AI Early Adopter Districts: The Promises and Challenges of Using AI to Transform Education

• In Event: Urban AI Unlocked: Transforming District Systems for Equitable AI Integration

Abstract

Objectives

AI could transform public education by making learning more personalized and teaching more sustainable, and addressing longstanding challenges like transforming high school, developing more relevant data and assessment systems, scaling evidence-based practices, and improving outcomes for marginalized students.

To understand how school districts are responding to this pivotal moment, we launched a national landscape study of early AI adopters in the 2024–25 school year. Interviews, surveys, and profiles of AI innovators in K–12 education ground our findings.

This session will highlight findings from our landscape study and offer recommendations for districts, and policymakers who aim to leverage AI to address long-standing challenges in public education.

Methods

Our team of researchers conducted a mixed methods study from August 2024 to May 2025 to surface insights from districts (defined as public school districts and public charter management organizations) engaging early with AI. We captured perspectives from superintendents, instructional chiefs, and support organizations to deepen understanding of Early Adopter practices and mindsets. We identified districts via referrals and desk research that met our criteria for systemic AI adoption (piloting or exploring an AI tool or strategy in one or more schools, with central office coordination or endorsement). Recruited districts agreed to participate in focus groups and interviews to deepen understanding of Early Adopter practices and mindset. We also collected survey responses from district representatives on AI practices, barriers, and enabling conditions. We coded data inductively and iteratively to identify patterns in adoption types, enabling conditions, and barriers, with typologies ranging from “Dabblers” to “Reimaginers.” While not nationally representative, the sample of participating focus group districts represented geographic, student demographic, and size diversity, with an overrepresentation of suburban districts.

Results

AI adoption, even in leading systems, is currently limited and aspirational. However, Early Adopters’ early wins and momentum indicate the potential for AI to create new solutions and circumvent longstanding barriers to change—if external actors can structure an ecosystem and marketplace in time to offer meaningful support.

Significance

Based on our study, our researchers recommend the following:

System Leaders: 

Develop a clear vision, or “why” for AI for your school system, informed by diverse stakeholder groups. Find and participate in peer networks to support your design and implementation. 

Recognize that your teachers, principals, and staff will learn about AI alongside students. Prioritize their literacy and encourage knowledge sharing among peers and with families.

Ensure the tools purchased or developed align with your systems’ goals, curriculum, and standards. 

State Leaders:

Develop AI learning networks through state offices, county offices, nonprofits, and/or universities.

Encourage edtech collaborations between districts and higher education providers.

Study AI tools and build an evidence base for their effectiveness.

Upgrade district infrastructure to make data more accessible and transparent.

Consider developing an “innovation zone” carve-out for districts interested in testing AI carefully and rigorously, perhaps with some easing of data privacy rules.

Authors

• Robin Lake, Arizona State University

• Steven Weiner, Arizona State University

• Maddy Sims, Arizona State University

• Bree Dusseault, Arizona State University