Back during the pandemic, instructors were all sent home to teach online. Many had no training or experience with this modality. As I realized I needed to accommodate students who had spread to multiple time zones and home responsibilities, asynchronous learning videos seemed like a great option. I was not alone in this thought process. Fortunately, I do have rudimentary video editing skills because of my field, but many of my colleagues around the country were not very comfortable with editing video. Finding existing, well-done videos that explained the process or information being taught seemed like a great option. The instructor, as the content expert, could curate the very best information and share it in a way that was easy for learners to access. Unfortunately, this landed badly with some students. There were many reports at the time of university administrators being besieged by students and their parents, claiming that the faculty had stopped teaching. Some even tried to sue for a rebate of their tuition (Brown 2020).  

AI Uses—and Flaws

Assessing the affordances of GenAI tools to create teaching materials seems like a similar decision point. Who wouldn’t want a podcast version of a lecture* that students could listen to for review that you don’t have to record and edit? Perhaps a snazzy infographic explaining a concept that the faculty member didn’t have to master a design tool to create (see figure 1). Maybe differentiated versions of assignment instructions or variations on a theme for test questions that are made seemingly automatically? What do learners think? A 2025 study from Inside Higher Ed found that students have widely varied feelings about faculty GenAI use, with about 40 percent thinking it’s OK and about 40 percent thinking it’s harmful (Flaherty 2025).

There’s one area where student opinion is clearer: grading. Some of this is because faculty using AI to grade the same assessments that students are not allowed to use AI to create feels hypocritical. Some is because of automation bias (Thomas,  Yildirim-Erbasli, and Hariharan 2025). Students may also feel that GenAI feedback is generic and not targeted to them and their issues, or even that using GenAI reflects laziness on the part of the instructor, which is unacceptable given the high cost of higher education.  

* The link here will take you to a GenAI-created podcast based on the content of this blog post. It was trivial for me to generate, but as you might imagine in a 20-minute podcast based on a 750-word article, there’s plenty of manufactured information. As an example, in the first minute it states that the pre-recorded videos were YouTube videos, which is not contained in this post. The audio file was originally created in Gemini Notebook, uploaded to YouTube for sharing purposes.

AI Use’s Effect on Learning and Teaching

At the same time, it is important to consider the effect on student learning. An important new report out from the University of Pennsylvania offers results from an experiment designed to look at the adequacy of GenAI as a teaching support. Sungu, Lira, and Duckworth (2026) note that GenAI offers multiple possible outcomes. The tools could expand instructor capacity, or they could encourage the same cognitive offloading that has been found for students and professionals. If it’s the latter, the result could be lower-quality materials because they are not as bespoke for a particular group of learners. The researchers report on a study was conducted in K–12. There was a large participant pool of twenty-four institutions and 4,500 students in Turkey, and some random assignment (by school and department) was possible. Three groups were studied: a control with no intervention, a second GenAI access group that had access to a specially developed tool for material design, and a third access plus reminder group, which received weekly email reminders that reported the instructor’s GenAI use score to encourage use.  

Student motivation/confidence, test scores, teacher usage, and teacher attitudes were outcome variables. The findings encourage caution. Overall, researchers found that when instructors use GenAI, it was negatively associated with student motivation. Teacher use of GenAI didn’t affect student performance on tests across all students, but the authors found that when they split the sample between low- and high-performing teachers, the lower-performing teachers’ use of GenAI was associated with students performing worse. The effect on teacher attitudes about GenAI was also not consistent. For teachers who already used it heavily before the study, they became more pessimistic during the study period. For those who previously used it lightly, their attitude became more positive.  

Takeaways

Although that study was conducted in K–12, there are lessons that apply in higher education, as well.  

• GenAI is not a neutral tool. If it can impact learner motivations, instructors should take care to motivate that use for the students.  
• Instructors should not consider themselves immune to the negative effects of GenAI in terms of cognitive offloading. Although experts can use the tool to good effect, we need to be honest about how expert we actually are in order to avoid unintended consequences.  
• Decisions to use GenAI as an instructional aid can have consequences for teachers and students alike. Honest communication can benefit both parties here.  

References

Brown, Maggie. 2020. “ECU Student Sues UNC System Over Switch to Online Classes.” WRAL News. https://www.wral.com/archive/19075083.

Flaherty, Colleen. 2025. “How AI Is Changing—Not ‘Killing’—College.” Inside Higher Ed. https://www.insidehighered.com/news/students/academics/2025/08/29/survey-college-students-views-ai.

Riahi, Bahare, Viktoriia Storozhevykh, and Veronica Cateté. 2026. “Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency.” arXiv. https://arxiv.org/html/2602.07754v2.

Sungu, Alp, Benjamin Lira, and Angela Duckworth. 2026. “Generative AI Can Harm Teaching.” SSRN. https://ssrn.com/abstract=7007339.

Thomas, Mackenzie L., Seyma N. Yildirim-Erbasli, and Shruthi Hariharan. 2025. “Exploring Undergraduate Students’ Perceptions of AI vs. Human Scoring and Feedback.” The Internet and Higher Education 68. https://doi.org/10.1016/j.iheduc.2025.101052.

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 Can Make Some Tasks Easier. But At What Cost?” Center for Engaged Learning (blog). Elon University. August 4, 2026. https://www.centerforengagedlearning.org/genai-can-make-some-tasks-easier-but-at-what-cost.