Supporting Faculty in Adapting to Generative Artificial Intelligence

In November 2022, OpenAI's ChatGPT was released and the potential for generative artificial intelligence (GenAI), large language model chatbots to be used by any individual across a range of activities began to be realized. Since that time, other chatbots have arisen including Gemini, CoPilot, Claude, and DeepSeek. Applications have developed that use these large language models to do a range of activities including customer service, editing, and healthcare. These emerging technologies present unprecedented challenges and opportunities for colleges and universities.

For faculty, the growing availability of these chatbots has created challenges and opportunities for student learning. It has been recognized that chatbots can impact academic integrity when students can use chatbots to circumvent expected work. Unlike plagiarism where reliable detection software exists, detectors for GenAI are problematic and prone to false negatives and false positives. Alternatively, skills in using GenAI are becoming increasingly valued by future employers. It is foreseeable that the ability to use GenAI in discipline-specific ways could become an important student learning outcome. For instructors, upholding academic integrity in the face of GenAI while also adapting the classroom to these new opportunities is a challenge.

Supporting faculty in navigating generative AI is important for institutions. Some components of this support could include: 1. Encouraging faculty to have a class policy on GenAI, 2. Providing on-demand resources for instructor learning, 3. Providing community spaces and workshops for discussion of GenAI, 4. Supporting early adopters in trying new GenAI applications in the classroom, and 5. Funding GenAI software that are geared towards the classroom and support exploration while protecting student privacy. This presentation will explore how institutions can support faculty in leveraging these technologies for educational enhancement. It will draw on resources and programming developed at the University of Kansas and suggest proactive and balanced approaches to GenAI.