Operationalizing AI in K-12 Education: Five District Use Cases for Ethical and Strategic Adoption

by Yenda Prado, Pati Ruiz, Judi Fusco, Katherine Adler, Brooke Falck, and Melissa L. Smith

Middle school history teachers discuss their lesson plans for teaching about the Great Depression.
Photo by Allison Shelley for EDUimages

Use Case 1: Building Collaborative Networks
Use Case 2: Developing Cross-Functional Teams & Procurement
Use Case 3: Tailoring Professional Learning to Educators’ Roles
Use Case 4: Setting Clear Guardrails for AI Implementation
Use Case 5: Establishing AI-Specific Accountability Systems

Download the Use Cases as a PDF

Introduction

The rapid emergence of AI-enabled technology necessitates a structured and ethical approach to integration within K-12 education. The following district use cases, developed through the U-GAIN Reading R&D Center, translate five critical recommendations for EdTech from Integrating AI-Enabled EdTech in PK16 Education: Five Recommendations for State and District Leaders. The use cases offer real-world examples for how school districts can responsibly operationalize AI adoption and offer insights for building capacity, ensuring governance, and maintaining instructional integrity as AI tools become ubiquitous in classrooms.

U-GAIN Reading seeks to ensure that the adoption of generative AI is grounded in the Science of Reading to accelerate learning for all students, including striving readers and multilingual learners. The following five use cases focus on broad AI adoption strategies to leverage foundational insights into the eventual development of additional reading-specific resources and recommendations through U-GAIN Reading.

The first use case, Building Collaborative Networks, highlights how districts like Orcutt Union School District can leverage national partnerships to share best practices, pilot new tools, and align local infrastructure with cross-country insights.

The second, Developing Cross-Functional Teams & Procurement, offers how districts can strengthen governance through multi-disciplinary teams, as exemplified by Indian Prairie School District’s Generative AI Task Force, to inform ethical adoption decisions and policies.

The third use case, Tailoring Professional Learning to Educators’ Roles, illustrates how strategic investment in dedicated instructional technology leadership and role-specific professional development, as was seen in Allentown School District, can be used to build educator capacity.

Fourth, we examine Setting Clear Guardrails for AI Implementation, which involves developing principles and structured learning progressions, as seen in Indian Prairie School District’s strategy, to ensure AI is a complement to human instruction while prioritizing student safety and critical evaluation.

Finally, the fifth use case, Establishing AI-Specific Accountability Systems, details how districts can embed mechanisms for continuous evaluation and refinement, including drafting student-facing playbooks and family guides, as seen at Indian Prairie School District, to ensure responsible use and transparent communication over time.

The Five Use Cases

Use Case 1: Building Collaborative Networks

Recommendation 1: Establish clear priorities and collaborate with state, local, and district hubs to advance responsible adoption of AI-enabled edtech tools through collaboratives that share tools, policies, and evaluation practices.

Orcutt Union School District (California): Leveraging National Networks for AI Readiness

Orcutt Union School District (OUSD) adopted a forward-looking approach to leveraging national networks by joining Digital Promise’s League of Innovative Schools (The League), a national collaborative of districts focused on advancing educational innovation to achieve positive outcomes for every student. OUSD was selected based on demonstrated leadership, commitment to fairness, and an innovative vision for learning. To bridge these national insights with local classroom practices, OUSD also established an internal AI Leadership Academy composed of classroom teachers and Teachers on Special Assignment. Administrators have also been receiving professional development (PD) on AI tools to enhance efficiency and productivity.

Through this membership, and local infrastructure OUSD gained access to convenings and peer learning opportunities to share emerging practices and pilot new tools and educational technologies.

Participation in The League, along with building leadership capacity in the district, placed OUSD in a collaborative ecosystem where district leaders share strategies, practices, and lessons learned around emerging technologies like AI by:

  • Attending convenings featuring sessions on innovation and technology integration.
  • Joining work groups on student engagement, digital access, and pathways in computing that inform AI and tech strategies.
  • Accessing peer-generated resources and models, reducing duplication of effort.
  • Empowering a cohort of classroom teachers through the AI Leadership Academy that evaluate pedagogical “best uses” and ensure responsible AI adoption at the student level.

OUSD’s involvement in this network expanded the district’s capacity to responsibly explore AI tools, assess emerging practices, and adapt insights from across the country, aligning with best practice models for innovation networks and cross-district learning. This multi-tiered approach positioned OUSD as a district able to both contribute to and benefit from collective knowledge about AI in education.

Key Outcomes:
  1. Shared knowledge flows: OUSD leveraged insights from multiple contexts to anticipate challenges and accelerate effective tech use.
  2. District benchmarks: Participation helped establish benchmarks for responsible technology adoption including digital access and student engagement.
  3. Cross-district alignment: Network membership facilitated alignment with national frameworks for integrating AI ethically and equitably.
  4. Teacher-led Innovation: The AI Leadership Academy created a sustainable feedback loop, ensuring that high-level AI policy is grounded in practical classroom experience.
Conclusion:

Orcutt Union’s membership in a national innovation network illustrated how districts can operationalize Recommendation 1 at scale by placing themselves within ecosystems that supported shared learning and experimentation around emergent technologies like AI.

Use Case 2: Developing Cross-Functional Teams & Procurement

Recommendation 2: Strengthen the adoption and governance of AI-enabled edtech through cross-functional teams, vendor transparency, and cooperative purchasing.

Indian Prairie School District (Illinois): Task Force-Led Strategy for AI Implementation

Indian Prairie School District (IPSD) offered an example of the use of cross-functional teams to guide the ethical and strategic integration of AI tools in schools. In preparation for responsibly deploying AI-enabled technologies in classrooms for the 2024-25 school year, the district established a Generative Artificial Intelligence Task Force comprising teachers, administrators, principals, and district office representatives.

This task force included educators and district leaders from elementary, middle, and high schools. The core leadership was handled by key administrators, including the Assistant Superintendent, the Director of Innovation, and Instructional Specialists. Elementary, Middle, and High School Principals and Assistant Principals were also members of the working groups to ensure alignment with district goals.

The taskforce was structured into working groups focused on:

  • Creating a belief statement and guiding principles for AI.
  • Designing professional learning for educators.
  • Identifying AI resources and assessing future learning needs.
  • Determining how to optimize student learning with AI.

To align AI integration with Strategic Plan Priorities and update board policies regarding academic dishonesty, the task force presented progress and outcomes to the IPSD Board of Education. As a result, the cross-functional task force supported the district in developing guiding principles emphasizing the ethical, responsible use of generative AI. The task force also supported the creation of professional learning opportunities, which were co-designed with instructional and technology leaders to build capacity among educators before rollout.

Leaders and Instructional Specialists have since partnered with Innovation Ambassadors across all levels to build capacity district-wide and deliver specific, targeted professional learning. Finally, the taskforce supported development of guidance for how high schools could introduce AI tools to be aligned with privacy protections and instructional/pedagogical goals.

Key Outcomes:
  1. A shared district vision and policy foundations for generative AI, including policy updates endorsed by the Board of Education.
  2. A coordinated professional learning strategy across grade levels, expanded through Innovation Ambassadors and district office staff.
  3. Clear alignment between instructional goals and technology deployment.
Conclusion:

In alignment with Recommendation 2, IPSD’s structured task force shows how districts can operationalize cross-functional planning to ensure that AI adoption decisions are informed by a mix of technical, pedagogical, and leadership perspectives, mitigating risk and maximizing instructional value.

Use Case 3: Tailoring Professional Learning to Educators’ Roles

Recommendation 3: Invest in role-specific professional learning aligned to grade levels and disciplines to build educator capacity for teaching AI literacy across PK16 pathways.

Allentown School District (Pennsylvania): Technology Leadership Investments

In May 2025, the district approved the appointment of multiple instructional technology leaders, including a Director of Innovation and Instructional Technology as well as Supervisors of Instructional Technology. These roles were structured to advance digital learning, AI literacy, and tech-enhanced instruction.

Leaders were charged with guiding a districtwide vision for integrating modern learning technologies and supporting teachers in effectively leveraging emergent tools, including but not limited to AI-related platforms, to enhance instructional practice. The Supervisors of Instructional Technology provide support for classroom teachers and instructional coaches, creating a multi-tier professional learning ecosystem.

Simultaneously, the district’s Strategic Plan explicitly called for:

  • Providing schools with meaningful experiences in emergent and generative technologies.
  • Creating professional learning experiences that support teachers’ use of advanced technologies for student-centered learning.
    • Bridgeview Academy Of Health, Science, Innovation, and Technology recently hosted a professional development session focused on using Playlab AI to design custom classroom tools. Teachers explored how to create their own apps tailored to their instructional needs, ranging from simple tools like warm-up or question generators to more complex applications such as behavior tracking systems. The session emphasized creativity, efficiency, and practical implementation, empowering teachers to leverage AI to enhance student engagement and streamline classroom practices.
  • Developing systems for staff to design and share approaches to tech integration.

These actions helped create an infrastructure for ongoing learning contextualized by role (e.g., building leaders, classroom teachers) and connected across levels (e.g., district leadership, instructional coaches, classroom practitioners). By aligning professional learning investments with strategic aims around innovation and technology fluency, Allentown SD is striving to build capacity for educators to integrate AI-enabled tools responsibly and purposefully.

Key Outcomes:
  1. Recruitment of instructional technology leaders to drive innovation.
  2. Professional learning aligned with strategic goals around emergent technologies.
  3. Integration of AI and digital fluency components into broader tech learning frameworks.
Conclusion:

Allentown School District’s recent strategic investments in instructional technology leadership illustrated a model to support Recommendation 3. By investing in dedicated roles for tech leadership and contextualized professional learning, the district demonstrated how districts can build educator capacity to leverage AI tools in responsible ways to facilitate instruction.

Use Case 4: Setting Clear Guardrails for AI Implementation

Recommendation 4: Ensure responsible AI-enabled technology use in classrooms by setting clear guardrails, limiting the use of high-risk applications, and prioritizing tools that are accessible to all learners and aligned to instructional goals.

Indian Prairie School District (Illinois): Ethical and Responsible AI Integration

In preparation for the 2024–25 school year, Indian Prairie School District (IPSD) generative AI governance and strategy emphasized the ethical and responsible use of AI tools across grade levels. At the core of the district’s approach was the development of a belief statement and set of principles that positioned the use of AI as a complement, not replacement, to human instruction.

Guidance focused on enhancing creativity, personalization, and teacher and student capacity while maintaining educator oversight and safeguarding academic integrity in the following areas:

  • High school access to generative tools (e.g., ChatGPT, Google Gemini) aligned with privacy policies and age-appropriate usage.
  • Structured learning progressions where middle and elementary grades engage with AI ethically and analytically before direct tool use, supporting responsible adoption.
  • Teaching students explicitly about AI limitations, biases, critical evaluation of AI outputs, reinforcing and reinforcement of AI as a tool, not an author.
Key Outcomes:
  1. Policies that emphasize responsible and ethical use.
  2. Structured age-appropriate engagement with AI tools.
  3. Teacher and student learning around critical evaluation of AI.
Conclusion:

IPSD’s approach combined policy, pedagogy, and ethical instruction to support the creation of guardrails. These guardrails aimed to ensure that uses of AI enhanced learning without compromising student safety, academic integrity, or equitable access, aligning with Recommendation 4.

Use Case 5: Establishing AI-Specific Accountability Systems

Recommendation 5: Establish AI-specific evaluation and accountability systems through independent audits, role-based dashboards, and safeguards for privacy and access.

Indian Prairie School District (Illinois): Integrating AI Evaluation and Accountability

As part of preparing for the rollout of generative AI tools, the Indian Prairie School District (IPSD) AI task force built mechanisms for continuous evaluation and refinement. The taskforce developed a deployment strategy, including how the use of tools like ChatGPT and Google Gemini will be monitored to ensure alignment with instructional goals and ethical standards.

One of the four working group outcomes was to optimize student learning and seek student voice in the process. To do this, the task force drafted an AI Playbook incorporating AI best practices for students at the elementary, middle, and high school levels to guide responsible use. Student-focused wording was prioritized to ensure the language was accessible for students at all levels. The AI Playbook has been continuously iterated based on feedback and was made available, along with other technology resources, on IPSD’s website.

The task force met regularly to evaluate implementation outcomes and adjust strategies over time, embedding ongoing accountability structures to track:

  • How AI tool use affects teaching and learning.
  • Whether professional learning is achieving desired classroom impact.
  • How ethical considerations and student data protections were being upheld.
  • Clarity and impact of communication with families about student AI tool use.

IPSD’s Strategic Plan also focused on building a dialogue with parents and community members regarding AI. Planned engagement includes the development of an IPSD 204 Family Guide to AI to support families in navigating responsible use at home. Task force work specifically prioritized parent collaboration and the student experience.

This iterative process allowed the district to both comply with existing requirements (e.g., privacy protections and age-appropriate use), and also surfaced emergent challenges and opportunities as tools evolved. By designing the initiative with built-in review cycles and stakeholder oversight, IPSD created an accountability framework to evolve with the districts’ technology adoption.

Key Outcomes:
  1. Ongoing task force meetings to evaluate and refine AI integration.
  2. Monitoring and adjusting implementation based on classroom and leadership feedback.
  3. Transparent communication with schools about tool use and data implications.
  4. Development of student-facing AI Best Practices and a Family Guide to AI, supporting responsible use at school and home.
Conclusion:

IPSD’s iterative refinement and accountability infrastructure demonstrated how school systems can build AI-specific evaluation mechanisms, ensuring responsible use, real-world impact, and continuous improvement over time as outlined in Recommendation 5.

Recommended Citation

Prado, Y.; Ruiz, P.; Fusco, J.; Adler, K., Falck, B.; Smith, M.L. (2026, April). Operationalizing AI in K-12 Education: Five District Use Cases for Ethical and Strategic Adoption. Digital Promise. https://doi.org/10.51388/20.500.12265/290

Funding/Acknowledgments:

This project is supported by the Institute of Education Sciences, U.S. Department of Education through Grant R305C240040 to Digital Promise. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

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