Overview
Artificial intelligence offers powerful new opportunities to support teaching and learning. But without clear goals, strong data systems, and a culture of continuous improvement, even the most promising tools risk becoming distractions rather than drivers of impact.
For schools to harness AI effectively, they must first invest in the foundational conditions that enable innovation to thrive. This includes cultivating a culture where educators are encouraged to learn and experiment, aligning AI use with instructional priorities, and embedding efforts within existing systems for professional learning, data analysis, and collaboration. When these conditions are in place, schools are better positioned to evaluate what works—and scale it in service of student learning.
Example from the School Teams AI Collaborative
At the Eliot K–8 Innovation School in Boston Public Schools, educators rooted their approach to AI in a long-standing commitment to adult learning, data-driven instruction, and a strong professional culture. Drawing on the educational philosophy about organizational culture and school improvement outlined in "Good Seeds Grow in Strong Cultures," the Eliot team extended existing practices to explore the promise of AI in a strategic, thoughtful, and sustainable manner.
In practice, this meant:
Building on a Strong Foundation: Before launching new AI work, the Eliot team reflected on existing systems, like their schoolwide writing data tracker and mid-year data protocols, to explore how they could support responsible experimentation. These systems created a natural pathway for testing tools, measuring impact, and grounding decisions in student learning goals.
Fostering a Culture of Curiosity and Collaborative Learning: Building on their investment in adult learning, Eliot launched an AI Text Club to provide educators with space to explore, reflect, and make sense of emerging tools together. This culture of inquiry created the conditions for AI integration to feel like a natural extension of their existing work.
Connecting AI to Instructional Priorities: Teachers leveraged familiar planning routines and student data to test how AI could support lesson design, small-group instruction, and writing development. Tool use was always anchored in instructional purpose, not novelty, and intentionally woven into everyday practice.
Embedding AI in Continuous Improvement Routines: Because the school already emphasized data reflection and collaborative planning, teachers could apply those same routines to AI integration—analyzing student work, sharing what was working, and adjusting course. This allowed for more informed, responsive implementation.
By leveraging the systems and culture they had already built, Eliot educators explored AI in ways that were aligned, iterative, and rooted in real student needs.
Applying This Strategy in Your Context
Schools don’t have to start from scratch to explore AI meaningfully. By building on systems already in place, educators and leaders can ensure AI integration is thoughtful, sustainable, and focused on student outcomes. The following steps can help lay the groundwork:
Assess Your Foundation: Inventory your current systems for instructional planning, collaboration, and reflection. Identify existing strengths, such as team-based data reviews, shared planning protocols, or peer-led PD, and explore how these might extend to AI-related inquiry.
Make Space for Collaborative Learning: Build time for educators to explore AI tools together. This might include book or text clubs, low-stakes sandbox sessions, or peer learning opportunities where staff can raise questions, test tools, and reflect on use cases. Creating this space may also require leaders to rethink existing PD structures, such as grade-level meetings, department planning time, or early release days, to prioritize collaborative experimentation and shared inquiry.
Anchor AI in Instructional Goals: Ensure AI experimentation is directly connected to learning goals, like improving writing instruction, supporting differentiated learning, or addressing equity gaps. When teachers can see how AI supports the work they’re already doing, implementation becomes more meaningful. For example, at the Eliot Innovation School, educators explored how AI tools could support their existing small-group instruction and writing development efforts, using classroom data to guide tool selection, planning, and implementation. When AI is aligned to student needs and instructional routines, it becomes a meaningful extension of core practice rather than an add-on.
Use Data to Reflect and Refine: As teachers begin testing tools, embed their use into existing data routines. Leverage protocols like student work analysis or grade-level data cycles to assess progress, share findings, and adapt approaches. Look for indicators that AI use is supporting instructional goals, such as increased student engagement, clearer evidence of learning, improved performance on targeted skills, or more efficient planning for teachers. If tool use feels disconnected from classroom needs, creates confusion for students, or doesn't lead to noticeable improvements in student work or participation, it's a sign to revisit the tool’s purpose or how it's being used. Iteration should be guided by both educator experience and evidence of student impact.
Start Small and Learn Together: Treat early AI integration as a learning process. Pilot with small groups, gather feedback, and iterate as you go. Involve teachers as co-leaders in shaping how AI is used to support instruction and student success.
Schools can move beyond surface-level AI adoption by anchoring innovation in strong instructional culture and continuous improvement. When educators explore new tools within systems they already trust—through collaborative inquiry, reflective data use, and shared instructional goals—AI becomes a lever for deeper learning, not a distraction. Grounding AI integration in what works builds capacity, sustains momentum, and ensures that emerging practices lead to lasting, equitable outcomes for all students.
This AI-enabled strategy was developed by a member of the School Teams AI Collaborative—a partnership between Leading Educators and The Learning Accelerator (TLA). The Collaborative was developed to bring together innovative educators from schools across the country to share ideas and discover effective ways to use AI in the classroom.
Strategy Resources
The Eliot Innovation School’s SY24-25 Mid-Year Data Analysis Slide Deck
This internal slide deck outlines Eliot’s process for mid-year data analysis, from identifying schoolwide trends... Learn More
The Eliot Innovation School’s Writing Data Tracker Sheet
This schoolwide Google Sheet tracks student progress in writing across informational, argumentative, and narrative genres... Learn More
The Eliot Innovation School’s January 2025 AI & Instruction PD Slides
This professional development session supported educators in deepening their understanding of AI tools and integrating... Learn More
The Eliot Innovation School’s Staff Culture Foundational Reading: Good Seeds Grow in Strong Cultures
This foundational article by Jon Saphier and Matthew King outlines 12 key cultural norms that... Learn More
The mission of the Eliot K-8 Innovation School is to provide a fully inclusive, joyful, rigorous learning journey that prepares every student to achieve their highest potential by embracing their identities, developing interdisciplinary 21st-century skills, and applying an antiracist mindset empowered by knowledge to participate actively in a complex and constantly changing, culturally diverse world.
