The New Learning Stack Reveals the Enduring Work of Education

American College of Education’s Eric Klein offers this commentary on how the new learning stack reveals the enduring of work of education. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Hardly a day goes by without AI becoming part of a conversation that I’m having about higher education.

I hear it in leadership meetings, conference sessions, workshops, and conversations with colleagues across the country. No matter the setting, the questions are consistent. Which AI tools should we adopt? How should faculty incorporate AI into their teaching? What role should AI play in the assessment of student learning? Which platforms are worth investing in? How do we preserve academic quality while embracing innovation?

These are important questions, and they deserve thoughtful answers.

Over the past year, however, I have become increasingly convinced that these questions all point to a much larger conversation.

The rapid emergence of AI platforms is not simply changing the tools we use in education. It is reshaping how learning itself is designed, delivered, supported, and evaluated. Adaptive learning technologies, AI teaching assistants, intelligent course design platforms, AI-assisted assessment tools, and personalized tutoring systems are no longer experimental. They are quickly becoming part of the educational infrastructure.

As an academic leader, I spend far less time wondering whether AI belongs in higher education than I do thinking about how it could reshape teaching, learning, and student success. Those are fundamentally different conversations.

That shift has also changed how I think about the phrase learning stack.

Most discussions describe the learning stack as a collection of technologies. While that is certainly true from a technical perspective, I have come to think about it differently. From an academic perspective, the learning stack is better understood as the set of enduring responsibilities that educational institutions have always carried. We help students access knowledge, guide learning, create meaningful evidence that learning has occurred, and recognize achievement within academic communities that establish credibility, standards, and public trust.

Although the tools continue to evolve, those responsibilities have remained remarkably consistent for generations.

What AI is changing is how we fulfill them.

Learning Becomes More Accessible

The first place we see this transformation is in the creation and delivery of learning itself.

Until recently, developing high-quality instructional materials often required specialized expertise and significant time. Today, AI can help instructional designers and faculty draft learning activities, generate examples, produce multimedia resources, create formative assessments, improve accessibility, and accelerate course development. What once required days or even weeks can often be accomplished in hours.

Students are experiencing a similar transformation as AI-powered learning platforms summarize readings, explain difficult concepts, generate practice questions, provide additional examples, and adapt explanations based on individual questions. Personalized support is becoming available whenever students need it, rather than only during scheduled class sessions or office hours.

These developments represent genuine progress.

At the same time, they remind us that providing access to information has never been the primary purpose of education. Information is abundant. Learning demands something more. Learning requires students to analyze ideas, apply knowledge, wrestle with ambiguity, receive meaningful feedback, and connect concepts across disciplines and experiences.

As content becomes easier to produce, designing meaningful learning experiences becomes even more valuable.

Guidance is Becoming More Personal

One of the most promising aspects of today’s AI learning stack is its ability to expand personalized academic support.

AI teaching assistants and tutoring systems are evolving rapidly. They can answer questions, provide formative feedback, recommend additional practice, explain concepts in multiple ways, and offer guidance at any time of day. For many students, particularly adult learners balancing careers, families, and other responsibilities, these capabilities meaningfully expand access to academic support.

Importantly, these technologies are augmenting rather than replacing educators.

Faculty have never been valuable simply because they possess knowledge. Their greatest value lies in helping students develop judgment. They challenge assumptions, recognize misconceptions, encourage persistence, facilitate meaningful dialogue, and help learners connect academic knowledge with professional practice.

Those responsibilities become even more valuable as AI grows more capable.

Rather than serving primarily as sources of information, faculty are increasingly becoming designers of learning experiences, mentors who cultivate critical thinking, and guides who help students navigate an environment where information is abundant, but wisdom still requires human development.

Assessment Becomes More Authentic

Assessment may be where AI is prompting the most meaningful institutional reflection.

For decades, higher education has relied heavily on assignments completed independently outside the classroom as evidence that students achieved the intended learning outcomes. AI has challenged many of the assumptions underlying that model.

In my experience, conversations about AI quickly move beyond questions of detection and into questions of evidence. What should count as convincing evidence that learning has occurred? How do we design assessments that reveal understanding rather than simply producing polished products?

Those are exactly the questions we should be asking.

Across higher education, institutions are placing greater emphasis on authentic assessments, simulations, portfolios, case analyses, oral presentations, collaborative projects, and iterative demonstrations of learning. These approaches ask students to explain their reasoning, apply concepts to realistic situations, and demonstrate competencies that more closely resemble the challenges they will encounter in their professions.

Ironically, AI may become one of the strongest catalysts for improving assessment. By encouraging us to rethink how learning is demonstrated, it is accelerating conversations that many educators have been advocating for years.

Institutions Matter More, Not Less

One concern I occasionally hear is that increasingly capable AI systems may diminish the role of colleges and universities.

I believe the opposite is more likely.

Educational institutions have always done far more than deliver information. They establish academic standards, organize coherent curricula, create scholarly communities where ideas are debated and refined, cultivate professional identity alongside disciplinary expertise, and validate achievement through credentials that carry public trust.

Those responsibilities become more important as learning becomes more decentralized.

AI can personalize instruction remarkably well. It cannot create an academic community. It cannot cultivate institutional culture. It cannot establish the credibility that comes from rigorous academic standards or replace the relationships that often shape a student’s educational journey.

The more capable AI becomes, the more we can distinguish between delivering information and creating learning.

AI isn’t redefining the purpose of education.

It’s revealing it.

That may prove to be one of the most important outcomes of this technological shift.

The Next Learning Stack

The next chapter of the AI learning stack will likely be defined less by individual tools and more by integration.

Students will soon encounter AI directly within learning management systems, tutoring platforms, advising technologies, productivity software, and student support services rather than moving among standalone applications.

Faculty will rely on AI to streamline routine tasks so they can spend more time mentoring students, facilitating discussions, and designing engaging learning experiences. Instructional designers will continue using AI to accelerate course development while focusing their expertise on creating richer educational experiences.

In other words, AI will become less visible even as it becomes more pervasive.

Over time, the conversation will shift from adopting AI to governing it thoughtfully. Instead of asking which platform to purchase next, institutional leaders will spend more time asking how AI can strengthen learning outcomes, improve assessment, support faculty, reinforce academic quality, and expand student success.

Those are not technology decisions.

They are leadership decisions.

I remain optimistic about where this is heading.

Every major technological advancement has prompted educators to reconsider long-held assumptions about teaching and learning. Artificial intelligence is no different. While it introduces new challenges, it also creates an opportunity to strengthen many of the practices that have always mattered most. It encourages us to design more engaging learning experiences, provide more personalized support, create stronger evidence of learning, and become more intentional about the distinctive value educational institutions provide.

Artificial intelligence will continue to evolve, and so will the learning stack.

The institutions that thrive will be those that continually ask a deeper question. How can technology expand opportunity while preserving the human relationships, intellectual curiosity, and sense of purpose that define great education?

How we answer that question won’t simply shape how we use AI. It will shape how future generations experience education.