Project Summary and Results
The project that lasted from Winter to Spring 2024 was complex and fluid, starting with some structure but ultimately becoming organized chaos. This page attempts to centralize the work that occurred. Undoubtedly some information has been left out or overlooked, but I hope that this small slice of insight into the project will be useful for your review.
Project Summary
With the goal of the project centering workforce education and GenAI, I first had to understand workforce education’s scope. The project began with a thorough investigation into the definition of workforce education at Spokane Community College. Ultimately a binary, paired with transfer education, workforce programs tend to be the “classic” or stereotypical programs offered at community and technical colleges. Working with college administration, including institutional research (IR), I was able to identify the list of divisions, departments, and programs that fall under the workforce. There are approximately 120 active programs that fall into this segment of academic offerings at SCC, across three divisions: Business, Hospitality/IT, Health/Environmental Science, and Technical Education. Organizational woes with this step in the process included a disconnect between the leadership or representative (usually Department Chair) for each program on record and in practice/reality. Additionally, many programs within the departments of the divisions had overlapping leadership, making a consistent, step by step approach to outreach with faculty leadership not as straightforward as I hoped.
Through a combination of cold calling and being connected via other faculty, I ultimately scheduled numerous meetings to discuss GenAI with workforce leads in numerous programs. I had a total of 16 meetings. Most were one-on-one, some were small group, and some were with entire departments. The typical format for these meetings was: introduction, disclaimer about the project versus GenAI generally at the college, and open discussion around the program’s interactions (or lack thereof) with GenAI through a workforce / employment context. I use the word “employment” because one of the concerns I identified early on was the relationship between skill development, resume building, and employer needs. Meetings were between 15 and 45 minutes, often with some relevant or unrelated follow-up due to the fast-growing reality of my identity as an “AI expert.” During meetings I took notes, and documented ideas and concerns as best as I could. The capturing of ideas was one of the more difficult aspects of this project, given the diversity of perspectives, positions, and information I was privy to. In some cases, I noticed that in meeting faculty for the first time, I had to both prove my legitimacy and my intentions around meeting, and occasionally trust was not fully established in the short time span of the meeting.
I will not include the full list of the workforce programs here, but the general disciplines and careers should be easily identifiable via this guide’s bibliography. Speaking of the bibliography, in addition to the meetings, my goal was to seek information, expertise, and additional perspectives from beyond community college’s faculty. I made an early decision to start by looking at academic research that I had immediate access to: through the SCC Library. I set up alerts in both our library system (Alma/Primo) and for at least two months only tracked what I was receiving through this “pipeline” of information. I used keyword combinations of “ChatGPT” and “GenAI,” noticing early on that “AI” and “Artificial Intelligence” more generally is being used outside of the GenAI context and has been in some industries for many years. Some searches were devoid of results, while many (notably Health Sciences) had an impossibly large selection of published research and editorials. Because of the gaps in published information, I ultimately made an effort to expand beyond the library’s subscriptions and move toward more publicly-available and popular sources. I used Google Alerts to do this, following the same keyword phrasing I used in the library system. It helped but did not resolve all the gaps. Additionally, I imposed upon myself another challenge: authority in popular sources. The majority of the sources being fed to me through Google Alerts were garbage, often for-profit companies with shallow articles that barely addressed the topic. In some cases, I noticed what looked like spun or generated content lacking any author and any significant motivation. But I did get access to trade publications, associations, and professional blogs that expanded the perspectives, opinions, and positions on GenAI in the workforce and impacting the workforce.
And what about researching workforce education and GenAI? The great challenge we are all facing: how to take what is identified as a need and teach to it? The reality is that literature in this area is incredibly absent. As my bibliography indicates, there were only a few resources that indicated the connection between higher ed teaching and workforce needs in the context of GenAI. This aligns with the absence of GenAI teaching in the workforce programs at SCC. In some cases, conversations yielded some relationships between faculty and employers, usually through advisory boards. In other cases, technology exposure was through vendors at conferences, often beyond the scope of the budget and capacity of the program. And in other cases, teaching was being evolved to respond to the literature but not necessarily specific workplaces or employers. Every single conversation with faculty yielded variability was extremely unique. As I explore on the following page, all the possible situations workforce programs and faculty may find themselves in are telling. Connecting this spectrum to my recommendations may alleviate some of the challenges and address unrealized academic evolution in the context of GenAI.
To close this page, let me acknowledge that though this project was immense, it also went by quickly. Across the two quarters, I put in close to 80 hours of documented work, with other adjacent but relevant tasks and work, including attending conference sessions and webinars and having informal conversations not being tracked. The work was often done outside of my “normal work hours” as a librarian, adding a challenging layer (or foundation) to my forays as an educator and one playing the long game with this difficult subject. My own learning (as opposed to organizing, if we can divide the two) was, at times, lacking due to exhaustion and a lack of capacity. Ideally, future projects would engage even more processing and critical deconstruction of the research that could yield some additional recommendations and forward thinking around the robust nature of GenAI.