Mark Iannarelli

A third grader just scored a 5 on AP Calculus BC. The system that trained him contains no LLM. The core is a knowledge graph one man spent 250 hours encoding by hand, two minutes per edge. The platform is Math Academy. Its "AI" is an expert system that routes each student through nearly 3,000 math topics, from 4th grade arithmetic to the math behind machine learning. Every node, every prerequisite link, every weight was placed manually by a team of mathematicians. The weights alone took Justin Skycak a full month: 1,500 topics at the time, roughly 5 prerequisite links each, 2 minutes to estimate each one. 8 hours a day of pure encoding, done before ChatGPT existed to ease the load. Why go through that? Because the graph unlocks mastery learning, the closest thing education research has to a cheat code. In 1984, Benjamin Bloom showed that students with one-on-one tutoring perform two standard deviations above a regular classroom. The average tutored kid beats 98% of the lecture hall. Nobody could afford a tutor per child, so the finding sat in journals for 40 years. A prerequisite graph with a mastery gate is the workaround. The system always knows the exact next topic a specific kid is ready for, drills it until proven, then moves on. Zero time spent waiting for 29 classmates. That waiting is most of school. A year of classroom math is roughly 150 hours of instruction, and the majority goes to pacing, review, and re-teaching. Strip it out and a motivated kid covers six grade levels in one calendar year. The origin makes it better: this grew out of a math program at Pasadena High School where 8th graders were passing AP Calculus BC, back when the founders were still hand-grading the whole thing. The most effective education AI running today is a graph a few humans built by hand, one edge at a time.