This study looks at how higher education faculty are contributing their
content and services to AI applications, with separate data sets for
contributions to commercial AI companies, university applications, trade
and professional association efforts, and more information efforts among
academic peers.  The study also examines how faculty view contributing to
AI applications, and whether they believe that their content may have
already been used without their consent.  The study measures paid sales or
licenses to AI applications, as well as gratis contributions.

The study also looks at the issue of the development of libraries or
archives of prompts for ChatGPT, Bard and other applications, and defines
the percentage of faculty participating in such activities.  AI
applications perform only as well as their end users enable them to, and a
new emerging form of intellectual property – AI application prompts – are a
fertile area for department chair, research offices, teaching and learning
centers,  institutional digital repositories and academic libraries.

Just a few of this 129-page report’s many findings are that:

• 3.6% of faculty were sure that their content was being used by AI
applications without their consent and another 38.61% were unsure.

• Male faculty were far more likely than female faculty to have taken part
in efforts to develop AI applications.

• Efforts to develop archives or libraries of prompts for ChatGPT were most
common among faculty in medicine, mathematics/statistics/computer science
and architecture/fine/visual arts.

Currently, the major AI platforms are engaged in an arms race to build the
biggest databases, the ones trained on the most content.  However, industry
observers often suggest that the AI industry will turn from what one might
call quantity to quality.  Applications will be focused on particular
applications, and appeal to a narrower, but paying audience.  The
scientific databases of the future will not only be places to find
information, but to manipulate information, develop new information based
on old information, and even conduct science.

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.

For a table of contents, the questionnaire and an excerpt – view the
product page for this report at:
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