What Educators Say They Need

AI is already changing what teaching looks like. It’s showing up in planning, grading, student writing, and classroom routines, often faster than systems can respond.

The question is no longer whether AI will shape K–12 education. It’s how to best harness it so that it strengthens thinking rather than simply making work move faster.

This month, Leading Educators releases the second edition of the Value Add of Technology on Teaching, a practical framework for making clearer, instruction-centered decisions about AI and technology use in classrooms, designed to help educators and leaders determine when and how technology strengthens instruction.

As we introduce what we have updated and upgraded for the age of AI, we’ll be sharing a series of reflections from our research that provide greater context. To start, we’re surfacing three big themes we heard repeatedly in conversations with teachers and leaders.

Shifting from Efficiency to Coherence

Teachers are overwhelmed. Many are turning to AI because it saves time. It can draft a quiz in seconds. It can generate feedback. It can summarize a reading passage. Used strategically, those efficiencies matter.

But speed alone does not improve learning. If AI use is not aligned to instructional priorities, curriculum, and expectations for rigor, it becomes a collection of individual experiments rather than a shared strategy. 

For system leaders, this is a coherence problem.

AI creates value when it reinforces your system’s instructional core. When it frees up time so teachers can analyze student work more closely. When it supports planning that aligns with your curriculum. When it reduces administrative load so that instructional conversations can take priority.

In the Value Add of Technology on Teaching framework, we describe this value add as “Do More,” when technology frees up time and energy in ways that reinforce the instructional core.

Supporting Human Judgment

Every day, teachers make hundreds of decisions. They notice confusion on a student’s face. They adjust the pacing when a concept isn’t landing. They decide whether to press students to struggle longer or step in with support. 

• AI can generate explanations and draft materials.
• It can analyze patterns across student responses.

It cannot, however, sit in a classroom and understand why one group of students is stuck while another is ready to move on.

Those decisions require professional expertise, relationships, and real-time instructional judgment. A high school physical science teacher in North Carolina–let’s call her Tiffany–shared how she supported students who were struggling to balance chemical equations, stating:

I found a PHET simulation that showed the atoms as little colored circles representing different elements. When we manipulated the numbers, they could actually see that you had the same number of atoms on both sides of the reaction.”

Tiffany didn’t choose the simulation because it was flashy or new. She selected it because it addressed a specific learning barrier: her students couldn’t visualize what was happening in a chemical reaction. The tool helped make an invisible process visible, strengthening sense-making without lowering rigor.

The value came not from the tool itself, but from the educator’s ability to diagnose the learning need and choose the right support that enhanced student thinking. AI can support that work, but it cannot replace the judgment that makes it effective.

We call this value add “Do Better” when technology is used instructionally to strengthen and improve learning outcomes. 

Designing with Intention

Every day, teachers search for ways to expand opportunities for their students. They imagine new ways for ideas to take shape. They look for structures that make thinking more visible and collaboration more meaningful. They design experiences that stretch beyond what was previously possible.

Margaret, an elementary math teacher at a bilingual school, wanted her sixth graders to experience learning beyond the classroom walls.

She partnered with a teacher in Colombia to co-teach a lesson live. Students in both countries worked through the same math problems simultaneously, collaborating in a shared digital workspace. As they collaborated, students were not only solving mathematical problems; they were actively practicing language skills in both English and Spanish, explaining their thinking, asking clarifying questions, and refining academic vocabulary across two domains.

Without video conferencing and shared digital workspaces, coordinating that kind of live collaboration across countries would have been extremely difficult. The technology made the connection possible. The learning came from how the task was structured and facilitated. Students were solving problems, communicating clearly, and learning from peers outside their own classroom. That is what intentional innovation looks like in practice.

In the framework, we describe this as “Do New,” when technology expands what’s possible in learning while keeping human judgment and relationships at the center.

What Comes Next

If these are the lenses, what does it look like to lead with them?

Our research suggests that AI alone does not improve teaching and learning. Its impact depends on how clearly it connects to instructional priorities, professional judgment, and classroom coherence.

Stay tuned for a deeper look into “Technology Value-Adds” in the coming weeks.