Purpose This paper aims to provide an overview of key definitions related to ChatGPT, a public tool developed by OpenAI, and its underlying technology, Generative Pretrained Transformer (GPT). Design/methodology/approach This paper includes an interview with ChatGPT on its potential impact on academia and libraries. The interview discusses the benefits of ChatGPT such as improving search and discovery, reference and information services; cataloging and metadata generation; and content creation, as well as the ethical considerations that need to be taken into account, such as privacy and bias. Findings ChatGPT has considerable power to advance academia and librarianship in both anxiety-provoking and exciting new ways. However, it is important to consider how to use this technology responsibly and ethically, and to uncover how we, as professionals, can work alongside this technology to improve our work, rather than to abuse it or allow it to abuse us in the race to create new scholarly knowledge and educate future professionals. Originality/value This paper discusses the history and technology of GPT, including its generative pretrained transformer model, its ability to perform a wide range of language-based tasks and how ChatGPT uses this technology to function as a sophisticated chatbot.

This article discusses OpenAI's ChatGPT, a generative pre-trained transformer, which uses natural language processing to fulfill text-based user requests (i.e., a "chatbot"). The history and principles behind ChatGPT and similar models are discussed. This technology is then discussed in relation to its potential impact on academia and scholarly research and publishing. ChatGPT is seen as a potential model for the automated preparation of essays and other types of scholarly manuscripts. Potential ethical issues that could arise with the emergence of large language models like GPT-3, the underlying technology behind ChatGPT, and its usage by academics and researchers, are discussed and situated within the context of broader advancements in artificial intelligence, machine learning, and natural language processing for research and scholarly publishing.

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Employing approaches adopted from studies of library and information science (LIS) research trends performed by Järvelin et al., this content analysis systematically examines the evolution and distribution of LIS research topics and data collection methods at 6‐year increments from 2006 to 2018. Bibliographic data were collected for 3,422 articles published in LIS journals in the years 2006, 2012, and 2018. While the classification schemes provided in the Järvelin studies do not indicate much change, an analysis of subtopics, data sources, and keywords indicates a substantial impact of social media and data science on the discipline, which emerged at some point between the years of 2012 and 2018. These findings suggest a type of shift in the focus of LIS research, with social media and data science topics playing a role in well over one‐third of articles published in 2018, compared with approximately 5% in 2012 and virtually none in 2006. The shift in LIS research foci based on these two technologies/approaches appears similar in extent to those produced by the introduction of information systems in library science in the 1960s, or the Internet in the 1990s, suggesting that these recent advancements are fundamental to the identity of LIS as a discipline.

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