UMVA has learned that the rise of artificial intelligence (AI) is forcing universities to confront a fundamental question: how do they teach students to think critically when machines can increasingly perform many of the tasks once used to develop those skills?
Rafif Srour, dean of programs at a leading school in Madrid, Spain, believes the answer is not to resist AI, but to ensure that universities do not abandon the cognitive exercises that help students build analytical judgment, problem-solving skills, and independent thinking. She warns that overreliance on AI could deprive students of opportunities to develop essential intellectual skills.
Universities worldwide are racing to integrate generative AI into classrooms, research, and administrative functions. While the technology can improve productivity and learning outcomes, Srour cautions that it should not replace the mental process required to arrive at a certain outcome. The concern is not whether AI can produce the outcome, but whether students still undergo the cognitive process required to get there.
An example of this is summarizing academic papers. For generations, students and researchers spent hours reading studies, identifying key information, and synthesizing insights into concise summaries. Today, large language models can perform the same task in seconds. However, Srour emphasizes that the exercise teaches students how to distinguish important information from noise, evaluate sources, and communicate ideas clearly.
If a generation never learns how to make a summary in their life, the impact on their cognitive development would be tremendous. The challenge extends beyond writing and research, as AI has become increasingly capable of performing analytical tasks that were once considered core components of university training.
Programs such as data science traditionally teach students how to identify patterns, interpret data, and support decision-making. Today, many of those functions can be executed by advanced AI systems within seconds. However, this does not make university education obsolete; instead, it requires institutions to rethink how they teach and what skills they prioritize.
Srour suggests that schools should take from traditional education the depth, the technical core, the foundation, and add different layers on top of this traditional education to prepare students for the future of work. The schools most likely to succeed will be those that update their curricula while preserving the intellectual rigor that higher education has traditionally provided.
Meanwhile, educational institutions that fail to evolve risk losing relevance. If institutions keep on resisting this and keep on teaching the same way that they’ve been teaching for the past 20, 50, or 100 years, what kind of minds are they developing?
The rapid pace of AI development is creating challenges not only for universities but also for governments and regulatory institutions. For the first time, it’s changing at a rate that is much faster than our ability to cope as a society. Educational systems, legal frameworks, and public institutions are all struggling to keep pace with technological change.
For emerging economies, broad technology literacy should become a national priority. Governments should create safe and responsible environments where students, researchers, and entrepreneurs can experiment with emerging technologies. The future of education will depend on whether institutions can strike a balance between embracing AI and preserving the human capabilities that machines cannot fully replicate.
As AI becomes more embedded in classrooms and workplaces, universities may ultimately be judged not by how quickly they adopt the technology, but by whether they continue producing graduates capable of exercising judgment, creativity, and independent thought in a world increasingly shaped by intelligent machines.
