Artificial intelligence is already reshaping how we work, communicate and create. In education, however, the conversation is stuck.

Sensational headlines make it seem like AI will either save public education (“AI will magically give teachers back hours in their day!”) or destroy it completely (“Students only use AI to cheat!” “AI will replace teachers!”).

These dueling narratives dominate public debate as state and district leaders scramble to write policies, field vendor pitches and decide whether to ban or embrace tools that often feel disconnected from what teachers and students actually experience in classrooms.

What gets lost is the fundamental question of what learning should look like in a world in which AI is everywhere. And that is why, last year, rather than debate whether AI belongs in schools, approximately 40 policymakers and sector leaders took stock of the roadblocks in an education system designed for a different era and wrestled with what it would take to move forward responsibly.

Related: A lot goes on in classrooms from kindergarten to high school. Keep up with our free weekly newsletter on K-12 education.

The group included educators, researchers, funders, parent advocates and technology experts and was convened by the Center on Reinventing Public Education. What emerged from the three-day forum was a clearer picture of where the field is stuck and a shared recognition of how common assumptions are holding leaders back and of what a more coherent, human-centered approach to AI could look like.

We agreed that there are several persistent myths derailing conversations about AI in education, and came up with shifts for combating them.

Myth #1: AI’s biggest value is saving time for teachers

Teachers are overburdened, and many AI tools promise relief through faster lesson planning, automated grading or instant feedback. These uses matter, but forum participants were clear that efficiency alone will not transform education.

Focusing too narrowly on time savings risks locking schools more tightly into systems that were never designed to prepare students for the world they are graduating into.

The deeper issue isn’t how to use AI to save time. It’s how to create a shared vision for what high-quality, future-ready learning should actually look like. Without that clarity, even the best tools quietly reinforce the same factory-model structures educators are already struggling against.

The shift: Stop asking what AI can automate. Start asking what kinds of learning experiences students deserve, and how AI might help make those possible.

Myth #2: The main challenge is getting the right AI tools into classrooms

The education technology market is already crowded, and AI has only added to the noise. Teachers are often left stitching together core curricula, supplemental programs, tutoring services and now AI tools with little guidance.

Forum participants pushed back on the idea that better tools alone will solve this problem. The real challenge, they argued, is to align how learning is designed and experienced in schools — and the policies meant to support that work — with the skills students need to thrive in an AI-shaped world. An app is not a learning model. A collection of tools does not add up to a strategy.

Yet this is not only a supply-side problem. Educators, policymakers and funders have struggled to clearly articulate what they need amid a rapidly advancing technology environment.

The shift: Define coherent learning models first. Evaluate AI tools based on whether they reinforce shared goals and integrate with one another to support consistent teaching and learning practices, not whether they are novel or efficient on their own.

Myth #3: Leaders must choose between fixing today’s schools and inventing new models

One of the tensions dominating the discussions was whether scarce state, local and philanthropic resources should be used to improve existing schools or to build entirely new models of learning.

Some participants worried that using AI to personalize lessons or improve tutoring simply props up systems that no longer work. Others emphasized the moral urgency of improving conditions for students in classrooms right now.

Rather than resolving this debate, participants rejected the false choice. They argued for an “ambidextrous” approach: improving teaching and learning in the present while intentionally laying the groundwork for fundamentally different models in the future.

The shift: Leaders must ensure they do not lose sight of today’s students or of tomorrow’s possibilities. Wherever possible, near-term pilot programs should help build knowledge about broader redesign.

Myth #4: AI strategy is mainly a technical or regulatory challenge

Many states and districts have focused AI efforts on acceptable-use policies. Creating guardrails certainly matters, but when compliance eclipses learning and redesign, it creates a chilling effect, and educators don’t feel safe to experiment.

The shift: Policy should build in flexibility for learning and iteration in service of new models, not just act as a brake pedal to combat bad behavior.

Myth #5: AI threatens the human core of education

Perhaps the most powerful reframing the group came up with: The real risk isn’t that AI will replace human relationships in schools. It’s that education will fail to define and protect what is most human.

Participants consistently emphasized belonging, purpose, creativity, critical thinking and connection as essential outcomes in an AI-shaped world.

But they will be fostered only if human-centered design is intentional, not assumed.

The shift: If AI use doesn’t support students’ connections between their learning, their lives and their futures, it won’t be transformative, no matter how advanced the technology.

The group’s participants did not produce a single blueprint for the future of education, but they came away with a shared recognition that efficiency won’t be enough, tools alone won’t save us and fear won’t guide the field.

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The question is no longer whether AI will shape education. It is whether educators, communities and policymakers will look past the headlines and seize this moment to shape AI’s role in ways that truly serve students now and in the future.

Maddy Sims is a senior fellow at the Center on Reinventing Public Education (CRPE), where she leads projects focused on studying and strengthening innovation in education.

Contact the opinion editor at opinion@hechingerreport.org.

This story about AI in education was produced byThe Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for Hechinger’s weekly newsletter.

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