Educator Practice in the Era of AI: From Efficiency to Re-Prioritization

There are many compelling reasons for educators to pay close attention to AI’s rapid development in the business world. At the most basic level, the unprecedented speed of adoption and large-scale deployment of AI by major companies signals that our students will need meaningful AI fluency to access future career opportunities. As Jensen Huang, CEO of Nvidia, put it: “You’re not going to lose your job to an AI, but you’re going to lose your job to someone who uses AI.”

Shifts in hiring practices further underscore that process efficiency and employee productivity are central business priorities. While technological change has always reshaped how humans work, AI is accelerating that evolution. Sam Altman, CEO of OpenAI, has said, “I can easily imagine a world where 30 to 40 percent of the tasks that happen in the economy today get done by AI in the not very distant future.”

So, while educators are (or should be) paying attention to what’s going on in the business world, at the same time, the language of automation and efficiency feels out-of-sync with the relationship-based, interest driven values that animate transformative educational experiences. 

Over the past three years, we've spent a lot of time at GOA helping educators make sense of AI and how they might integrate it into their own practice. The framework we've provided asks educators to think about three things: personalization, engagement, and—yes—efficiency.

Personalization is about how students have voice and choice in their learning—how we might rethink time to allow students to develop competencies at their own pace, and how AI might support different demonstrations of learning. Engagement is about how students' behavioral, cognitive, and emotional activity is heightened by their experiences in the classroom. And efficiency is about how to achieve a specific objective—such as producing a good or providing a service—using the fewest possible resources, namely, time.

Because of the expansiveness and sheer developmental speed of AI, for educators tasked with attending to the needs of groups of students, this framework has been a useful way to identify first steps toward AI use that personalizes education and increases engagement. And conversations about efficiency have led to some interesting ideas that may well improve educator practice; things like creating better communications for parents, generating more specific feedback for students, and even aligning assessment tools with colleagues across grade levels in less time than was ever possible before AI. Efficiency was a useful frame for understanding the “how” of AI. 

But I get stuck on the word efficiency because the goal of the shifts in practice I just mentioned is not to do more with less. Indeed, as a teacher at a recent workshop reminded me, some of the most transformative experiences I've had as a student and as a teacher are those that are the least efficient. Put differently, the framing of the “how” of AI has to be enmeshed and aligned  with how we frame the “why” of AI use. 

One helpful thinking routine that can illuminate how we approach educator practice in the era of AI is to think about where we want to subtract, add, or multiply.

-1: Subtract. What practice is no longer serving the needs of students, teachers, or the mission of the school?

+1: Add. What new practice would benefit students, their learning goals, or their social-emotional wellbeing? What new idea would shift our practice to being more mission-aligned?

x2: Multiply. What existing practice do we want to lean into? What are we currently doing that benefits students, but that we haven't had the time or space to flesh out to its full potential?

In a world where AI is now ubiquitous, the questions that emerge from this framework are simple: How can AI help me subtract, add, or multiply?

The answers look different for every educator, but they share something in common; they're not primarily about doing things faster. Re–prioritizing is a mission-aligned movement that might be assisted by AI technologies: 

• Add (+1): Teachers can shift assessment structures toward live oral defenses of learning—a more human, if less efficient, alternative to take-home quizzes—by using AI to handle some automated feedback earlier in the learning journey, creating time and space for the human-to-human interaction that makes those defenses meaningful.
• Subtract (-1): Teachers can identify complex tasks that students have historically struggled with and use AI to rethink the process—replacing a single high-stakes submission with more incremental check-ins that allow them to respond to students' formative demonstrations of learning before it's too late.
• Multiply (x2): For teachers who have built strong relationships with students through regular one-on-one conferencing, AI can help extend that practice by surfacing patterns in student work ahead of time—so that those conversations become even more targeted, substantive, and productive.

This isn't a shift toward greater efficiency. It's an intentional re-prioritization that is enabled by emerging technologies.

As conversations about AI evolve in schools, our language matters.

I don't want to have conversations about how educators can be more efficient. In a lot of ways, we already ask schools to behave like businesses: to do more with less, to measure what's easy to measure, to optimize. Let's be intentional about our language to empower educators to use their discernment and agency to create incredible learning experiences for students.

At the same time, as we think about where AI lives in the classroom and specifically in educator practice, let's empower educators to use AI as a tool to facilitate re-prioritization. Not to save time for its own sake, but to make space for what matters most: the slow, inefficient, transformative work of teaching and learning.