This study presents data from 23 colleges and universities about how they are using various artificial intelligence applications in academic library cataloging. The report gives separate data sets for Bard, AI-enabled Bing and ChatGPT, defining the extent and kind of use. The study also gives data for use of these programs for particular applications, such as for subject headings, descriptions, indexing, cataloging training and other uses. The data also explores the impact of use of these applications on other cataloging tools such as the Library of Congress Catalogers Desktop Developers Tool, MarcEdit and OCLC’s Connexion. In addition, the report details exactly how much time catalogers are spending on AI tools, their level of overall satisfaction with them, and their plans for the future.
Just a few of this 96-page report’s many findings are that:
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For automatic subject heading assignment, 11.54% of the total sample reported usage, with no significant differences across demographic or institutional characteristics.
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Survey participants from the smallest colleges were more likely than those from larger ones to report high productivity increases resulting from AI use in cataloging.
Half of all survey respondents report using ChatGPT in cataloging functions.
The data is derived from a sample of 23 cataloging librarians; it is presented in the aggregate and also broken out by institutional characteristics of the respondent's institutions such as enrollment sizes, Carnegie classes, public or private status, and tuition levels, as well as by some personal characteristics of the respondents, such as age, gender, and other variables.