2026 UEN Tech Summit

Monticello Academy built a custom AI platform that scans handwritten student work, runs it through an OCR/LLM pipeline, and surfaces instructional insights for teachers — on a school budget. This is the real story: architecture decisions, FERPA compliance, what worked, and what we'd do differently.

What happens when a small K-12 school decides to build its own AI-powered student work analytics platform rather than wait for a vendor to solve the problem? This session tells that story from the ground up. Attendees will walk through how Monticello Academy architected StudentQuill — a cloud-native platform that captures handwritten student assignments via scanning, processes them through an OCR and multimodal AI pipeline, and delivers structured insights directly to teachers. We'll cover the full decision stack: why we chose Google Cloud Platform, how we evaluated OCR technologies for handwritten work, how we engineered prompts for rubric-aligned AI analysis, and how we designed the system to be fully FERPA-compliant from day one. Crucially, this is not a vendor pitch — it's a practitioner case study. Presenters will share the real architecture, real costs, real trade-offs, and real teacher feedback. We'll discuss what surprised us, where we hit walls, and the practical lessons any district or school can apply, whether they're building, buying, or evaluating AI tools for instructional use. Attendees will leave with a replicable architectural framework, a vendor evaluation rubric for OCR/AI tools, and a clear picture of what it actually takes to put AI to work for teachers — not just in theory, but in a live school environment.