According to Alpha’s principal, Joe Liemandt, the American K-12 education system is failing despite a $1 trillion annual investment due to a time-based model that lowers standards and creates knowledge gaps. By switching to a mastery-based system powered by AI tutors, students can learn up to 10 times faster. Liemandt’s “2-hour learning” model gives students their time back for life skills and sports, creating the motivation needed to unlock their academic potential and rebuild their self-belief.
The United States spends $1 trillion annually on K-12 education, yet academic performance, outside of the top 1%, is in steady decline. The core problem is the entrenched, time-based educational model where students advance by age rather than by mastering the material. This creates compounding knowledge gaps—a 7th grader may fail chemistry not because it’s too hard, but because they never mastered 4th-grade fractions. This systemic failure has created a narrative where students believe achievement is about inherent capability rather than effort.
The solution, as implemented and championed by Joe Liemandt at the Alpha school, is a complete rebuild of the educational model centered on principles from learning science. The Alpha model replaces the “teacher in front of a classroom” with personalized AI tutors. This allows for a mastery-based system where every student fills their knowledge gaps and learns at their own pace, achieving results up to 10 times faster. By framing academics as a “2-hour learning” block, the model returns the most valuable asset to students: their time. This freedom is a powerful motivator that drives engagement.
The results are transformative. Students who previously believed they “couldn’t do math” are now achieving top 1% scores on standardized tests. This model frees human guides (formerly teachers) to focus on mentorship, motivation, and teaching critical life skills. The vision, backed by Liemandt’s billion-dollar commitment, is to use AI as the “light microscope” for education, finally enabling these proven methods to scale affordably to a billion children worldwide.
The system is built on a flawed, time-based model. We advance students every year based on age, not on whether they’ve mastered the material. This creates compounding knowledge gaps, leading to a steady decline in performance as students are promoted with a weak foundation.
Switching from a time-based system to a mastery-based system where students must demonstrate proficiency before moving on. This ensures every child has a solid foundation. When powered by AI tutors, this approach is highly efficient and scalable.
Traditional classrooms are incredibly inefficient, with retention from lectures as low as 5%. An AI tutor provides a personalized, one-on-one lesson plan for each student, keeping them in the optimal learning zone (the “zone of proximal development”). It ensures they master basics before advancing, eliminating time wasted on remediation and allowing them to cover material much more quickly. An entire year’s math curriculum can be mastered in just 20-30 hours.
It can be a powerful “unlock.” For a student who believes they “can’t” succeed, an extrinsic motivator like a $1,000 reward can provide the initial push needed to do the work. Once they achieve a high standard they thought was impossible, their entire self-perception changes, creating a powerful intrinsic motivation that lasts long after the reward is gone. It’s the kindling that starts the fire.
Their role becomes more important, not less. Freed from grading tests and delivering repetitive lectures, they become guides and mentors. They focus on connecting with students one-on-one, providing motivational and emotional support, setting high standards, and teaching the life skills—leadership, teamwork, public speaking—that AI can’t.
It’s an educational approach where students progress based on their mastery of a concept, not on a fixed schedule. If you don’t understand fractions, you don’t move on to algebra. This prevents the knowledge gaps that plague the traditional system.
Students spend a focused, two-hour block on core academics using AI-powered apps. Once they complete their daily lessons to a mastery standard, their “school work” is done. The rest of the day is freed up for workshops, sports, and projects focused on life skills.
The model incorporates well-established concepts like Bloom’s 2 Sigma (the effectiveness of tutoring), the zone of proximal development (keeping content not too hard, not too easy), cognitive load theory (not overloading working memory), and active learning (testing over passive listening).
For decades, learning science has described a better way to teach, but it was impossible to implement at scale in a traditional classroom. AI is the instrument that finally allows us to measure what a student knows with precision and deliver a perfectly tailored, one-on-one lesson, making the theories of learning science a practical reality for every child.
Generative AI can create dynamic, endlessly engaging content tailored to each child’s interests. If a student loves baseball, their math problems will be about batting averages. If they love the musical Hamilton, their history lessons will be presented as song lyrics. This makes learning compelling and relevant, not a chore.
While the technology will continue to improve, the core principles can be implemented today. Even with current “static” AI-curated content, students can learn 3-5 times faster. The primary risk is not technological but sociological: getting society to adopt a fundamentally new model for schooling.
Because there was no scalable, cost-effective technology to deliver personalized, mastery-based tutoring. You couldn’t give every child a dedicated human tutor. AI is the first tool that can provide that one-on-one relationship to millions of students simultaneously.
Inertia and mindset. The “teacher in front of a classroom” model is all anyone knows. The biggest challenge is convincing parents, educators, and policymakers to embrace a complete rebuild of the school day, even if it’s proven to be vastly superior.
The cost is primarily in the AI compute, which is currently expensive. However, with the rapid development of on-device AI chips, the expectation is that within 3-5 years, a sub-$1000 tablet will have all the local processing power needed to run these AI tutors. The goal is to make this accessible to a billion kids, including through public and charter schools.
