Selected AI builds
Systems I've designed and shipped.
Projects I lead or built — each takes something from the AI frontier (fine-tuning, RAG, OCR, LLM evaluation) and turns it into a tool a classroom can actually use.
Flagship · founder & builderLive · Summer 2026
TheAIProgram.org · AI for High School Teachers
A complete learning platform I built from scratch — now live at TheAIProgram.org, with Iowa's first statewide AI course for teachers as its flagship (Summer 2026 cohort).
- Full LMS: enrollment, auto-graded exercises, a Canvas-exportable gradebook, discussions, and course certificates.
- In-browser Python (Pyodide) with CodeLens line-by-line execution — no install, no setup, built for beginners.
- A Socratic AI tutor on every step: a three-rung hint ladder (nudge → strategy → worked step) that deliberately withholds full solutions.
- Anchored to the UNESCO AI Competency Framework — teachers from any subject, statewide, no coding required.
TheAIProgram.orgPyodideSocratic AI tutorFull LMSUNESCO framework
LLM-as-judge · eval
AI Arena
An automated arena that grades student LLM, RAG, and GenAI projects — the open-ended assignments Kaggle InClass can't score.
- LLM-as-judge scoring with a primary evaluator and cross-family re-grades, so results don't inherit one model's bias.
- Live leaderboard and submission flow on FastAPI + Cloud Run, Firebase Hosting, and Firestore.
- Built to be reused across AI 2010, data-science courses, and Iowa teacher bootcamps.
FastAPICloud RunFirestoreClaudeGemini
Project lead · LLM fine-tuning
AI Python Tutor
I lead this project: an open-weight LLM fine-tuned for Socratic teaching — guiding questions first, never just the answer.
- Custom Socratic fine-tune for CS, DS, ML, and AI coursework — SFT + DPO with a pedagogical reward model that favors hint-first responses.
- Five-benchmark evaluation stack that scores teaching quality, not just answer accuracy.
- Trained and served on Nova, ISU's GPU research cluster.
SFTDPOReward modelsNova GPUOpen weights
Project lead · RAG + OCR
RAG Systems at Scale
A portfolio of retrieval systems I lead — from expert-finding over ISU's research archive to OCR pipelines that make scanned documents answerable.
- Expert-finding RAG over ~120,000 institutional-repository documents (OAI-PMH ingestion → vector index).
- OCR ingestion that turns scanned PDFs and archives into retrievable, citable knowledge.
- Embedding and inference jobs scaled across GPU nodes on ISU's Nova cluster; Qdrant, pgvector, and Gemini File Search evaluated head-to-head.
RAGOCRQdrantNova GPUn8n
Multi-model · LMS
Multi-Vendor AI Teaching Assistant
A course AI assistant that runs on several models at once — so students aren't locked to one vendor, and neither is the university.
- Deploys open models (Llama 4 Scout, Phi-4, gpt-oss, Gemma, Nemotron) on Azure AI Foundry for ~100 students per course.
- A Canvas-LTI RAG "living textbook" grounded in each course's own materials and owned by ISU.
- Multi-vendor by design (Claude / GPT / Gemini) to avoid single-vendor lock-in.
Azure AI FoundryCanvas LTIRAGOpen models
LMS automation · auto-grading
Canvas Course Copilot
Python tooling that auto-generates course content and quizzes, then wires up automated grading inside the LMS.
- Generates Canvas modules and QTI 1.2 quizzes straight from lecture slides, using Python + lxml.
- Gradescope ↔ Canvas grade-sync workflows that hold up at large-course scale.
- Cuts the manual work of authoring and grading hundreds of assessments.
CanvasQTI 1.2PythonlxmlGradescope
Workshop · enablement
Build Your Own AI Assistant
A hands-on workshop series teaching educators and students to stand up their own RAG assistants — local open-weight models vs. hosted APIs.
- Walks through the full local-vs-cloud tradeoff: cost, privacy, latency, and quality.
- Includes automation patterns with n8n and KNIME for data and grading pipelines.
- Delivered through ISU's Student Innovation Center and the D4 bootcamp.
RAGLocal inferenceAPIsn8nKNIME
