There are different origins and interpretations of what liberal education means, but a favorite of mine is the idea that a liberal education liberates. You gain the ability to think critically and to know how to learn, which liberates you to learn other things, enabling you to act in line with your values. Generative AI (GenAI) tools have a curious place in that ideal. 

This post is inspired by a recent article about how, at one company, coding has started to feel like warehouse work. According to the article, the company’s introduction of AI-supported tools has made some developers feel like their work is less creative and more rushed. In an age where widely available support for many kinds of intellectual work is getting less expensive, it raises the question of whether human tasks will still have meaning.  

AI and Educational Autonomy 

I see a few parallels between what the developers are saying and higher education. Elon’s Imagining the Digital Future Center, in collaboration with the American Association of Colleges and Universities, released a recent report of a faculty survey about AI, which, among other things, found that 86 percent of respondents believed GenAI tools would impact their own work as instructors. The instruction role at the AI-fueled Alpha schools looms large.  

Called “Guides” instead of teachers, faculty are selected for their ability to motivate students to complete AI-tutor lessons and for their ability to plan “engaging life skills workshops.” Students do academic learning with an AI app for two hours a day and participate in workshops on topics like personal finance and storytelling for the rest of the day. The university faculty role would probably not go that far, but there are already models like highly online universities where curriculum developers plan classes and instructors have little autonomy when running them.  

For instructors, these kinds of concerns intersect with their roles as academic employees. The old joke goes that faculty have a lot of autonomy—they can work any 60 hours a week they wish. Kidding aside, as a faculty member, I do really appreciate the autonomy and the opportunities for creativity, as I not just meet learning objectives but try to help my learners feel the same passion for my field that I have. That task is different every semester, and adjusting to those differences requires me to innovate in some truly fun ways. If I must incorporate AI support, and doing so makes my job more routine, I risk losing something I value.  

AI’s Effects on Worker Motivation 

Academia is not alone in this. Scholars and professional researchers who study work have found that AI tools can change people’s motivation and autonomy in some conflicting ways. As a positive, if AI automates routine tasks, workers can use the extra time and cognitive resources in other ways (Kong et al. 2024). A study from Microsoft and LinkedIn research found that employees they categorized as “power users” said AI helps them manage large workloads, and this, in turn, helps them be creative at work.  

Conversely, when using AI systems reduces choices or determines how work must be done, workers feel less autonomy and perceive their work as less meaningful (Faas et al. 2024). Employers can also use AI tools to monitor worker behaviors, which creates issues that include a loss of job satisfaction (Rieder 2025). Ultimately, to keep humans engaged at work, AI must be implemented as a tool that scaffolds rather than replaces human agency and self-regulatory processes (Babayev 2025). 

Student Satisfaction 

A lingering question, then, is whether these effects also extend to learners in higher education. Research on this is still emerging, but some scholars have found similar conflicting effects. Some studies suggest that when adaptive AI tutors are used to teach concepts, the ability to get rapid feedback and to iterate until mastery offers a boost, enhancing the learner’s senses of competence and autonomy. But if those tools are perceived as taking away choices, it may reduce the student’s sense of agency. (Pawar and Rai 2023; Watanabe 2024).  

If learners rely on GenAI to do work for them, it can induce “metacognitive laziness,” where students prioritize technological convenience over the struggle needed for critical thinking, eventually diminishing their intrinsic interest and sense of ownership (Fan et al. 2024; James and Andrews 2024). Furthermore, transitioning from AI-supported collaboration back to solo work can lead to a measurable surge in boredom and a sharp drop in motivation, suggesting that the presence of AI may erode the human worker’s perceived sense of control over their own learning journey (Wu et al. 2025; Babayev 2025). When the joy is removed from the equation, the goal can be to just get through what’s required, so you can go find that joy elsewhere.  

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References 

Alpha School. n.d. “Frequently Asked Questions.” https://alpha.school/faq/. 

Babayev, Javid. 2025. “Algorithmic Autonomy or Dependence? A Mixed-Methods Study on AI Personalization and Self-Regulated Learning in Higher Education.” Journal of Azerbaijan Language and Education Studies. 2 (4): 32-39. https://doi.org/10.69760/jales.2025004002. 

Babayev, Javid. 2025. “Algorithmic Autonomy or Dependence? A Mixed-Methods Study on AI Personalization and Self-Regulated Learning in Higher Education.”  Journal of Azerbaijan Language and Education Studies 2 (4): 32-39. https://doi.org/10.69760/jales.2025004002. 

Faas, Cedric, Richard Bergs, Sarah Sterz, Markus Langer, and Anna Maria Feit. 2024. “Give Me a Choice: The Consequences of Restricting Choices Through AI-Support for Perceived Autonomy, Motivational Variables, and Decision Performance.” arxiv. https://doi.org/10.48550/arXiv.2410.07728. 

Fan Yizhou, Luzhen Tang, Huixiao Le, et al. 2024. “Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance.” British Journal of Educational Technology. 56 (2): 489-530. https://doi.org/10.1111/bjet.13544. 

James, Trixie, and Grant Andrews. 2024. “Levelling the Playing Field Through GenAI: Harnessing Artificial Intelligence to Bridge Educational Gaps for Equity and DisadvantagedStudents.” Widening Participation and Lifelong Learning. 26 (3): 250-260. https://doi.org/10.5456/WPLL.26.3.250. 

Kong, Weijuan, Yanhua Ning, Ting Ma, et al. 2024. “Experience of Undergraduate Nursing Students Participating in Artificial Intelligence + Project Task Driven Learning at DifferentStages: A Qualitative Study.” BMC Nursing. 23 (314). https://doi.org/10.1186/s12912-024-01982-1.  

Pawar, Meera, and Ram Das Rai. 2023.“Ethical Challenges of Artificial Intelligence in Education: Achieving Learner Centricity With Respect to Learner Autonomy.” International Conference on Computers in Education. https://doi.org/10.58459/icce.2023.1437. 

Reider, Emanuel. 2025. “Strategic Alignment and AI Adoption: How Organizational Factors Shape Perceived Productivity.” International Journal of Management and Accounting. 7 (4): 184-192. doi: 10.34104/ijma.025.01840192. 

Sainato, Michael. 2024. “‘You feel like you’re in prison’: workers claim Amazon’s surveillance violates labor law.” The Guardian. May 21. https://www.theguardian.com/us-news/article/2024/may/21/amazon-surveillance-lawsuit-union. 

Scheiber, Noam. 2025. “At Amazon, Some Coders Say Their Jobs Have Begun to Resemble Warehouse Work.” New York Times. May 27. https://www.nytimes.com/2025/05/25/business/amazon-ai-coders.html?unlocked_article_code=1.JVA.slhz.nojQw2XBEXwZ&smid=url-share. 

Watanabe, Alice. 2024.“Have Courage to Use your Own Mind, with or without AI: The Relevance of Kant’s Enlightenment to Higher Education in the Age of Artificial Intelligence.” The Electronic Journal of e-Learning, 22 (2): 46-58. https://doi.org/10.34190/ejel.21.5.3229. 

Watson, C. Edward, and Lee Rainie. 2026. “The AI Challenge: How College Faculty Assess the Present and Future of Higher Education in the Age of AI.” American Association of Colleges and Universities and Elon University’s Imagining the Digital Future Center. https://imaginingthedigitalfuture.org/wp-content/uploads/2026/01/Elon-AACU-faculty-AI-survey-full-report-1-21-26.pdf. 

Wu Suqing, Yukun Liu, Mengqi Ruan, Siyu Chen, and Xiao-Yun Xie. 2025. “Human-generative AI collaboration enhances task performance but undermines human’s intrinsic motivation.” Scientific Reports. 15. https://doi.org/10.1038/s41598-025-98385-2. 

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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. “Might AI Assistance Take the Joy Out of Learning?” Center for Engaged Learning (blog). Elon University. February 20, 2026. https://www.centerforengagedlearning.org/might-ai-assistance-take-the-joy-out-of-learning/.