[Eril-l] Primary Research Group Releases New Report: Survey of Library Science Faculty: Contributions of Content to AI Models, ISBN 979-8-88517-323-0

[Electronic Resources in Libraries discussion list]

Primary Research Group has published a new report, *Survey of Library
Science Faculty: Contributions of Content to AI Models*, 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 (Table 2.1).

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.

Availability

   - Excerpt + Table of Contents: Available at
   https://www.primaryresearch.com/AddCart.aspx?ReportID=868
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.eril-l.org/pipermail/eril-l-eril-l.org/attachments/20251117/2f5408af/attachment.htm>