Higher education under pressure from AI

An essay published on October 22, 2026, by The Conversation, written by Jeff Sparrow, a researcher and teacher at the University of Melbourne, argues that artificial intelligence is forcing higher education to reinvent itself. Sparrow claims that while AI is causing disruption, the problems in higher education were already present due to decades of treating degrees as products, students as customers, and research as performance metrics.

AI as a mirror of systemic issues

Sparrow's analysis aligns with recent studies involving students, teachers, and management teams, suggesting that AI is not the root cause of the crisis but rather a revealer of deeper systemic weaknesses. When AI can produce an acceptable essay in seconds, the issue is not just cheating but the value of the learning exercise itself.

Widespread use of AI tools

Students already use AI tools extensively, often without detection by teachers, while management teams struggle with inconsistent policies—sometimes banning AI, sometimes adopting it quickly. The challenge now is not whether to integrate AI, but how to do so without repeating the problems the essay describes.

French context adds urgency

The situation is especially urgent in France, where demographic decline is expected to reduce the number of students, and recent reforms have cut funding for many educational institutions. These pressures push universities toward a race for enrollment and cost control, risking the use of AI as a cost-saving measure rather than a learning tool.

Misunderstanding AI's role

The fundamental mistake, Sparrow argues, is to treat AI as a technological issue rather than a systemic one. Universities buy licenses, sign partnerships with major tech companies, and create usage guidelines, yet fail to see real changes in classrooms. The University of Sydney's recent multi-year agreement with OpenAI, signed just days after a staff strike, highlights this gap between strategy and practice.

Four transformations needed

The integration of AI into higher education must be considered holistically, involving four interconnected transformations: technological, pedagogical, educational practices, and evaluation. The technological transformation involves adapting to new learning environments. The pedagogical one requires shifting from teaching to designing meaningful learning experiences.

Interconnected challenges

None of these transformations can be done in isolation. Changing technology without rethinking pedagogy leads to passive learning. Updating pedagogy without changing evaluation creates confusion. Updating evaluation without supporting teachers leads to exhaustion. Most institutions lack a clear plan to address these challenges.

Quality of learning matters

Research shows that AI itself does not enhance student engagement—it is the quality of the learning experience that matters. When AI is thoughtfully integrated into a well-designed pedagogical approach, cognitive engagement improves. When it is simply made available without context, it can lead to dependence and shallow learning.

Rebuilding meaningful learning

Sparrow notes that teachers least affected by AI are those who lead small, passionate groups in their fields. This suggests that students delegate tasks they find unengaging. The solution, therefore, is not to fight AI but to create learning experiences that make intellectual effort valuable again. Higher education must seize this opportunity to rethink learning, teaching, and evaluation.