Rethinking Teaching and Learning in an AI-Enabled World: Questions Every Education Innovator Should Be Asking
Artificial intelligence is challenging long-held assumptions about learning, assessment, instructional practice, and educational innovation. While today's conversations focus largely on cheating, AI policies, and how to use AI to support instructional goals, those debates often overlook the deeper instructional questions that AI is forcing education organizations to confront.
This essay doesn't attempt to answer those questions. It argues that organizations asking foward-looking questions today will be better positioned to design, communicate, fund, and implement meaningful educational innovation tomorrow.
Debating the Symptoms, Not the Disruption
Conversations and headlines about AI now routinely focus on cheating, plagiarism, AI detectors, classroom policies, and the role of generative tools in student work.
These are important issues, and educators are right to take them seriously. Yet the intensity of these debates may also be obscuring a larger reality: many of today's most pressing AI questions are symptoms of a broader instructional transition that education has only begun to understand.
As education innovators, we have good reason to focus on these immediate challenges.
Schools need practical guidance.
Educators need clear policies.
Organizations developing AI-enabled products and services need thoughtful implementation strategies to boost learning, streamline and hone assessment, and ensure public trust, but forward-looking education organizations understand that AI is forcing education to confront much larger questions.
As AI reshapes the nature of productive work and lowers the barriers to many forms of cognitive output, education is being challenged to reconsider not only use policies but also what we teach, how we teach, and how schools prepare students for an AI-enabled world.
This reflection begins with a simple premise: before we can make sound decisions about implementing artificial intelligence, we need to ensure we're asking the right kinds of questions.
The implementation challenges surrounding AI deserve thoughtful attention, but they also belong within a much larger conversation about teaching, learning, and educational purpose.
The goal here is not to answer those larger questions. Rather, it is to make them more central to the conversations education innovators are having today.
Today’s AI Challenges Are Familiar Enough…But Bigger Ones Are on the Horizon
The first wave of AI adoption presents institutions of learning with a familiar challenge. Like every major instructional innovation before it, artificial intelligence succeeds or fails as an educational tool not because of the technology itself, but because of how thoughtfully it is integrated into teaching and learning.
Effective implementation still depends on clear instructional goals, evidence-informed instructional design, educator capacity, organizational leadership, and meaningful adaptation to the needs and values of local communities.
Those practical challenges deserve careful attention and are explored in our companion article, Leading Through AI Uncertainty: Four Guiding Principles for Education Innovators.
Yet these challenges are only a first wave of change.
The second wave is likely to prove far more consequential.
Because AI has powers that fundamentally exceed the present generation of EdTech tools, challenges for education innovators extend beyond questions of adoption and into the future of teaching and learning themselves.
Unlike previous generations of educational technology, today's AI systems can do far more than deliver information or organize learning resources. They can generate original text, summarize complex ideas, analyze data, write computer code, create multimedia, translate languages, provide individualized tutoring, and assist with increasingly sophisticated forms of problem solving.
Although these capabilities continue to evolve, our prevailing views of education — about core learning outcomes and prevailing instructional approaches — are naturally being drawn into a broader conversation, one that extends well beyond questions of classroom policy and academic integrity:
If AI increasingly performs tasks that have traditionally served as demonstrations of student learning, what knowledge, skills, and habits of mind become most valuable?
If learners have routine access to intelligent systems throughout their education and careers, how should teaching and learning evolve to help them use those capabilities thoughtfully, responsibly, and effectively?
These are not entirely new questions. Education has always adapted to changing technologies, evolving workforce expectations, and new understandings of how people learn.
What makes this moment feel different is the pace at which AI is expanding human cognitive capability and the breadth of activities it now influences.
The first wave of AI adoption asks how artificial intelligence can be implemented responsibly and effectively within today's educational systems. The second wave asks whether some of our assumptions about learning, assessment, expertise, and educational preparation may themselves need to evolve.
Three Big Ideas
AI is changing more than classroom tools — it is challenging long-held assumptions about teaching and learning.
The biggest educational questions lie beyond today's debates over cheating and AI policies.
Education innovators should begin asking longer-term questions now, even while today's implementation challenges continue to evolve.
Those questions do not diminish the importance of sound implementation but do remind us that implementation is no longer the whole conversation.
The sections that follow explore two of the most important questions emerging from this second wave of AI-enabled change:
What do students need to learn in order to thrive in an AI-enabled world?
And equally important,
How might AI reshape teaching and learning?
Question 1: What Do Students Need To Learn In Order To Thrive in an AI-Enabled World?
One of the most significant educational shifts created by artificial intelligence has little to do with the technology itself. It concerns the relationship between the work students produce and the capabilities that work is intended to represent.
For generations, educators have generally been able to treat student performance as an imperfect but useful indicator of what learners know and can do independently. As AI increasingly participates in writing, research, coding, analysis, problem solving, and other forms of cognitive work, that relationship becomes less straightforward. A learner may now produce work that exceeds what they could have created on their own. That does not automatically diminish the educational value of the work, but it does make the relationship between assistance, performance, and learning more difficult to interpret.
This shift extends beyond questions of academic integrity or appropriate AI use.
It reaches into the heart of curriculum design and standards.
AI can retrieve information, synthesize ideas, generate explanations, solve increasingly complex problems, and support many forms of cognitive work. While these capabilities do not eliminate the need for knowledge itself, they do reorient what learning matters most.
Learners still need sufficient understanding to frame worthwhile questions, evaluate AI-generated responses, recognize inaccuracies, exercise sound judgment, and apply ideas in unfamiliar contexts.
Schools have always made decisions about what students should remember, understand, practice, and ultimately be able to perform independently. AI does not eliminate those decisions. It simply makes them more visible and urgent now, as they have been in other moments of innovation, such as the advent of space exploration.
That naturally leads to a broader educational question: What knowledge and capabilities should learners continue to develop internally? Which can be responsibly augmented through increasingly capable technologies?
Rethinking Foundational Knowledge
If learners have immediate access to information, explanations, and increasingly sophisticated AI-generated content, what knowledge must they still develop internally?
At first glance, the answer might appear to be "less than before." Yet access to information has never been the same as understanding it, constructing it, or applying it. Learners still require sufficient background knowledge to recognize patterns, connect ideas, ask meaningful questions, evaluate evidence, identify inaccuracies, and apply what they know in unfamiliar contexts. Those capacities depend on more than retrieval. They depend on various kinds of connection-making and judgment.
At the same time, broadly prohibiting AI use without a clear framework and rationale for why it’s necessary and pushing learners to do themselves what AI can do, is going to grow harder to justify and likely to be met with from learners ever-more adapted to digital spaces. But here too, we urgently need shared insights and reflection to guide the way.
The question, then, is not merely whether basic cognitive competencies and dimensions of thought still matter. It’s reflecting on which capabilities can be responsibly augmented with AI or “off-loaded” to AI tools vs. which forms of knowledge, judgment, creativity, and communication remain crucial for individual learners and social institutions to thrive in an AI-enabled world.
Rethinking Student Work
A second tension concerns the relationship between academic work and learning itself. For generations, educators have used the work students produce as one of the clearest indicators of what they know and can do. As AI becomes capable of assisting with writing, analysis, coding, research, and other complex tasks, that relationship becomes less straightforward.
This does not mean that AI-assisted work lacks educational value. It does suggest, however, that educators may need to think more carefully about what student work is intended to demonstrate. Is the goal to produce a polished product, to develop a particular capability, or both? How should learning be understood when intelligent tools increasingly become part of the work itself?
These questions shift attention away from educational outputs alone and back toward the initial conversation about educational outcomes. The central issue is no longer simply What did the student produce? It increasingly becomes What capabilities did the student develop in the process?
Rethinking Human Expertise
A third tension emerges as AI assumes a growing role in routine cognitive work. As intelligent systems become more capable of generating content, analyzing information, and solving increasingly sophisticated problems, what forms of human expertise become more valuable rather than less?
One obvious possibility is that education places even greater emphasis on the capacities that enable learners to work thoughtfully with AI rather than simply rely upon it. Discernment, contextual understanding, ethical reasoning, intellectual curiosity, sound judgment, and the ability to frame worthwhile questions may become increasingly important — not because AI cannot contribute to these activities, but because meaningful human decisions continue to depend upon them.
The purpose of this observation is not to redefine the curriculum or suggest that these qualities are entirely new. Rather, it is to recognize that AI policy will feel reactive rather than forward looking if conversations stall at “students still need to learn how to write” or “AI use is cheating.”
More important will be to reconsider which human capabilities deserve greater emphasis as intelligent technologies become more deeply integrated into learning and professional life — making this a foundation for longer-term AI integration and policymaking.
These tensions do not alone tell any of us what students should learn in an AI-enabled world. They simply illustrate why the question itself has become more urgent.
Question 2: How Might AI Reshape Teaching and Learning?
If AI prompts education to reconsider what students should learn, it also invites a second question that is equally important: How should those capabilities be developed?
Throughout history, changes in educational priorities have almost always been accompanied by changes in instructional practice. If the outcomes we value evolve, the learning experiences designed to cultivate those outcomes may need to evolve as well.
This is not to suggest that artificial intelligence replaces established principles of effective teaching.
Research on learning continues to affirm the importance of active engagement, meaningful practice, timely feedback, retrieval, collaboration, and thoughtfully sequenced instruction. Those foundations remain remarkably durable.
What AI may change is not the science of learning itself, but the range of instructional approaches available to support it.
Rethinking the Learning Process
Already, AI is beginning to serve a variety of instructional roles. It can function as a tutor that provides immediate feedback, a collaborator that helps learners refine ideas, a coach that encourages reflection, a cognitive scaffold that supports complex tasks, or a simulation partner that allows students to practice difficult conversations or professional scenarios. Each role offers new possibilities for extending learning beyond the traditional boundaries of classroom instruction.
These developments naturally raise broader instructional questions.
How might the sequence of learning change when learners have immediate access to individualized guidance?
Which learning experiences remain most valuable to complete independently, and which are strengthened through thoughtful collaboration with intelligent tools?
How should educators distinguish between productive support and unproductive dependence?
These questions are unlikely to have universal answers, but they point toward new opportunities for instructional design rather than simply new technologies to adopt.
Rethinking Assessment
Questions about instruction inevitably lead to questions about assessment. If AI increasingly becomes part of authentic academic and professional work, what kinds of evidence of learning should educators rely on?
At the moment there's lots of emphasis on discerning what learners can do without AI assistance vs. what they can do, or how well they can perform on an assessment, without AI assistance.
This is not inevitable, but not surprising. For generations, education has often relied on completed products as evidence of understanding.
The larger question now is how assessment can continue to provide meaningful evidence of learning in environments where intelligent tools increasingly become part of the learning process itself and of the world that learners, and the rest of us, live in.
We will likely need to be thinking about how to assess the kinds of thinking, judgments, discernment, and decision making that go into a sample of learning — dimensions of learning, skill, creativity, innovation, and application that finished products alone cannot always capture.
As some traditional assessment models, approaches, and products lose their relevance or value, others will emerge in their place perhaps: demonstrations of reasoning, iterative revision, collaborative problem solving, oral explanation, authentic performance, and portfolios that document learning, for example. These may all prove to be assessment formats better aligned with measuring and prompting learning in an AI-enabled world — assessments that reveal
Rethinking the Teacher's Role
Perhaps the most enduring question concerns the role of the teacher. Discussions of AI often focus on what technology can do, but education has never depended on information alone.
Learning is shaped by relationships, encouragement, professional judgment, thoughtful feedback, and the ability to create environments where curiosity, perseverance, and intellectual growth can flourish.
This reminds us that AI is a knowledge production tool within a much broader educational enterprise. Schools are concerned not only with knowledge acquisition, but also with character development, socialization, identity formation, and preparing young people to participate thoughtfully in society.
If AI assumes a greater role in providing information or routine instructional support, teachers may become even more important as designers of learning experiences, facilitators of inquiry, mentors, coaches, cultivators of sound judgment, and builders of classroom communities. These are not entirely new responsibilities.
Which aspects of teaching are most fundamentally human?
Which responsibilities are best supported by intelligent technologies?
How might educators combine both in ways that strengthen learning rather than diminish it?
Teachers have always been central to student success and wellbeing, and with AI introducing as many future challenges as promises, it’s important to ask new and aspirational questions about the crucial role teachers will play amid fast-evolving ideas about what students most need to learn and about the emerging challenges students will confront in an AI world.
Final Thoughts
The history of educational innovation suggests that lasting change has rarely been defined by technology itself. Instead, enduring innovations have emerged when new tools were integrated into thoughtful instructional designs in the service of relevant and meaningful learning goals.
Artificial intelligence appears likely to present education with the same opportunity.
The technology will continue to evolve. The more enduring challenge will be ensuring that our thinking about teaching and learning evolves with it.
The first wave of AI adoption is understandably focused on implementation. Schools need practical guidance and educators need policies and strategies that ensure the best possible integration of AI for teaching and learning as well as for academic integrity, fairness, and student privacy and safety as well.
The second wave, however, invites a broader kind of reflection — one that asks not simply how artificial intelligence fits within today's educational models, but how teaching and learning themselves may continue to evolve in an AI-enabled world.
Both conversations matter, but history suggests that the most enduring educational innovations aren’t really about what technology can do for learning. They are about the questions educators and the communities they serve are asking about learning — and demanding from learning.
