Although I still have more to say about workloads in higher education, I managed to stay off my soapbox this week, so let's jump right in. First, we dig into an article by Kevin McClure, professor of higher education and department chair at the University of North Carolina at Wilmington, who has done extensive research on the higher education workplace. From there, we look at how research is increasingly pointing to the impact AI is having and will have on the labor market.  

After reading today’s issue, share your thoughts about higher education employee workloads in the comments! 

Too Much Work 

From Higher Ed Has a Workload Problem | The Chronicle of Higher Education 

Kevin McClure argues that for many higher education employees, workloads have become unreasonable.  

Our Thoughts  

This article hits close to home. I spent roughly 20 years working at higher education institutions before joining Evisions, and the pattern it describes is not just familiar; it is the dominant experience of most people I worked alongside. Early in my career, I was more than happy to take on additional responsibilities. I wanted the experience; I wanted the exposure, and I believed that saying yes was how you built a career in higher education. The problem is that once you absorb that extra work, it is nearly impossible to hand it back. The tasks quietly become yours, the institutional memory about how they got assigned to you fades, and the next new responsibility lands on top of them rather than replacing them. I watched colleagues do the same thing, and I watched many of them burn out because of it. 

One concept that I think is worth adding to the conversation is what librarian and scholar Fobazi Ettarh calls vocational awe. Ettarh developed the idea in the context of librarianship, but it applies almost perfectly to higher education more broadly. The argument is that when workers in a field view their work as inherently sacred or noble, they become vulnerable to exploitation because questioning workload, compensation, or boundaries feels like questioning the mission itself. Higher education is full of people who got into this work because they believe in students, believe in learning, and believe in the institution. That belief is essential to our work, but it also makes us susceptible to the "do more with less" framing in a way that workers in other industries might push back on more quickly. When the work is framed as a calling, saying "this is too much" can feel like personal failure rather than a reasonable response to an unreasonable workload. 

What I appreciate most about this article is that McClure does not stop at naming the problem. He offers concrete practices that individual leaders can adopt. Starting with trust, practicing subtraction, using the supervisory shield carefully, being cautious about automation as a cure-all, and advocating with specific data are all things a manager can do tomorrow without waiting for institutional permission. None of them solve the underlying structural issue, which is that higher education has grown its commitments without growing its capacity, but they give leaders tools to protect their teams in the meantime. I particularly liked the example of the teaching and learning center director who had her staff log their work in 15-minute increments for a month. It is the kind of exercise that feels excessive until you realize that nothing else will convince a skeptical administrator that the workload is real.  

The bigger concern sitting underneath all of this is what it means for the future of the higher education workforce. The sector is already struggling with talent pipelines in academic advising, student affairs, financial aid, IT, and dozens of other areas. A 2025 CUPA-HR survey found that almost half of higher education employees were at least somewhat likely to look for a new job in the coming year, with workload and compensation topping the list of reasons. If higher education wants to attract and retain the next generation of scholars, researchers, and professionals, it has to make peace with the fact that those workers are watching how we treat the current generation. They are watching the burnout, the unfilled positions, the expectations that one person can do the work of two, and they are drawing conclusions. Fixing the workload problem is not just about protecting the people who are here today. It is about whether anyone will be willing to take the job five or ten years from now. 

AI Risks for the Labor Market 

From Which Jobs Are Most at Risk in the Age of AI? | Inside Higher Ed 

A growing number of researchers worry that AI is beginning to disrupt the job market. 

Our Thoughts  

The projections in this article are the kind that make you want to look away, but we can't. Whether or not you believe the most extreme forecasts (and I don't), the data is getting more consistent across research groups, and more importantly, students and families are reading these headlines. Every prospective student sitting across from an admissions counselor has probably seen at least one article telling them that their intended major is about to be automated out of existence. Every parent writing a tuition check is asking themselves whether this is still a sound investment. Even if the underlying research is still evolving, the perception is already shaping enrollment decisions, and higher education has to engage with that reality rather than wait for the numbers to settle. 
 
To be clear, I do not believe all white-collar work will disappear in the next 18 months. That is a tech sector sales pitch dressed up as a forecast, and the people making those claims have a financial incentive to sound certain. What the more measured research is showing, including the Tufts report cited in this article, is that the impact is concentrated at the entry level. Writing, coding, data analysis, basic research, and document preparation are the kinds of tasks that large language models handle reasonably well, and those tasks also happen to be what entry-level employees do in their first few years on the job. That matches what the Burning Glass Institute has been reporting about the erosion of the entry-level job market. 

Here is where my rational brain starts asking an uncomfortable question, though. If companies displace entry-level workers with AI, who is going to be available to fill their mid-level roles in five or ten years when their current mid-level people retire or move up? Entry-level jobs are not just output. They are training grounds. They are how people learn the organization, develop judgment, build relationships with colleagues and customers, and figure out how the work actually gets done. You cannot skip that phase and expect to have a bench of experienced professionals waiting in the wings. Companies eliminating entry-level positions today are making a workforce decision with consequences that will not show up on a balance sheet for years, and by the time they do, it will be very hard to rebuild the pipeline. I am not sure most organizations have fully thought that through. 

None of this means higher education should sit on the sidelines waiting for clarity. Tiffany Hsieh's comment in the article is exactly right. Our systems are not built to move quickly, which means we need to start moving now on the things we can actually influence. That includes being honest with students about how their fields are changing, building AI literacy into curricula across disciplines, creating meaningful experiential learning opportunities that cannot be replicated by a chatbot, and helping students develop the judgment and adaptability that will matter regardless of how the technology evolves. We may not be able to predict exactly which jobs will exist in 2030, but we can prepare students to be the kind of workers who can navigate uncertainty once they get there. That is a role higher education has always played, and it is one of the few areas where our value proposition actually strengthens in an AI-disrupted economy rather weakens.  

Sparks 

• State Funding per Student Drops for First Time Since 2012 (Inside Higher Ed) - According to a new SHEEO report, inflation-adjusted, per-student state and local higher education spending declined by 1 percent last year. Not the best news for public higher education right now. 
• NASH and National Student Clearinghouse Partner for Groundbreaking Academic and Labor Market Analytics (NASH) - A new pilot aims to directly connect labor market data with institutional enrollment and completion data. Two non-profits working to join this data in a way that makes sense for the public and helps institutions better understand the outcomes of their academic programs. Sounds like a great start toward answering questions about postsecondary value. 
• The quest to build a better AI tutor (The Hechinger Report) - Researchers are attempting to determine how to use AI to build a better personalized tutor. Interesting idea, but one that has been tried multiple times before.