An analysis of policy documents from 116 R1 universities in the United States found that 63% of these universities encourage the use of generative AI, and 41% provide detailed guidance on its use in the classroom. More than half of the institutions discussed the ethics of using generative AI, but the majority of instruction focused on using generative AI for writing activities. This study Computers in human behavior: Android.
Generative AI is a type of artificial intelligence that creates new content, such as text, images, audio, code, and video, based on patterns learned from large datasets. It works by using models such as neural networks to predict and generate output that resembles human-authored content.
People use generative AI to create documents, summarize information, create artwork, design products, and automate everyday tasks. We also support scientific research by analyzing data, generating hypotheses, and helping design code and experiments. Businesses use it to improve productivity across customer support, marketing, prototyping, and many workflows.
In education, generative AI helps students learn by providing explanations, tutoring, and personalized feedback. In medicine, it helps interpret data, write reports, and even explore molecular designs for new drugs. Artists and designers use it to explore creative variations and accelerate the creative process. However, generative AI also raises concerns about misinformation, copyright issues, and ethical use.
Study author Nora MacDonald and her colleagues wanted to find out what guidance higher education institutions are providing to their constituents regarding the use of generative AI, what the overall sentiment is about its use, and how that sentiment translates into actual guidelines.
They were also interested in the ethical and privacy considerations that would be included in the guidelines. These authors point out that although the use of generative AI (mainly ChatGPT) became very popular soon after its release, there are still strong voices in educational settings against the use of such applications.
The study authors used the Carnegie classification framework for classifying U.S. universities to collect policy documents and guidelines published on the Internet from 116 R1 institutions. According to this classification, R1 institutions are universities with the highest level of research activity.
The researchers downloaded documents that specifically dealt with generative AI, resulting in a total of 141 documents. Four researchers reviewed 20 of these documents and created a codebook (a coding system for classifying documents according to content). This system was then used to classify all other documents.
They found that 56% of institutions provided faculty with a sample syllabus that included policies regarding the use of generative AI, and 55% of institutions provided examples of permission statements such as “accept,” “restrict,” and “prohibit.” 50% provided activities to help instructors integrate and leverage generative AI in the classroom, but 44% did not recommend the use of discovery tools aimed at detecting AI-generated work. 54% provided guidance on designing assignments in a way that discourages student use of generative AI, and 23% provided guidance on how to use AI detection tools.
Overall, 63% of universities encouraged the use of generative AI, and 41% provided detailed guidance on its use in the classroom. The majority of instruction focused on writing activities. References to codes and STEM-related activities were rare, and when they were mentioned, they were often vague. 52% of institutions discussed the ethics of generative AI on a wide range of topics.
“Based on our findings, we caution that teacher guidance may become burdensome as policies suggest or imply significant modifications to existing educational practices,” the study authors concluded.
This study contributes to a scientific understanding of U.S. universities' attitudes toward the use of generative AI. However, the findings are based on an analysis of policy documents rather than a study of actual classroom practices, and therefore may not fully reflect the provisions specified in the policy.
The paper, “Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines,” was authored by Nora McDonald, Aditya Johri, Areej Ali, and Aayushi Hingle Collier.
