AI-Empowered
Education for All
Industrialize and personalize pedagogy to democratize access to world-class teaching.
Three Futures
The same classroom. Three different decades. Three different realities.
The Targeting System
A field allows for industrial-scale progress only when we can state, with mathematical precision: “This number is what success looks like.”
A Targeting SystemOutcome-based measurement infrastructure that optimizes for real-world value, not abstract metrics. does not care about abstract statistics like “accuracy on a dataset.” It optimizes for real-world value. For education, it measures one thing above all: did the student actually learn faster?
The measurable increase in skill for every hour spent studying. Not engagement minutes. Not click-through rates. Actual learning.
Retention FloorsThe minimum knowledge retention level required at 30, 60, and 180 days after instruction.
It's not enough to learn something today. The Targeting System demands that students still know the material 30, 60, and 180 days later.
Old Metrics vs. Targeting System
- ✕ Engagement minutes
- ✕ Software licenses purchased
- ✕ Teacher hours logged
- ✕ Standardized test scores (annual)
- ✕ Student satisfaction surveys
- ✓ Learning Gain per Hour (LG/H)
- ✓ Retention at 30/60/180 days
- ✓ Return on Cognitive Spend (RoCS)
- ✓ Personalization accuracy per student
- ✓ Real-time skill gap closure rate
The Maturity Model
Education's path from craft to commodity. Click each level to explore.
Repeatable
CURRENTSOPs for lesson plans. Teacher training standardized. Consistent quality within top schools, but wildly uneven globally. Still dependent on individual teacher skill.
The gap between L2 and L5 is not decades. With the right infrastructure — Targeting Systems, Data Trusts, and Outcome-Based Procurement — it is years.
The Industrial Intelligence Stack
Nine integrated layers required for AI to industrialize education.
The AI Tutor
Not a chatbot. A personalized, predictive, world-class teaching agent.
“The AI tutor is not a simple Q&A bot. It is an agent that acts as a personalized and predictive academic and life-skills teacher for every student.”— SolveEverything.org
Cognitive Profiling
Understands each student's unique cognitive profile: language skills, interests, and preferred learning style.
Contextual Analogies
Soccer analogies for sports fans. Anime illustrations for visual learners. Music theory for musicians. Every explanation is bespoke.
Global Research Lab
A/B tests teaching methods in real-time across millions of learners to discover exactly what works best for each type of brain.
Bespoke Content Generation
Generates millions of custom explanations, practice problems, lesson plans, and exams — for a billion students simultaneously.
Zero Marginal Cost
Makes a world-class personalized tutor available to every human on Earth for essentially zero cost. Education as a public utility.
Continuous Improvement
Every interaction feeds back into the system. The AI gets better at teaching every student, every day, automatically.
Think: Neal Stephenson's “The Diamond Age: A Young Lady's Illustrated Primer” — a book that teaches, adapts, and grows with its reader. Except this one is real, and it's for every child on Earth.
How It Works
From first visit to verified mastery in six steps.
Student Opens the Platform
A new student arrives. The AI begins with a warm, low-pressure conversation to understand their background, interests, and goals.
AI Assesses Current Knowledge
A short, adaptive pre-test maps the student's existing skills. No trick questions — just honest, calibrated measurement to establish a baseline.
Personalized Lesson Generated
Based on the cognitive profile, the AI generates a custom lesson plan. Soccer analogies for Jamal. Anime visuals for Maria. Each explanation is bespoke.
Smart Recitation & Practice
The student enters Smart Recitation mode — AI-supervised quizzing, spaced repetition, and real-time feedback. Powered by CompareGPT's study engine.
LG/H Tracked in Real-Time
Every session's Learning Gain per Hour is calculated. The teacher's dashboard shows skill gains, not just time spent. The AI optimizes relentlessly.
Retention Floor Tests
Automated re-assessments at 30, 60, and 180 days verify the student still knows the material. The system intervenes if retention drops below the floor.
The Public LG/H Dashboard
A tournament of ideas. The best teaching methods rise to the top.
Mockup data for illustration. Real dashboards will use verified, blinded assessments.
CompareGPT Education
Experience AI-powered education. Compare AI models, use Smart Recitation, and see personalized learning in action.
Smart Recitation
AI-supervised quizzing and Q&A on uploaded study materials. Spaced repetition built in.
Model Comparison
Side-by-side AI model comparison. Find which model explains concepts best for your brain.
Progress Tracking
Track your study progress across sessions. See your LG/H improve over time.
Guardrails & Safety
Power without accountability is dangerous. Every system needs brakes.
Right of Abstention
The student can pause or opt out of AI instruction at any time. No lock-in. No pressure. The human always has the final say over their own learning.
Human-in-the-Loop Override
Teachers remain final decision-makers for placement, advancement, and intervention. AI recommends; humans decide. The teacher's role shifts from instructor to mentor and guardian.
Parental Transparency
Real-time dashboards showing parents exactly what the AI taught, what it observed about their child, and what it recommended. No black boxes. Full visibility.
Decision Records for AI SystemsDecision Records for AI Systems — permanent, immutable logs of how AI made decisions about students.
Every decision the AI makes about a student — from selecting a lesson to recommending advancement — is recorded in a permanent, immutable log. These DR-AISDecision Records for AI Systems — permanent, immutable logs of how AI made decisions about students. records ensure that:
- ●Every algorithmic decision can be audited after the fact
- ●Bias can be detected and corrected through retrospective analysis
- ●Parents and regulators have a verifiable trail of AI behavior
- ●The system can be held accountable to the outcomes it produces
Outcome-Based Procurement
The market will re-align overnight.
Today, schools buy software licenses. They pay per seat, per year, regardless of whether students learn. This is like paying a hospital per patient visit rather than per patient cured. The incentives are misaligned.
Outcome-Based ProcurementBuying verified results (e.g., LG/H) instead of software licenses or seat-hours. flips this model. Schools buy verified Learning Gain per HourLearning Gain per Hour — the measurable increase in skill for every hour spent studying.. AI Copilots that provably beat the LG/H floor are procured automatically. Those that drift or fail to teach are automatically “downshifted” (removed).
Compute EscrowFunds locked until verified benchmarks are cleared; payment tied to results, not promises.
Funds are locked in escrow until verified benchmarks clear. If the AI tutor delivers the promised LG/H, payment releases automatically. If it doesn't, the funds return. This is not radical — it's how construction bonds have worked for centuries. We're simply applying the same accountability to education technology.
The future of education is
measurable, personalized, and free.
We are building the infrastructure to make world-class tutoring available to every human on Earth. Join the mission.
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