What is the Two Sigma Problem?
In the 1980s, educational psychologist Benjamin Bloom ran an experiment comparing traditional classroom teaching with two alternatives: one-on-one tutoring and mastery learning. The results were stunning. Students who received individual tutoring performed two standard deviations better than their peers — hence the "two sigma" effect.
The challenge Bloom posed to the world was clear: how do we deliver that level of impact to every student, not just the few who can afford personal tutors?
Why one-on-one tutoring works
- Immediate feedback: Misconceptions are caught and corrected before they calcify.
- Adaptive pacing: Learners linger on tough topics and breeze through familiar ground.
- Personal relevance: Examples and practice feel aligned with individual interests and goals.
How RABBI replicates and amplifies the effect
RABBI's tutor is always paying attention. It listens to your words, tracks your progress, and keeps a living map of your strengths. With that context, it can adjust in the moment and design sessions that feel supportive and deeply tailored.
- Context-aware planning: Every lesson starts by analyzing what you have already mastered and what should come next.
- Adaptive assessment: Quick checks surface confusion instantly. RABBI pivots the explanation without you having to ask.
- Memory-first reviews: The system schedules spaced refreshers so knowledge sticks.
- Encouragement baked in: A coach-like tone keeps you motivated even when the material gets dense.
Bloom wondered whether the two sigma effect could be replicated at scale. With modern AI, the answer is finally yes.
Accessibility at the core
Bloom's study highlighted the power of personalized learning — but historically, access was expensive. RABBI's free plan makes a real tutor-like experience available to more learners, while paid tiers scale up for deeper guidance, projects, and professional goals.
The new frontier
We are constantly training RABBI to read between the lines: to spot subtle cues in your responses, to suggest smarter practice, and to celebrate progress. The goal is simple: make the two sigma difference feel normal for every learner.