This comprehensive study looks at who, how, and how often higher education faculty are using artificial intelligence applications to search for, cite and summarize information. The study provides highly distinct data sets for each of these purposes, and focuses on the precise use of specific applications, with separate data for use of ChatGPT, Bard, Bing and other applications. The study enables its readers to find out exactly how higher education faculty are using AI and what their experience has been. For librarians focusing on information literacy and search strategy, it provides a highly detailed map of faculty experience with AI as a supplement to, or even a replacement of, traditional search engines and library search interfaces. This detailed report enables its readers to find out exactly how AI is beginning to reshape information and scholarly searching.
Data in the report is based on a representative survey of 777 higher education faculty; data is broken out by a range of personal and institutional variables, including but not limited to personal characteristics such as work title, academic field, gender, personal income, and race/ethnicity, and institutional attributes such as Carnegie class, public/private status, and enrollment.
Just a few of this 133-page report’s many findings are that:
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Department heads are more likely than other faculty to experiment with developing proper citations from AI searches.
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Untenured faculty who are on a tenure track are also much more likely than others to adjust their prompts to request specific sourcing information from AI applications.
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The majority of respondents (63.19%) expressed little to no confidence in the accuracy of sourcing and citation information from AI applications.
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Males were twice as likely as females to use AI to recommend journals to which to submit research articles.
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ChatGPT use was highest among faculty in architecture, fine and visual arts.