The Executive Technology Board has been considering the necessary evolution of education in a world influenced by AI. Many traditional methods are no longer adequate: Higher education was initially designed for a time when information was scarce, expertise was contained in libraries, and resources for exploration and utilization were limited. Today, knowledge is readily available, and AI is not just another tool—it is redefining the concept of work itself.
The rate of transformation is remarkable. Conventional curricula are becoming outdated more rapidly than ever before. Skills that were once foundational for careers now become obsolete within a few years, sometimes even months. AI is not only changing existing job roles but also giving rise to completely new job categories while making others redundant. The question is not whether education needs to change, but how swiftly it can adapt.
During a recent event in London, we had the honor of hosting the dean and associate dean from Northeastern University’s computer sciences department. The conversation was enlightening—NU’s initiatives provide insight into how education can be reinvented for this modern era. Their viewpoint highlighted a crucial truth: preparing students for the future demands moving beyond small updates to curricula and towards a complete reimagining of the essence of education itself.
From our discussions and broader contemplations at our global technology think tank, three key imperatives emerge:
1. Transitioning from educational centers to learning facilitators. Universities must no longer see themselves merely as knowledge repositories but as promoters of continuous learning. This involves establishing flexible systems and cultures capable of swiftly incorporating new knowledge, tools, and practices as industries evolve. Success for students will no longer hinge on rote memorization but on their ability to adapt, unlearn, and relearn.
2. AI as an interdisciplinary cornerstone. One of the most promising aspects of AI lies in its intersections with various fields: healthcare and AI, design and AI, sustainability and AI, law and AI. Future innovation will stem less from isolated expertise and more from interdisciplinary collaboration. Universities must integrate AI literacy across disciplines—not just in computer science programs but also in social sciences, arts, and professional schools. Students across diverse fields need a functional understanding of AI since it will shape their domains as much as it will shape technology itself.
3. Infusing real-world experience into the curriculum. Learning should not be confined to classrooms alone. The most effective educational models blend theory with practical experience. Northeastern’s co-op program serves as a prime example: students alternate between classroom studies and full-time industry roles, graduating not just with degrees but also with substantial hands-on practice. This form of integration is no longer a choice—it is essential in an era where employers expect immediate contributions from graduates and where technologies evolve too rapidly for classroom learning alone to suffice.
Perhaps the most significant shift we must embrace is that education does not conclude at graduation anymore. In the era of AI, every professional must continually update their skills, adapt to new tools, and reinvent themselves throughout their careers. Universities, industry players, and policymakers must collaborate to establish a genuine ecosystem for lifelong learning. Micro-credentials, modular certifications, and ongoing access to new learning pathways will become standard practices.
This challenge extends beyond academia; it is a shared responsibility. Businesses should invest in workforce development initiatives; policymakers should create frameworks supporting large-scale reskilling efforts; universities should revamp their models; learners themselves must take charge of their ongoing growth. The future of education will revolve not around instructing students what to learn but around equipping them with the skills to learn continuously, flexibly, across various disciplines.