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Survey of Higher Education Faculty 2024, Use of Artificial Intelligence Applications

Publisher Primary Research Group
Published Dec 06, 2023
Length 113 Pages
SKU # PF18483128

Description

Survey of Higher Education Faculty 2024, Use of Artificial Intelligence Applications


In summary, while the overall interest in making a significant investment of time in learning AI applications like ChatGPT and Bard is relatively low across the board, there are notable differences based on academic titles, types of institutions, demographics, and fields of study. Particularly, Black or African American and Asian or Asian American faculty, as well as those in specific fields like Architecture and Medicine, show a higher inclination towards investing significant time in learning ChatGPT. For Bard, the interest is generally lower, but certain demographics like Asian or Asian American faculty and those in Economics/Finance show a bit more inclination towards significant investment.
This study looks closely at which AI applications higher education faculty are using, how much they are using them, how important they are to them, and how much they plan to use them in the future.  The study provides distinct data sets on how much time faculty spend on ChatGPT, Bard, AI-enabled Bing, and Llama. It also provides data on how much time survey participants plan to spend learning about each application, and just how much an impact they expect AI to make on their research, scholarship and teaching. Respondents estimate how important AI is to their work currently, and how important they expect it to be in the future.  The study enables higher education policymakers to pinpoint the growth of use of AI among faculty, enabling better and  more targeted assistance, workshops, and other services, and aid in developing institutional polices based on hard data.

Data in the report is based on a representative survey of 777 higher education faculty; the data is presented in the aggregate and also broken out by a broad range of institutional and personal characteristics including age, gender, race/ethnicity, income level, work title and academic field, as well as institution size, type and public/private status, among other variables.

Table of Contents

113 Pages
Table 1.1.1 In the past week how much time in total aggregate weekly minutes did you spend using each AI application? Bard.
Table 1.1.2 In the past week how much time in total aggregate weekly minutes did you spend using each AI application? Bard. Broken out by academic title
Table 1.1.3 In the past week how much time in total aggregate weekly minutes did you spend using each AI application? Bard. Broken out by enrollment
Table 1.1.4 In the past week how much time in total aggregate weekly minutes did you spend using each AI application? Bard. Broken out by type of college or Carnegie Class
Table 1.1.5 In the past week how much time in total aggregate weekly minutes did you spend using each AI application? Bard. Broken out by public or private college

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