For generations, we have thought about learning in school as acquiring, recalling, and applying information and skills. These core learning elements were generally considered to be enough to secure employment, support a family, and have a good life. Schools, therefore, have been designed to align to—and have been reasonably successful in accomplishing—this task.
Now, artificial intelligence (AI) is disrupting much of what was assumed about how schools should operate and what they should accomplish. AI can immediately retrieve information on almost any topic, generate endless explanations, solve a wide variety of problems, write and correct narratives, and demonstrate a broad range of skills. Yet when students access these resources in the current formal education model, they are often accused of cheating. Maybe what is really happening is that they are showing that the traditional school model and its way of thinking about learning are no longer adequate.
The reality surrounding learning structures, approaches, and expectations is shifting, and we need to be open, thoughtful, and flexible as we consider our role, what matters most, and how to reposition learning. Consider these fundamental shifts in how we need to think about learning and engaging students in the age of AI.
Shift #1: From generating answers to engaging in thinking.
Traditional tests have valued correct answers, even though producing the right answer is not necessarily evidence of deep understanding or quality thinking. Yet the world for which we are preparing students will value the thinking behind answers more than the answers themselves. Assessments of learning need to shift from focusing on answers to examining the thinking behind them. The future will place higher value on the ability to explain reasoning, recognize patterns, justify conclusions, compare alternatives, and related thinking evidence than on providing the correct answer.
Shift #2: From knowing information to knowing what to do with it.
Recalling and repeating information may have been enough in the past, but the instant availability of information makes interpreting, evaluating, connecting, and applying it an increasingly important skill set. Asking AI for an answer is not necessarily a demonstration of learning. Making sense of information, understanding why it matters, and knowing how to apply it is a higher and increasingly important learning outcome. We need to move from “What do you know?” to “What can you do with what you know?”
Shift #3: From remembering everything to remembering what matters.
The emergence of AI does not mean that there is no need to learn and retain information and skills. Rather, AI makes it more important to distinguish between information that can be outsourced and accessed from knowledge that needs to be internalized and available. For example, students need to have enough background knowledge and understanding to recognize inaccurate and useless information, make connections and see relationships, ask relevant and sophisticated questions, and draw accurate and responsible conclusions.
Shift #4: From isolated learning and independent performance to thoughtful, productive collaboration.
Schools have traditionally placed a premium on students learning independently and without assistance. Care is taken to ensure that work production and performance represent the efforts of the student alone. The future will be more likely to ask what individuals have contributed, whether working with AI, collaborating with a partner, or engaging with a team. When AI is involved, the relevant questions become what AI did for the student, what the student did with AI, and what the student was able to do because of AI. In a collaborative context, learning looks more like how learners decided the work to be done, moved the work forward, and improved the outcome.
Shift #5: From learning guided by compliance to learning driven by agency.
When students see learning as completing tasks designed and assigned by adults and serving adult needs, they may find it easier to complete tasks by accessing AI without engaging in deep, meaningful learning. Learning in the age of AI is shifting toward experiences that are meaningful, purposeful, and useful to the learner. Interestingly, AI can help us to present learning activities that meet these criteria while also giving students greater agency in their learning. For example, by meeting students where they are, we can engage them in understanding what they need to learn, recognizing what they already know, identifying learning gaps to close, choosing and pursuing goals, locating resources to support their learning, testing their understanding, and monitoring their progress.
Shift #6: From “get it and forget it” to learning, unlearning, relearning, and adapting.
A prevailing assumption in traditional learning environments is that the environment remains relatively static. Once people understand information and develop a skill, the work is done. Yet the pace of change was accelerating before the emergence of AI, and since its emergence, that pace has quickened in unimaginable ways. New knowledge, new approaches, and new tools arrive at a dizzying pace. This shift requires students to know how to learn, recognize when they need to let go of what no longer holds value, and relearn and adapt as circumstances change. Nurturing curiosity, asking insightful questions, building situational awareness, and growing reflection skills will serve students well in response to this shift.
As AI gets better at providing answers, it becomes more important for students to ask the right questions, think deeply, exercise keen judgment, and use those answers in meaningful, useful ways. What it means to learn in the age of AI is changing. We need to be sure that students are ready.
