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 enrolment size, public/private status, college Carnegie class or type, and level of tuition.

Table of contents

The Questionnaire

Characteristics of the sample

Participant List

List of Tables

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
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
Table 1.2.1 How much time (in minutes) have you spent using each of the following applications in the past week? Bard/Gemini
Table 1.2.2 How much time (in minutes) have you spent using each of the following applications in the past week? Bard/Gemini Broken out by tuition, $
Table 1.2.3 How much time (in minutes) have you spent using each of the following applications in the past week? Bard/Gemini Broken out by enrollment
Table 1.2.4 How much time (in minutes) have you spent using each of the following applications in the past week? Bard/Gemini Broken out by public or private college
Table 1.2.5 How much time (in minutes) have you spent using each of the following applications in the past week? Bard/Gemini Broken out by type of college or Carnegie Class
Table 1.2.6 How much time (in minutes) have you spent using each of the following applications in the past week? Bard/Gemini Broken out by age of respondent
Table 1.2.7 How much time (in minutes) have you spent using each of the following applications in the past week? Bard/Gemini Broken out by gender of respondent
Table 1.3.1 How much time (in minutes) have you spent using each of the following applications in the past week? AI-enabled Bing
Table 1.3.2 How much time (in minutes) have you spent using each of the following applications in the past week? AI-enabled Bing Broken out by tuition, $
Table 1.3.3 How much time (in minutes) have you spent using each of the following applications in the past week? AI-enabled Bing Broken out by enrollment
Table 1.3.4 How much time (in minutes) have you spent using each of the following applications in the past week? AI-enabled Bing Broken out by public or private college
Table 1.3.5 How much time (in minutes) have you spent using each of the following applications in the past week? AI-enabled Bing Broken out by type of college or Carnegie Class
Table 1.3.6 How much time (in minutes) have you spent using each of the following applications in the past week? AI-enabled Bing Broken out by age of respondent
Table 1.3.7 How much time (in minutes) have you spent using each of the following applications in the past week? AI-enabled Bing Broken out by gender of respondent
Table 2.1 To what degree have you tried to apply these applications mentioned above, or other AI applications, to the work involved in interlibrary loan?
Table 2.2 To what degree have you tried to apply these applications mentioned above, or other AI applications, to the work involved in interlibrary loan? Broken out by tuition, $
Table 2.3 To what degree have you tried to apply these applications mentioned above, or other AI applications, to the work involved in interlibrary loan? Broken out by enrollment
Table 2.4 To what degree have you tried to apply these applications mentioned above, or other AI applications, to the work involved in interlibrary loan? Broken out by public or private college
Table 2.5 To what degree have you tried to apply these applications mentioned above, or other AI applications, to the work involved in interlibrary loan? Broken out by type of college or Carnegie Class
Table 2.6 To what degree have you tried to apply these applications mentioned above, or other AI applications, to the work involved in interlibrary loan? Broken out by age of respondent
Table 2.7 To what degree have you tried to apply these applications mentioned above, or other AI applications, to the work involved in interlibrary loan? Broken out by gender of respondent
Table 3.1 If you have applied artificial intelligence to the inter-library loan process what has been the impact on the productivity of the interlibrary loan process?
Table 3.2 If you have applied artificial intelligence to the inter-library loan process what has been the impact on the productivity of the interlibrary loan process? Broken out by tuition, $
Table 3.3 If you have applied artificial intelligence to the inter-library loan process what has been the impact on the productivity of the interlibrary loan process? Broken out by enrollment
Table 3.4 If you have applied artificial intelligence to the inter-library loan process what has been the impact on the productivity of the interlibrary loan process? Broken out by public or private college
Table 3.5 If you have applied artificial intelligence to the inter-library loan process what has been the impact on the productivity of the interlibrary loan process? Broken out by type of college or Carnegie Class
Table 3.6 If you have applied artificial intelligence to the inter-library loan process what has been the impact on the productivity of the interlibrary loan process? Broken out by age of respondent
Table 3.7 If you have applied artificial intelligence to the inter-library loan process what has been the impact on the productivity of the interlibrary loan process? Broken out by gender of respondent
Table 4.1 Has your library created a new or adjusted an existing chatbox to guide patrons through facets of the ILL process?
Table 4.2 Has your library created a new or adjusted an existing chatbox to guide patrons through facets of the ILL process? Broken out by tuition, $
Table 4.3 Has your library created a new or adjusted an existing chatbox to guide patrons through facets of the ILL process? Broken out by enrollment
Table 4.4 Has your library created a new or adjusted an existing chatbox to guide patrons through facets of the ILL process? Broken out by public or private college
Table 4.5 Has your library created a new or adjusted an existing chatbox to guide patrons through facets of the ILL process? Broken out by type of college or Carnegie Class
Table 4.6 Has your library created a new or adjusted an existing chatbox to guide patrons through facets of the ILL process? Broken out by age of respondent
Table 4.7 Has your library created a new or adjusted an existing chatbox to guide patrons through facets of the ILL process? Broken out by gender of respondent
Table 5.1 Has the widespread availability of generative AI application such as CHatGPT and Bard/Gemini led you to use ILL applications for routing ILL requests such as WorldCat xAnalytics and Ex Libris Rapid and similar applications, more, less or has it had no impact on their use?
Table 5.2 Has the widespread availability of generative AI application such as CHatGPT and Bard/Gemini led you to use ILL applications for routing ILL requests such as WorldCat xAnalytics and Ex Libris Rapid and similar applications, more, less or has it had no impact on their use? Broken out by tuition, $
Table 5.3 Has the widespread availability of generative AI application such as CHatGPT and Bard/Gemini led you to use ILL applications for routing ILL requests such as WorldCat xAnalytics and Ex Libris Rapid and similar applications, more, less or has it had no impact on their use? Broken out by enrollment
Table 5.4 Has the widespread availability of generative AI application such as CHatGPT and Bard/Gemini led you to use ILL applications for routing ILL requests such as WorldCat xAnalytics and Ex Libris Rapid and similar applications, more, less or has it had no impact on their use? Broken out by public or private college
Table 5.5 Has the widespread availability of generative AI application such as CHatGPT and Bard/Gemini led you to use ILL applications for routing ILL requests such as WorldCat xAnalytics and Ex Libris Rapid and similar applications, more, less or has it had no impact on their use? Broken out by type of college or Carnegie Class
Table 5.6 Has the widespread availability of generative AI application such as CHatGPT and Bard/Gemini led you to use ILL applications for routing ILL requests such as WorldCat xAnalytics and Ex Libris Rapid and similar applications, more, less or has it had no impact on their use? Broken out by age of respondent
Table 5.7 Has the widespread availability of generative AI application such as CHatGPT and Bard/Gemini led you to use ILL applications for routing ILL requests such as WorldCat xAnalytics and Ex Libris Rapid and similar applications, more, less or has it had no impact on their use? Broken out by gender of respondent
Table 6 Have you used AI to partially or fully automate any of the following ILL workflows?
Table 6.1.1 Have you used AI to partially or fully automate any of the following ILL workflows? Copyright Clearance
Table 6.1.2 Have you used AI to partially or fully automate any of the following ILL workflows? Copyright Clearance Broken out by tuition, $
Table 6.1.3 Have you used AI to partially or fully automate any of the following ILL workflows? Copyright Clearance Broken out by enrollment
Table 6.1.4 Have you used AI to partially or fully automate any of the following ILL workflows? Copyright Clearance Broken out by public or private college
Table 6.1.5 Have you used AI to partially or fully automate any of the following ILL workflows? Copyright Clearance Broken out by type of college or Carnegie Class
Table 6.1.6 Have you used AI to partially or fully automate any of the following ILL workflows? Copyright Clearance Broken out by age of respondent
Table 6.1.7 Have you used AI to partially or fully automate any of the following ILL workflows? Copyright Clearance Broken out by gender of respondent
Table 6.2.1 Have you used AI to partially or fully automate any of the following ILL workflows? Managing Returns or Shipments
Table 6.2.2 Have you used AI to partially or fully automate any of the following ILL workflows? Managing Returns or Shipments Broken out by tuition, $
Table 6.2.3 Have you used AI to partially or fully automate any of the following ILL workflows? Managing Returns or Shipments Broken out by enrollment
Table 6.2.4 Have you used AI to partially or fully automate any of the following ILL workflows? Managing Returns or Shipments Broken out by public or private college
Table 6.2.5 Have you used AI to partially or fully automate any of the following ILL workflows? Managing Returns or Shipments Broken out by type of college or Carnegie Class
Table 6.2.6 Have you used AI to partially or fully automate any of the following ILL workflows? Managing Returns or Shipments Broken out by age of respondent
Table 6.2.7 Have you used AI to partially or fully automate any of the following ILL workflows? Managing Returns or Shipments Broken out by gender of respondent
Table 6.3.1 Have you used AI to partially or fully automate any of the following ILL workflows? Personalizing Content Recommendations
Table 6.3.2 Have you used AI to partially or fully automate any of the following ILL workflows? Personalizing Content Recommendations Broken out by tuition, $
Table 6.3.3 Have you used AI to partially or fully automate any of the following ILL workflows? Personalizing Content Recommendations Broken out by enrollment
Table 6.3.4 Have you used AI to partially or fully automate any of the following ILL workflows? Personalizing Content Recommendations Broken out by public or private college
Table 6.3.5 Have you used AI to partially or fully automate any of the following ILL workflows? Personalizing Content Recommendations Broken out by type of college or Carnegie Class
Table 6.3.6 Have you used AI to partially or fully automate any of the following ILL workflows? Personalizing Content Recommendations Broken out by age of respondent
Table 6.3.7 Have you used AI to partially or fully automate any of the following ILL workflows? Personalizing Content Recommendations Broken out by gender of respondent
Table 6.4.1 Have you used AI to partially or fully automate any of the following ILL workflows? Search for titles and Other Resources
Table 6.4.2 Have you used AI to partially or fully automate any of the following ILL workflows? Search for titles and Other Resources Broken out by tuition, $
Table 6.4.3 Have you used AI to partially or fully automate any of the following ILL workflows? Search for titles and Other Resources Broken out by enrollment
Table 6.4.4 Have you used AI to partially or fully automate any of the following ILL workflows? Search for titles and Other Resources Broken out by public or private college
Table 6.4.5 Have you used AI to partially or fully automate any of the following ILL workflows? Search for titles and Other Resources Broken out by type of college or Carnegie Class
Table 6.4.6 Have you used AI to partially or fully automate any of the following ILL workflows? Search for titles and Other Resources Broken out by age of respondent
Table 6.4.7 Have you used AI to partially or fully automate any of the following ILL workflows? Search for titles and Other Resources Broken out by gender of respondent
Table 6.5.1 Have you used AI to partially or fully automate any of the following ILL workflows? Processing Requests
Table 6.5.2 Have you used AI to partially or fully automate any of the following ILL workflows? Processing Requests Broken out by tuition, $
Table 6.5.3 Have you used AI to partially or fully automate any of the following ILL workflows? Processing Requests Broken out by enrollment
Table 6.5.4 Have you used AI to partially or fully automate any of the following ILL workflows? Processing Requests Broken out by public or private college
Table 6.5.5 Have you used AI to partially or fully automate any of the following ILL workflows? Processing Requests Broken out by type of college or Carnegie Class
Table 6.5.6 Have you used AI to partially or fully automate any of the following ILL workflows? Processing Requests Broken out by age of respondent
Table 6.5.7 Have you used AI to partially or fully automate any of the following ILL workflows? Processing Requests Broken out by gender of respondent
What advice can you offer your peers in the use of AI in in interlibrary loan?
Table 7.1 After the next two years, how much more or less productive, defined by successful borrowing and lending transactions with a fixed amount of staff time and money to spend, do you feel that your office will be?
Table 7.2 After the next two years, how much more or less productive, defined by successful borrowing and lending transactions with a fixed amount of staff time and money to spend, do you feel that your office will be? Broken out by tuition, $
Table 7.3 After the next two years, how much more or less productive, defined by successful borrowing and lending transactions with a fixed amount of staff time and money to spend, do you feel that your office will be? Broken out by enrollment
Table 7.4 After the next two years, how much more or less productive, defined by successful borrowing and lending transactions with a fixed amount of staff time and money to spend, do you feel that your office will be? Broken out by public or private college
Table 7.5 After the next two years, how much more or less productive, defined by successful borrowing and lending transactions with a fixed amount of staff time and money to spend, do you feel that your office will be? Broken out by type of college or Carnegie Class
Table 7.6 After the next two years, how much more or less productive, defined by successful borrowing and lending transactions with a fixed amount of staff time and money to spend, do you feel that your office will be? Broken out by age of respondent
Table 7.7 After the next two years, how much more or less productive, defined by successful borrowing and lending transactions with a fixed amount of staff time and money to spend, do you feel that your office will be? Broken out by gender of respondent

Methodology

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Just a few of this 55-page report’s many findings are that:

Table of Contents

Methodology

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