Why the greatest risk of AI in higher education is the erosion of learning

Fast Company Tech

by The Conversation

February 22, 2026

AI-Generated Deep Dive Summary

The integration of artificial intelligence (AI) into higher education is transforming university life in ways that extend far beyond concerns about cheating. While debates often center on whether students will use AI tools like chatbots to write essays or if universities should ban the technology, the article highlights a broader shift: AI is becoming deeply embedded in institutional operations and academic workflows, from admissions and resource allocation to teaching and research. This transformation raises critical questions about the future of learning, mentorship, and the university's purpose. AI systems are categorized into nonautonomous, hybrid, and autonomous types, each with distinct impacts on education. Nonautonomous AI automates routine tasks like scheduling or risk assessment but keeps humans "in the loop." Hybrid AI, often using generative models, assists with teaching and learning by generating feedback, designing syllabi, or summarizing papers. While these tools can enhance productivity, they also raise ethical concerns about transparency, accountability, and intellectual ownership. For example, students may struggle to distinguish between human and machine-generated feedback, undermining trust in the learning process. The potential for AI to hollow out the core educational ecosystem is a significant risk. Autonomous systems could replace routine academic tasks, potentially eroding opportunities for hands-on learning, mentorship, and skill development—processes that are essential for building expertise and judgment. Cognitive offloading, where users rely on AI to handle tasks like writing or problem-solving, risks displacing the very struggles and revisions that foster deep understanding. This could weaken the foundation of higher education as a system designed to培养expertise and cultivate human capacities. As AI becomes more autonomous and capable of performing knowledge work, universities must grapple with fundamental questions about their purpose. Should they prioritize efficiency and output over the integrity of the learning process? The article argues that universities should preserve their role as ecosystems for forming expertise, mentorship, and productive struggle. By doing so, higher education can adapt to the AI era while maintaining its core value: fostering human development through meaningful engagement with knowledge.

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Originally published on Fast Company Tech on 2/22/2026