Joe Liemandt, principal of Alpha School and founder of Trilogy, lays out a first‑principles rebuild of K‑12: two hours of AI‑tutored, mastery‑based academics that are ~10× more efficient than the traditional, time‑based classroom, followed by life‑skills workshops students love.
The model is rooted in learning science: content is personalized to the student’s knowledge grade, not age. Alpha’s intake data show transcript grades can mask gaps: “A” students may range one year ahead to three behind, while “B” students are three to seven years behind. Alpha remediates quickly because a full grade level typically takes 20–30 hours to master.
The core motivator isn’t “learn faster” but “2‑hour learning” that gives kids their afternoons back for high‑engagement projects (leadership, entrepreneurship, public speaking, etc.). Lightweight incentives (e.g., “100 for 100”) reinforce mastery and overhaul self‑belief: in a mastery model, effort, not IQ, determines who gets 100s.
Liemandt’s team is productizing the approach via the Timeback platform and complementary ventures (e.g., a AAA game built on the learning engine, free to learn yet monetizable), with a goal to reach a billion kids and catalyze an ecosystem of builders and new school formats (including lower‑cost variants such as sports academies).
A student spends ~two hours with an AI tutor on personalized, mastery‑based academics; when complete, the interface “goes green,” and students transition to life‑skills workshops for the rest of the day.
Workshops in leadership, teamwork, grit, entrepreneurship, financial literacy, storytelling/public speaking, and relationship-building/socialization.
Alpha starts with diagnostic testing (knowledge grade), then assigns targeted lessons. A full grade level is usually 20–30 hours of mastery work; three years behind ≈ ~60 hours of focused study (e.g., a third hour per day).
Not necessarily. Incoming “A” students often range +1 to –3 grades; “B” students –3 to –7 grades behind on Alpha’s standardized diagnostics.
Liemandt states the engine supports top‑1% performance on standardized tests with the 2‑hour model.
The highest‑impact lever is “Time Back” (finishing academics to earn compelling afternoons). Alpha also uses lightweight incentives like “100 for 100” to catalyze mastery and change self‑perception.
Liemandt cites data that the median U.S. high‑schooler gains about 1 point (of 300) across four years—a symptom of time‑based progression and prerequisite gaps.
Alpha is the high‑end model, but the team is building lower‑cost formats (e.g., sports academies, higher guide‑to‑student ratios) while preserving the academic engine.
The model aims to deliver 2–3 hours/day academics and strong results (e.g., 1550+ SAT, AP 5s) while freeing afternoons for multi‑year projects.
A platform packaging the learning engine so builders can open schools or apps on top of it (Alpha afternoons are programmable). A AAA video game built on the engine is intended to be free‑to‑learn and massively scalable.
