AI in higher education and the ‘erosion’ of learning
Summary
Prof. Nir Eisikovits and Jacob Burley explore the implications of AI in higher education, focusing on its potential to transform learning and the ethical concerns surrounding cognitive offloading.
Why It Matters
This article highlights the critical role AI is playing in reshaping higher education, moving beyond concerns about cheating to address broader ethical implications. Understanding these changes is essential for educators, administrators, and policymakers to navigate the future of learning effectively.
Key Takeaways
- AI is increasingly integrated into various aspects of higher education, from admissions to academic advising.
- The ethical implications of AI use extend beyond cheating, raising questions about privacy, bias, and the role of mentorship.
- Understanding the different types of AI systems—nonautonomous and hybrid—is crucial for addressing their impacts on learning.
MACHINES AI in higher education and the ‘erosion’ of learning 27 Feb 2026 Save article Image: © Jakub Krechowicz/Stock.adobe.com Prof Nir Eisikovits and Jacob Burley of the University of Massachusetts Boston discuss the ethics of AI in higher education and the technology’s role in ‘cognitive offloading’. A version of this article was originally published by The Conversation (CC BY-ND 4.0) Public debate about artificial intelligence in higher education has largely orbited a familiar worry: cheating. Will students use chatbots to write essays? Can instructors tell? Should universities ban the tech? Embrace it? These concerns are understandable. But focusing so much on cheating misses the larger transformation already underway, one that extends far beyond student misconduct and even the classroom. Universities are adopting AI across many areas of institutional life. Some uses are largely invisible, like systems that help allocate resources, flag ‘at-risk’ students, optimise course scheduling or automate routine administrative decisions. Other uses are more noticeable. Students use AI tools to summarise and study, instructors use them to build assignments and syllabuses, and researchers use them to write code, scan literature and compress hours of tedious work into minutes. People may use AI to cheat or skip out on work assignments. But the many uses of AI in higher education, and the changes they portend, beg a much deeper question: As machines become more capable of doing th...