Results & Impact

NROC came to me at a moment many organizations recognize: their team was already using AI tools in pockets, but there was no shared framework for deciding what was appropriate, no policy language to point to, and no way to evaluate whether AI-generated content met their standards. They needed someone who understood both the technology and the educational mission to build that infrastructure with them, not hand it to them from the outside.

Over the course of the engagement, I designed and implemented a complete AI governance framework that included ethical guidelines, a six-criteria decision matrix for evaluating AI use cases, five operational red lines the organization agreed not to cross, and a 26-page prompting handbook grounded in 44 research citations. I met with every department lead to assess their workflows, identify where AI could genuinely improve their work, and flag where it introduced risk. That hands-on assessment mattered. Governance documents that sit in a shared drive don't change behavior. Conversations with the people doing the work do.

The technical work went deep. I conducted a systematic AI vulnerability analysis, testing Claude against 384 of their learning objectives to identify specific risk vectors and recommend mitigation strategies that directly informed their product roadmap. I built custom AI-powered tools using Python, the Claude API, and MCP server architecture, including an accessibility pipeline spanning over 2,500 lines of code and integrations connecting Claude to R statistical software and their database systems for AI-assisted data analysis. These weren't proof-of-concept demos. They were production tools their team uses independently.

To make sure the work stuck, I developed an AI adoption strategy using Rogers' Diffusion of Innovations framework, translating governance principles into daily operational workflows. The goal was always the same: build internal capacity so the organization doesn't need a consultant forever. By the end of the engagement, staff were making confident, principled decisions about AI use on their own, grounded in a framework they helped shape.

The district brought me in to conduct a comprehensive curriculum review across their course offerings. That's a common starting point, but what made this engagement different was what came after. The review identified gaps in rigor, accessibility, and equity. It also surfaced a pattern I see often: the curriculum had been built incrementally over years by different people with different assumptions, and no one had stepped back to look at the whole picture. The courses weren't bad. They were uneven, and unevenness hits some students harder than others.

I provided detailed, actionable revision recommendations and then stayed on to support the revision process itself, working alongside their instructional team over the course of a full school year. This wasn't a report that got filed. It was hands-on collaboration: co-designing units, modeling how to integrate AI tools into the design process, and building the team's capacity to do this work independently. I developed ethical frameworks and implementation guidelines for AI integration that the district adopted across the organization, and I designed comprehensive curriculum frameworks that now serve thousands of students.

The equity piece was central, not an add-on. I led initiatives that measurably increased underrepresented student participation in advanced coursework. That outcome didn't come from a single intervention. It came from rethinking how courses were structured, what assumptions were baked into the assessments, and who the curriculum was designed to serve. When you change the design, you change who succeeds in it. The professional development I delivered throughout the year gave their staff the tools and the confidence to sustain the work after I was gone.