As AI becomes more common in K-12 education, district leaders are facing a practical question: How can schools use it in ways that create real value without compromising trust, privacy, or instructional focus?
Taking on this challenge is Newburgh Enlarged City School District, which is located about two hours north of New York City and serves 11,500 students across elementary, middle, and high school settings. Rather than adopting AI for its own sake, this district is grounding its AI implementation in clear, student-centered goals, strong safeguards, and a thoughtful implementation strategy.
Challenges
- Newburgh Enlarged City School District needed a better way to differentiate instruction, serving a diverse student population with a wide range of needs.
- They also needed to coordinate MTSS interventions while working to improve academic performance and avoid overburdening teachers.
- Additionally, the district wanted to adopt AI safely and strategically, with strong data privacy, reliability, and consistent management across campuses.
Solution
Results
- Teachers spend less time on manual planning and more time supporting students, strengthening data-driven collaboration and contributing to measurable progress.
- MTSS conversations became more meaningful and actionable, grounded in student data and focused on individual student needs.
Challenges
Newburgh Enlarged City School District serves a diverse student population, including multilingual learners, students receiving special education services, and students facing economic disadvantage, with a wide range of learning needs. The district has a strong commitment to improving academic performance, especially in ELA and math. With differentiation and better MTSS coordination as priorities, leaders needed a way to build educator capacity. As Joseph McGrath, Newburgh Assistant Superintendent, CIO, DPO, put it, “How do you serve such a diverse population in a meaningful way on a broad scale?”
One of the district’s biggest challenges was differentiation. On this topic, McGrath said, “Something we've struggled with is if you can give the students the right level of rigor, you can move them. But if it's too far advanced or it's too far below them, it's not a good use of their time.”
The reality was that students were experiencing both extremes: boredom when work is too easy and frustration when it’s too hard. As McGrath noted, searching for that ‘Goldilocks’ instruction—right level of rigor for each learner—has been no easy feat.
Teachers were being asked to meet a wide range of student needs all in the same classroom—more specifically, using student data to group students dynamically and adjust instruction in real time.
As McGrath explained, “We want differentiation, but inherent in that desire is the task for teachers to create, in a single lesson, five groups with differentiation, five times a day, five days a week. That’s 125 lesson plans.” He continued, “Then they are teaching students, assessing them, and repeating the process over again and again. That’s just out of reach. It always has been.”
The district faced a similar challenge with MTSS, striving to build a robust MTSS plan with the right data, and actually get it moving. Newburgh needed a more workable way to review student needs, develop supports, and move plans forward without overloading teachers and interventionists.
District leaders were also thinking carefully about how AI could fit into that work. As McGrath put it, “We didn’t just want some shiny, new toy.” For Newburgh, an AI rollout needed to solve real district problems in a way that would be simple, safe, and student-centered.
McGrath went on to say, “I care about kids. And if AI can help me get to helping kids, then I like AI.”
Solution
Newburgh built on its existing work with Panorama’s Student Success by introducing Panorama Solara in a secure, district-controlled environment. The goal was not to replace teacher judgment, but to make differentiation and intervention planning more manageable and efficient while keeping the work tied to district priorities.
For instructional planning, that meant helping teachers adapt existing lesson plans rather than replacing them. As McGrath explained, “A teacher may already have a lesson plan that they use year in and year out, and [with Solara] they can have it adjusted to meet the needs of the students in front of them.”
Most importantly, McGrath noted, “Solara elevates the power of the teacher, giving them more time to actually spend with the students.”
Solara is Panorama’s AI platform, built specifically for K–12 and grounded in district expectations, not generic outputs. In Newburgh, Solara includes student data, lesson plan formats, curriculum maps, rubrics, and New York State standards. Rather than expecting teachers to weave all of that together themselves, Solara helps them build differentiated lesson plans and dynamic groupings more efficiently.
Solara can also generate teacher-ready materials like vocabulary supports, sentence stems, and exit tickets, all aligned to district expectations. This has given Newburgh a way to support teachers with usable materials while continuing to refine the tool over time, rather than treating implementation as a one-time rollout.
In practice, the district was dealing with a process that asked teachers and interventionists to gather data, build plans, and keep supports moving, all while managing day-to-day demands. Panorama Student Success and Solara helped reduce that burden by making it easier to pull together the right information and turn it into stronger intervention plans. That gave teams a more workable way to review student needs, develop supports, and keep plans moving.
Just as important, Newburgh wanted to make sure the technology itself fit the district’s needs. This was not about adding AI for the sake of AI, but about building something that staff at every level could actually use with confidence. Prioritizing privacy and security, Solara was developed with a “walled garden” approach. This means secure access to student data and district context, along with pedagogical guardrails shaped by district and state expectations. And this helped the district make sure Solara reinforced, rather than disrupted, the way Newburgh wanted teaching and planning to happen.
Results
Today, the district’s work with Panorama is helping teachers spend less time on manual planning and more time supporting students. In classrooms, that has meant stronger support for differentiated instruction. For Newburgh’s MTSS, it has meant more substantive conversations built around student data and student needs.
“In meetings, now teachers and interventionists poke at the data and they go back and forth, grappling with the data points and what the student needs to learn best,” McGrath said. “And it does change that conversation considerably.”
McGrath also emphasized that those outcomes rely on both the quality of the data and the district’s willingness to keep refining the work. “We ask: ‘What kind of data are you pouring into this?’ Because you want the AI tools to really know your kids. And that is something that we work on all the time. We might say: ‘What about this data? Let’s upload that for next year so we can start differentiating for our incoming kindergartners.’”
Additionally, McGrath made clear that this is not a one-time setup: “This isn't one of those set it and forget it. You're constantly looking at your data and at your outcomes.”
That steady attention has helped Newburgh build a stronger foundation for data-driven collaboration and more consistent support for students. Just as importantly, the work appears to be contributing to meaningful district progress. As Superintendent Dr. Jackielyn Manning Campbell said, “We’re proud of the outcomes we’re seeing. We used to have seven schools in New York’s accountability system, and we’ve moved five out this year. That reflects intentionality, data use, and instructional focus.”
Next Steps
Newburgh is continuing to expand how it uses AI to support district and school-level decision-making. A growing part of that work is helping leaders respond more quickly and precisely to what the data is showing. As McGrath explained, they’re using integrated data, such as i-Ready, to help principals analyze trends, generate intervention strategies, create walkthrough tools, and even draft teacher communications quickly. The district is also using item analysis to help leaders more clearly identify strengths and gaps by region, grade, and school.
At the classroom level, Newburgh is piloting Panorama’s student-facing solution, which is designed to make writing practice more iterative. Instead of waiting until the next day for feedback on an open-ended response, students can get immediate rubric-aligned feedback and revise again in the same class period. Teachers can see where students started, where they improved, and what patterns are emerging across the class. That gives teachers quicker insight into how to adjust instruction.
Together, those efforts reflect how Newburgh is continuing to expand AI use in targeted, practical ways. As Dr. Manning Campbell put it, “AI is here to stay.” For Newburgh, that means continuing to use AI with purpose and care.
