Survey of Library Science Faculty: Contributions of Content to AI Models
Description
The Survey of Library Science Faculty: Contributions of Content to AI Models is the first systematic look at how library science faculty are contributing—voluntarily or otherwise—to artificial intelligence training data, and how they perceive the use, reuse, and risks associated with their scholarly and instructional materials.
The report provides extensive data tables, faculty subgroup analysis, and open-ended commentary that together map a rapidly evolving relationship between academic content creation and AI model development. Findings cover issues ranging from voluntary submissions to concerns about unauthorized use, departmental activities, and early classroom experimentation with AI-assisted tools.
Some Key Findings
More than half of faculty believe they may have content suitable for training AI models — or are unsure.
55.55% of respondents either said they have AI-trainable content (24.44% “Yes”) or are unsure (31.11% “Not really sure”).
Just 6.67% of faculty reported submitting articles, data, or instructional materials for use in AI training.
Nearly 29% say “Yes” when asked whether their content has been used in AI models without permission, and uncertainty is widespread
11.11% of faculty report using their own class materials—notes, videos, research, or texts—in a teaching chatbot or model.
Just 8.89% say their department has taken steps to collect or prepare faculty content for AI use, and several respondents describe opaque or revenue-driven departmental motives.
About the Report
Survey of Library Science Faculty: Contributions of Content to AI Models includes:
150+ tables (sample dependent) breaking down responses by:
Institutional rank
Data Broken Out by Carnegie Classification
Data Broken Out by Enrollment size
Data Broken Out by Faculty Rank
Data Broken Out by Age, Gender, and Political Views
Data Broken Out by Sector (public vs. private)
Data Broken Out by Level of Teaching Load
Verbatim open-ended responses illustrating concerns about unauthorized use, shifting expectations, and emerging instructional experimentation
Analysis of faculty uncertainty regarding obligations, rights, and opportunities as AI developers and universities seek new sources of training data
This report is essential reading for library schools, academic departments, faculty leaders, publishers, university administrators, and anyone seeking to understand how the emergence of AI models intersects with academic authorship and scholarly rights.
The report provides extensive data tables, faculty subgroup analysis, and open-ended commentary that together map a rapidly evolving relationship between academic content creation and AI model development. Findings cover issues ranging from voluntary submissions to concerns about unauthorized use, departmental activities, and early classroom experimentation with AI-assisted tools.
Some Key Findings
More than half of faculty believe they may have content suitable for training AI models — or are unsure.
55.55% of respondents either said they have AI-trainable content (24.44% “Yes”) or are unsure (31.11% “Not really sure”).
Just 6.67% of faculty reported submitting articles, data, or instructional materials for use in AI training.
Nearly 29% say “Yes” when asked whether their content has been used in AI models without permission, and uncertainty is widespread
11.11% of faculty report using their own class materials—notes, videos, research, or texts—in a teaching chatbot or model.
Just 8.89% say their department has taken steps to collect or prepare faculty content for AI use, and several respondents describe opaque or revenue-driven departmental motives.
About the Report
Survey of Library Science Faculty: Contributions of Content to AI Models includes:
150+ tables (sample dependent) breaking down responses by:
Institutional rank
Data Broken Out by Carnegie Classification
Data Broken Out by Enrollment size
Data Broken Out by Faculty Rank
Data Broken Out by Age, Gender, and Political Views
Data Broken Out by Sector (public vs. private)
Data Broken Out by Level of Teaching Load
Verbatim open-ended responses illustrating concerns about unauthorized use, shifting expectations, and emerging instructional experimentation
Analysis of faculty uncertainty regarding obligations, rights, and opportunities as AI developers and universities seek new sources of training data
This report is essential reading for library schools, academic departments, faculty leaders, publishers, university administrators, and anyone seeking to understand how the emergence of AI models intersects with academic authorship and scholarly rights.
Table of Contents
76 Pages
- Table 1.1 Do you feel that you have articles, data, lab logs, code, blog posts, class records or videos, personally developed educational materials, or any other content connected to your research that could be used to train an AI model?
- Table 2.1 Have you submitted any of your articles, data, lab logs, code, blog posts, class records or videos, personally developed educational materials or any other content connected to your research for inclusion in an AI model? If you have already contributed content to train an AI application briefly describe what you have contributed and, if possible, to what kind of effort.
- Table 3 To which of the following have you ever contributed your content outlined in the past two questions for the development of an AI model by any of the parties below? (check all that apply)
- Table 4.1 To the best of your knowledge has content developed by you been used by an AI model without your permission?
- Table 5.1 Have you ever sold or licensed any content developed by you to any developer of an AI model or engine?
- Table 6.1 To the best of your knowledge, has your college/university department made any effort to collect and prepare content from its scholars to be used in an AI model of any kind?
- Table 7.1 Have you used your own class and lecture notes, videos or tapes of your classes, research work, texts or other info in a chatbot or model developed for your class or students?
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