More Than a Shortcut: What Educators Really Want from AI

Generative AI has landed in schools, but most of what it’s doing right now is just scratching the surface.

Lesson plan generators. Rubric writers. Auto-filled report cards. These tools are helpful. They save time. They reduce cognitive load. But in the grand scope of education, they’re not transformational yet.

They’re making teaching faster. But they’re not yet making it better.

If we want AI to truly move the needle in education, we need to stop thinking in terms of productivity and start thinking in terms of possibility.

The SAMR Lens: Where We Are, and Why It Matters
The SAMR model—Substitution, Augmentation, Modification, and Redefinition—is a simple way to measure how technology transforms learning.

Right now, most AI tools in education sit firmly in the Substitution or Augmentation levels:

Drafting lesson plans. Summarizing data. Creating rubrics. Writing quick emails

Valuable? Yes. Transformative? Not quite.

These tools aren’t reinventing how we teach or learn. They’re helping us complete old tasks more efficiently.

But transformation isn’t just about saving time. It’s about opening new doors.

Teaching Is Both Art and Science
At its core, teaching is a blend of two powerful forces:

The science of teaching: rooted in evidence, learning theory, cognitive science, and data

The art of teaching: fueled by relationships, intuition, culture, and lived experience

AI can’t replace the art. It shouldn’t try.

But it can, and should, revolutionize the science.

When used wisely, AI has the power to take research-based practices that too often live in journals or conference sessions, and translate them into real-time, student-ready strategies.

Because here’s the truth:

Education research rarely reaches the classroom in time to matter.

What if AI could change that?

AI as a Research-to-Practice Bridge
One of the biggest systemic gaps in education is the time lag between when we know something works and when we actually do it.

Teachers are constantly told to “use evidence-based practices,” but those strategies often feel abstract, outdated, or hard to implement without support.

AI could be the missing link.

Imagine:

–An AI tool that surfaces spaced repetition strategies while a teacher builds a unit

–A lesson designer that integrates Universal Design for Learning (UDL) principles based on student needs

–A feedback generator that aligns with growth mindset language and trauma-informed practices

AI could become a real-time delivery mechanism for the best of education research.

That’s not just innovation; it’s systems-level transformation.

What Educators Are Using AI For Now
Most current AI use in education centers on support tasks:

–Drafting pacing guides

–Composing parent updates

–Writing sample IEP goals or report card comments

–Creating multiple-choice quizzes

–Generating exemplar responses

These tools help teachers reclaim time. They lighten the load. But they don’t fundamentally shift how learning happens.

And that’s what educators are asking for next.

Where Educators Push Back
Ask educators to hand over more interpretive or relationship-driven tasks, and you’ll hear hesitation:

“I don’t want AI to give all my students the same feedback.”

“Grouping students requires more than data; it takes knowing them.”

“I’m not sure AI can understand the tone behind that student’s writing.”

These tasks require human insight, not just algorithms.

Educators aren’t resisting AI, but they are protecting the parts of their craft that matter most.

From SAMR to TPACK: Beyond Efficiency
While SAMR helps us assess how a tool changes practice, the TPACK framework (Technological, Pedagogical, and Content Knowledge) helps us evaluate whether it integrates meaningfully at all.

Many AI tools today focus solely on the “tech” piece (generating content or automating tasks) without aligning to pedagogy or curriculum.

That’s a problem.

A truly transformative AI tool would:

–Deepen content through context

–Support strong instructional strategies

–Adapt to different grade levels, subjects, and classroom cultures

Because no matter how advanced the tool, if it’s out of sync with how and why we teach, it won’t stick.

So What Do Educators Actually Want?
1. Flexible Instructional Design Partners
Not just templated lessons, but tools that scaffold, remix, and adapt to student needs.

Ex: An AI assistant that generates multiple modalities for one objective and lets teachers mix and match.

2. Student-Centered Scaffolding
Differentiation support that informs, not overrides.

Ex: A writing coach that builds on a student’s unique voice, while giving teachers the power to adjust tone, rigor, and next steps.

3. Insight, Not Just Analytics
AI should help educators see patterns, not just numbers.

Ex: A dashboard that detects engagement drops, flags inequities, and suggests instructional pivots.

4. Student Use That Builds Agency
AI should help students create, not just consume.

Ex: A chat-based AI that helps students explore historical perspectives or compose poetry, while teaching responsible use and digital citizenship.

Systems-Level: Designing AI for All Learners
At the district and state level, AI is more than a technology initiative. It’s a leadership responsibility.

The way AI is implemented can either expand access to quality learning… or unintentionally reinforce existing gaps.

The questions system leaders must ask include:

–Who’s shaping the design of the AI tools we’re adopting?

–Whose languages, learning styles, and lived experiences are reflected?

–Do all teachers have the tools and support they need to use this technology effectively?

If AI becomes yet another tool that only well-resourced schools or tech-savvy educators can leverage fully, it won’t solve problems. In fact, it will create new ones.

District and school leaders play a critical role in ensuring:

–Access is intentional from the start, not assumed

–Training and support are prioritized, not optional

–Teachers and families are part of the rollout, not just recipients of it

Thoughtful implementation means more than checking boxes! It means asking who benefits, who might be left out, and how we create the conditions where every learner can thrive.

For District & System Leaders: Start Here
Want to lead with vision instead of playing catch-up? Start with these three moves:

✅ Audit Your AI Landscape Where is AI already embedded across your district? Are those tools aligned with your instructional goals and values?

✅ Form an AI Leadership Team Include curriculum directors, tech leaders, equity advocates, classroom teachers, and even students. Decisions about AI must reflect multiple voices.

✅ Pilot with Purpose
Choose one use case (lesson generation, student feedback, family communication) and try it with clear goals, training, and feedback loops.

AI isn’t just an IT strategy. It’s an instructional one. It’s a leadership strategy. And leaders must shape the conditions for impact.

Final Thought: From Automation to Amplification
AI won’t replace educators. It can’t.

What it can do is give them back the time, insight, and tools they need to focus on what only they can do: build relationships, spark curiosity, and nurture learning.

Let’s stop asking what AI can do instead of teachers and start asking what it can do because of them.

When AI elevates the science of teaching, educators are freed to focus on the art. And that’s where transformation lives.

For Your Reflection…

For Educators:

What AI tools are helping you work more efficiently, but not necessarily teach more effectively?

What would a truly impactful AI tool look like in your subject area or classroom setting?

For EdTech Designers:

Are your tools built with an understanding of how real classrooms operate or just what the code can generate?

How are you ensuring your designs reflect the needs of different learners, communities, and teaching approaches?

For District & System Leaders:

Are you guiding AI adoption with a clear instructional vision or reacting to market momentum?

How are you ensuring that every school and every educator has what they need to implement AI tools confidently and meaningfully?