Key Takeaways from Faculty Conversations

One of the often-cited articles from Microsoft, AI at Work Is Here. Now Comes the Hard Part, sheds light on how many people are using GenAI in the workplace, and how we can move forward. Articles like this are inspiring and shouldn’t be left unread, but much of the research and writing is not as comprehensive.

Throughout this project, I was exposed to many imperatives faculty are facing as they provide workforce education and support professional technical (prof tech) experiences to students. It would be impossible to provide a comprehensive look without spending another three months of work compiling summaries and translating my notes into comprehensive prose. And so here are some takeaways that emerged from the conversations, some of which, but not all, are also in harmony with the literature in the bibliography. Note that these are all presented as takeaways for faculty, and may result in more questions than answers. Note also that the majority of these takeaways came from the conversations with faculty, and the temporary nature of these results may make them slightly out of date by the time of this guide’s publication.

1. Many faculty are so overwhelmed with their curriculum and everyday responsibilities that they have not considered GenAI. This includes not being aware of what GenAI is (or only having heard of ChatGPT), and the way students may be using it (including misconduct issues), let alone impacts on GenAI on jobs and the workforce.
2. Additionally, many faculty have been focused on GenAI misconduct, which has kept them from thinking about workforce impacts. Mitigation of GenAI misuse has been a priority and the time and effort it takes to adjust curricula and teaching practices to respond to GenAI has shut down explorations into workforce education.
3. Some faculty are adamantly against GenAI and have concerns about its place in the workforce, notably the impact it will have on removing, reducing, or negatively impacting jobs. This stance ultimately countered interest in teaching students about GenAI and their fields.
4. Some faculty are aware of the potential of GenAI in the classroom and/or the workforce, and some even use it as educators for their own tasks and work, but have not formalized integrating it into curricula. Additionally, there is a distinct lack of formal connections to the workforce / employers in general, which has kept conversations around GenAI and workforce impacts from occurring.
5. A handful of faculty I talked with are actively facilitating conversations around GenAI with their students, but in an informal way. They discuss AI technologies and technology in general through a lens of curiosity, and respond to student interest more than building in content into the curriculum formally. I got the sense that this was the case for many “special topics” with some programs, given how short and rigid the associate degrees are. Importantly, student demand for knowledge around GenAI and its impacts on jobs did significantly not come up during any conversations.
6. Multiple departments and programs have very robust, ongoing support from advisory boards, which provide insight into what is happening in industry. In one case, the advisory board actually recommended faculty center GenAI in their program because the workforce was already changing/changed and the demand for new technology skills was clear. Another advisory board I was privy to talking with had addressed GenAI as a focus point / concern, but did not have any recommendations on integrating it into the curriculum.
7. No workforce programs I encountered had any substantial, set curriculum around GenAI, though one program ran a course about GenAI as a test. In general the programs did not have any collaborative or collective stance on GenAI, including statements around the use of GenAI, or the inclusion of language around where it may be impacting the workforce. While faculty engagement with GenAI ranged widely, in almost all programs it appeared that individual faculty were or were not responding to GenAI on individual levels, most likely indicative of a lack of capacity to work on a collective response.
8. Many faculty were aware of various departments responding to GenAI (including eLearning and the Library), and had similar questions around college-level responses to the technology, rather than program- or department-specific responses.
9. There are concerns around the normalization of GenAI technology outside of the college (in K-12 schooling, in everyday life, and from students who are actively or were recently employed) and how the normalization of this technology may impact higher education.
10. There are concerns around a disconnect between workforce programs and the reality of the jobs, and where faculty may not be privy to changes in job requirements or expectations from employers in the context of GenAI and other technologies.
11. There are concerns from some faculty around a lack of a collective movement around GenAI. Locally we have seen growing efforts toward addressing GenAI at the college, but some faculty are hoping for more college-wide/college-universal responses, including from administration.

Because this list is not exhaustive, it is important to recognize there may very well have been additional perspectives and situations that came up during the faculty conversations and meetings, which are not represented here. Also acknowledging that many of the meetings I had were not entirely representative of programs and departments, due to my meetings being with specific leadership or representatives of the program or department due to timing, interest, and so on, can leave perspectives out. Ultimately, I hope that this “short list” reflects a basic range / diversity within the perspectives and situations of many of the faculty I connected with during the project, even with the list’s lack of exhaustiveness and other flaws.