Editor’s note: This article is adapted from a conversation with Jennie Sanders on the Illumination Podcast. To hear the full discussion, listen to the episode here.
Artificial intelligence isn't changing the purpose of education—it's changing the speed at which institutions must adapt. As technology reshapes the skills learners need and employers expect, curriculum can no longer evolve on traditional timelines. Institutions have an opportunity to rethink how programs are designed, delivered and continuously improved while ensuring graduates leave with both technical expertise and the human capabilities that will always matter.
For decades, curriculum has followed predictable review cycles. Programs are updated every three, five or even seven years through thoughtful governance and faculty collaboration. But today's workforce is evolving much faster than those timelines allow. New technologies emerge overnight, employer expectations shift rapidly and the skills students need continue to change.
That doesn't mean institutions should abandon careful curriculum design. Instead, they have an opportunity to rethink how curriculum evolves. Artificial intelligence can accelerate research, identify emerging trends and analyze curriculum more efficiently, giving faculty more time to focus on what they do best: applying their expertise to create meaningful learning experiences. Rather than replacing academic judgment, AI has the potential to strengthen it.
The institutions that will succeed in this next era won't chase technology for technology's sake. They'll stay focused on outcomes.
Preparing learners has always been the mission of higher education. AI doesn't change that mission—it changes how institutions fulfill it. By keeping learner outcomes at the center, colleges and universities can avoid becoming distracted by the technology itself and instead use it intentionally to improve teaching, learning and curriculum development.
That mindset also changes how institutions should think about teaching AI.
Rather than treating AI as a standalone topic, it should become part of the learning experience across disciplines. Just as writing is a foundational skill that looks different in engineering than it does in communications, AI should be woven into programs in ways that reflect each profession's unique tools, workflows and expectations.
Students studying healthcare, business, education or software engineering won't use AI in the same way. While foundational knowledge around responsible AI use and ethics is important, learners also need experience with the discipline-specific technologies they'll encounter throughout their careers.
At the same time, the rise of AI makes something else even more valuable: the skills only humans can provide.
Critical thinking. Communication. Collaboration. Creativity. Problem solving.
Research continues to show these are among the capabilities employers value most. AI can generate information quickly, but it cannot replace human judgment, empathy or the ability to navigate complexity with others. These aren't secondary skills—they're becoming the competitive advantage graduates will carry into every profession.
Ironically, technology also creates a new challenge. As AI removes more routine tasks, institutions must become increasingly intentional about creating opportunities for students to practice these distinctly human abilities.
Consider how the automobile transformed daily life. Cars made transportation faster and more convenient, but they also quietly removed much of the movement people once built naturally into their day. Today, staying physically healthy requires intentional effort.
Learning may be entering a similar moment.
As AI takes over repetitive tasks, institutions should ask what forms of intellectual and interpersonal "exercise" students might lose if they're not deliberately designed into the learning experience. Productive struggle—working through difficult problems, collaborating with classmates, navigating uncertainty and learning through iteration—isn't something to eliminate. It's fundamental to learning.
This distinction between productive and unproductive friction may become one of the defining characteristics of successful institutions.
Administrative tasks, repetitive processes and manual curriculum reviews are forms of friction AI can reduce, creating valuable efficiencies for faculty and staff. But learning itself should never become frictionless. Growth requires challenge, reflection and human interaction—experiences that build confidence, resilience and the ability to adapt long after today's AI tools have evolved.
The opportunity ahead isn't simply to integrate AI into curriculum. It's to build curriculum that evolves as quickly as the world around it while remaining grounded in the enduring purpose of education.
Institutions that embrace AI thoughtfully won't just graduate students who know how to use new technology. They'll graduate learners who know how to think critically, collaborate effectively and continue learning throughout their lives—qualities that will remain essential no matter how AI continues to evolve.