Many students and parents are employment-focused, and depending on the country you teach in, at a public institution, your government may require you to show job market applicability. I teach in a professional program (communications), and we talk a lot in my department about the role of AI skills as students head into their internships. These aren’t easy questions, though, because industry expectations around AI are a moving target. 

What Counts as an AI Skill When the Workplace Keeps Changing? 

For example, we need to decide what counts as a workplace skill when it comes to GenAI. That is more nuanced than it seems. We are dealing with students who expect clear lessons, learning objectives, and assessments, as industry itself is still deciding what constitutes useful implementation of these tools. Chen, Srinivasan, and Zakerinia (2004) suggest that there are multiple, coexisting effects. GenAI can threaten employment when job tasks are clear-cut, but it can also make the jobs where workers collaborate with GenAI more complex and skilled. Business Insider is even tracking GenAI-related layoffs (Altchek et al. 2026). Also, work from OpenAI suggested that a large percentage of workers can expect to have parts of their jobs replaced by GenAI support (Eloundou et al. 2023). More recent work by Dehouche (2026) offers more specific results. His study of labor market data found that AI isn’t just changing jobs, it is replacing them. The threat is uneven—entry-level positions are at more risk, as are structured cognitive tasks like coding. Dehouche also suggests that this disruption may take a higher toll on the developing world.  

The Workplace Is Learning GenAI’s Limits 

It’s worth noting, though, that the OpenAI report is from 2023, Chen and colleagues were writing back in 2004, and even though Dehouche’s work was published this year, it uses historical data. GenAI is rapidly evolving, and its intersection with work is evolving as well. Looking at the news in summer 2026, it looks like some employers are pulling back on their initial enthusiasm. For example, Noam Scheiber in the New York Times writes that AI-produced documents create something of a problem for organizations (2026). It is not necessarily just because of pitfalls in the technology, but an interaction between the creation of work products and the employees who might have to vet those products when produced by GenAI. Scheiber writes “…most corporate users appear to be blissfully unaware of these issues, raising the possibility that A.I.’s promise of increased productivity and vast cost savings could be undermined.” Writing in the Independent, Anthony Cuthbertson tells the story of Ford Motor Company, which tried replacing senior engineers, unflatteringly called “gray beards,” with GenAI (2026). The company has been rehiring them after learning that the tool cost the company billions when it failed to anticipate quality issues. A column from Harvard Business Review discusses “workslop”, which is defined as when “individuals use AI to produce polished-seeming, low-quality work that ends up wasting people’s time and eroding trust among colleagues.” The column suggests that over time, established business processes start failing because people no longer feel they can trust information (Holweg and Davenport 2026). Capelouto and Jones report from New York Tech Week that the return on the investment in costly AI tokens is becoming a concern (2026).  

Should Universities Prepare Students for Jobs That May Disappear? 

A rapidly changing and disruptive technology presents many challenges, more so for those in fields that tend to have internships, co-ops, and other types of work-integrated learning. The immediate contact with employers exerts pressure towards immediate change. Professional programs have always struggled with balancing general education that foregrounds critical thinking and how to learn with specific technical concepts and processes that will be immediately useful in the workplace upon graduation, but obsolete with time.   

These pressures are even sharper with a technology that may be eliminating entry-level jobs. Is it the job of colleges and universities to train students to begin their careers at higher levels, or should we teach them to compete for a shrinking number of entry-level roles? If we do this, are there trade-offs with other parts of the curriculum? Is it ethical to train students to make themselves obsolete? And how might academic-industry partnership facilitate high-quality engaged learning that benefits students for their entire lives, both professional and otherwise? Academia and industry tend to work on different schedules—with the academy lagging (Salatino, Osborne, and Motta 2020)—and GenAI seems to introduce some particular challenges as answering these questions is slow, but also urgent.  

References 

Altchek , Ana, Kelsey Vlamis, Shubhangi Goel, Katherine (Tangalakis-Lippert) Ortiz, and Huileng Tan. 2026. “17 Companies That Have Cut Jobs or Restructured Their Workforces Because of AI.” Business Insider, July 23, 2026. https://www.businessinsider.com/list-companies-replacing-human-employees-with-ai-layoffs-workforce-reductions.  

Capelouto, J. D., and Rachyl Jones. 2026. “Companies Struggle to Measure AI’s ROI.” Semafor, June 5, 2026. Reproduced in Yahoo Finance. https://finance.yahoo.com/sectors/technology/articles/companies-struggle-measure-ais-roi-171020855.html.  

Chen, Wilbur Xinyuan, Suraj Srinivasan, and Saleh Zakerinia. ”Displacement or Complementarity? The Labor Market Impact of Generative AI.”  Harvard Business School Working Paper 25-039. December 2024. https://www.hbs.edu/faculty/Pages/item.aspx?num=67045.  

Cuthbertson, Anthony. 2026. “Ford Hired AI and Sacked Humans. It Backfired Badly.” The Independent, June 29, 2026. https://www.the-independent.com/tech/ford-ai-automation-humans-hiring-artificial-intelligence-b3004733.html.  

Dehouche N (2026) Creation, validation, obsolescence: observed evidence of AI-driven labor market displacement, 2020–2025. Front. Hum. Dyn. 8:1815037. https://doi.org/10.3389/fhumd.2026.1815037. 

Eloundou, Tyna, Sam Manning, Pamela Mishkin, and Daniel Rock. 2023. “GPTs Are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models.” arXiv, August 21, 2023. arXiv:2303.10130.  

Holweg, Matthias, and Thomas H. Davenport. 2026. “Don’t Let AI Slop Muck Up Your Company’s Processes.” Harvard Business Review, June 16, 2026. https://hbr.org/2026/06/dont-let-ai-slop-muck-up-your-companys-processes.  

Salatino, Angelo, Francesco Osborne, and Enrico Motta. 2020. “ResearchFlow: Understanding the Knowledge Flow between Academia and Industry.” In Proceedings of the 22nd International Conference on Knowledge Engineering and Knowledge Management, edited by C. Maria Keet and Michel Dumontier, 219–236. Lecture Notes in Computer Science 12387. Springer. https://doi.org/10.1007/978-3-030-61244-3_16.  

Scheiber, Noam. 2026. “We’re Only Starting to Grasp the Pitfalls of Using A.I. at Work.” New York Times, June 29, 2026. https://www.nytimes.com/2026/06/29/business/artificial-intelligence-workplace-consequences.html.  

About the Author   

Amanda Sturgill, associate professor of journalism, is the 2024-2026 CEL Scholar. Her work focuses on the intersection of artificial intelligence (AI) and engaged learning in higher education. Dr. Sturgill also previously contributed posts on global learning as a seminar leader for the 2015-2017 research seminar on Integrating Global Learning with the University Experience.   

How to Cite This Post  

Sturgill, Amanda. 2026. “GenAI’s Rapid Evolution Challenges University Instruction.” Center for Engaged Learning (blog). Elon University. August 25, 2026. https://www.centerforengagedlearning.org/genais-rapid-evolution-challenges-university-instruction.