There is a question that Eduardo Liberos, CAIO of IEDGE AI Business School, has been asking himself for some time: are we using AI to learn more, or to strive less? next+ spoke with Eduardo about the impact that artificial intelligence is generating on graduate programs, from student learning and the role of the professor to the way content is assessed. This new scenario forces specialized higher education institutions to adapt to one of the most disruptive changes occurring since the Industrial Revolution.
AI is not a tool, it is a transversal competence
In conversation with next+, Eduardo Liberos indicated that transformation occurs on three fronts simultaneously: how we learn, how we teach, and how knowledge is applied in the professional environment. In all three, the starting point is the same: integrating AI as a structural part of the educational model, not as an additional layer over what already existed.
The real value is not in using AI as a trendy tool, but in integrating it as a transversal competence. Today, a professional must know how to use it to think better, decide better, and execute more efficiently.
The distinction made in the conversation is precise: using AI to generate a deliverable is one thing. Using it to explore hypotheses, compare approaches, and detect weak points in an idea is something completely different. The difference does not depend on the tool, but on who uses it and with what criteria.
The professor does not disappear, he becomes harder to replace
One of the topics addressed was the future of the teaching role. Eduardo Liberos was clear: the professor does not disappear, but his function evolves significantly.
The professor does not disappear; he becomes more important. But his role changes. He shifts from being mainly a transmitter of information to becoming a guide, curator, mentor, and critical evaluator. In an environment with AI, the professor must teach how to think with AI, but not to delegate the thinking to AI. That difference is fundamental.
This evolution has direct consequences on how evaluation is conducted. The approach of IEDGE to evaluation was discussed in a context where deliverables can be generated with the support of AI. Eduardo Liberos explained that the institution directs its methods towards the process, the criteria, and the defense of the work: applied projects, presentations, oral defense, live case analysis, and assessment of strategic capability. The goal is to verify whether the student understands what they have produced, can justify their decisions, and is capable of defending them against critical questions.
Learn, unlearn, relearn
The conversation expanded towards the role of education in the face of the changes happening in the global labor market. Eduardo Liberos pointed out that education must evolve from being a one-time event to becoming a system of continuous updating.
We are no longer talking about studying once and working for a lifetime with that knowledge. We are talking about learning, unlearning, and relearning constantly. And programs that are not designed for that cycle will become obsolete before the professions they claim to train.
It was also raised whether professionals should prioritize strategic thinking programs or technical skills programs. Eduardo Liberos noted that this is a complementary choice, not exclusive. A professional with strategic vision but without technological understanding may remain in general approaches. One with technical mastery but lacking strategic judgment may execute without direction. The most valuable programs, he pointed out, are those that combine both dimensions along with ethics and real practical application.
Conclusion
At the close, there was a discussion about the long-term prospects of AI in graduate education. Eduardo Liberos pointed to a horizon where technology and pedagogy must advance together: the trivialization of learning, the homogenization of thought, and the dependence on private platforms are scenarios that can be avoided with vision, method, and institutional responsibility. For Eduardo Liberos, the future of graduate education is not simply more AI, but better education with AI. More critical, more practical, more ethical, and more connected to the real challenges of the professional world.