas younger participants described it more comprehensively. All participants were optimistic about AI’s potential to improve productivity and accuracy in their work. Additionally, they all used SirsiDynix Symphony WorkFlows, OCLC, and WebDewey for cataloguing and desired improvements, particularly in detecting duplicate ISBNs. In conclusion, the insights provided by participants offer valuable considerations for the integration of AI-driven solutions within the CoT cataloguing section. The thorough understanding of cataloguing workflows lays a solid foundation for leveraging AI technologies to enhance efficiency and accuracy in cataloguing operations. The recommendations outlined in response to participants’ insights aim to support the CoT cataloguing section in optimising cataloguing practices and embracing innovative solutions such as AI technologies. It is further recommended that the CoT cataloguing section should initiate targeted education, awareness programs, training and AI skill acquisition. These initiatives should aim to bridge the knowledge gap and alleviate any scepticism or reluctance towards AI integration, particularly among the older generation of cataloguers.