Survey of Use of Artificial Intelligence in Inter-Library Loan Management
This study presents data from 33 academic libraries about their use of artificial intelligence in various facets of inter-library loan management, with separate datasets for use in copyright clearance, returns and deliveries, title and content searches, patron service requests, personalized content recommendations, and other tasks connected to interlibrary loan management.
Survey participants also give data or commentary or both on how much they are using AI, which applications they are using, and what their plans are for the future. The report provides specific datasets on the amount of worktime spent on ChatGPT, Bard/Gemini, and AI enabled Bing. In addition, survey participants evaluate the present and presumed future impact of AI on interlibrary loan management.
Just a few of this 55-page report’s many findings are that:
Interlibrary loan librarians at institutions charging annual tuition of less than $7,500 showed the highest mean usage time of ChatGPT, a mean of approximately 23 minutes per week.
No survey participant was using AI to enhance, accompany or in any way service title searches.
Interlibrary loan librarians at public colleges have a slightly more optimistic view compared to those at private colleges, with some at public institutions expecting up to a more than 50% increase in productivity, while interlibrary loan librarians at private colleges are less hopeful for such significant improvements.
Data in the report is broken out by numerous institutional variables such as enrollment size, public/private status, college Carnegie class or type, and level of tuition.
- Table 1 How much time (in minutes) have you spent using each of the following
- applications in the past week?
- Table 1.1.1 How much time (in minutes) have you spent using each of the following
- applications in the past week? ChatGPT
- Table 1.1.2 How much time (in minutes) have you spent using each of the following
- applications in the past week? ChatGPT Broken out by tuition, $
- Table 1.1.3 How much time (in minutes) have you spent using each of the following
- applications in the past week? ChatGPT Broken out by enrollment
- Table 1.1.4 How much time (in minutes) have you spent using each of the following
- applications in the past week? ChatGPT Broken out by public or private college .... 21
- Table 1.1.5 How much time (in minutes) have you spent using each of the following
- applications in the past week? ChatGPT Broken out by type of college or Carnegie
- Class
- Table 1.1.6 How much time (in minutes) have you spent using each of the following
- applications in the past week? ChatGPT Broken out by age of respondent
- Table 1.1.7 How much time (in minutes) have you spent using each of the following
- applications in the past week? ChatGPT Broken out by gender of respondent
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