Survey of US Higher Education Faculty 2024, Contributions of Content & Design to AI Applications

Survey of US Higher Education Faculty 2024, Contributions of Content & Design to AI Applications


5.92% of faculty sampled have taken part in an effort to develop prompts for ChatGPT that might be used as a form of template that could benefit others. 8.57% of deans/department heads/distinguished professors have taken part in such efforts, the highest percentage by job title. This kind of effort was most common at subject specialized colleges, at which 14.29% of faculty in the sample had taken part. It was also largely negatively correlated with the age of faculty; in general, and with modest exception, the younger thefaculty member, the greater the propensity to participate in efforts to develop shared prompts. Such efforts were also more common among Hispanic, Asian-origin and mixed race faculty than among White or Black faculty. By academic field it was most common in medicine, mathematics/statistics/computer science and architecture/fine/visual arts.


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.

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 1.2 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? Broken out by academic title
Table 1.3 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? Broken out by enrollment

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