It's Not Just Marking You Wrong. It's Figuring Out Why.
“Adaptive” is an overused word in ed-tech. This page makes the actual mechanism concrete — concrete enough that a skeptical parent or school buyer believes it's different.
The old way
“Incorrect. Try again.”
A wrong answer is treated as a single data point — one mark against a total. The fix is almost always the same explanation, repeated. Nothing about the underlying gap is understood, so nothing about the teaching changes.
The 2UtorTech way
“This maps to a gap in distributing across brackets.”
The same wrong answer is classified against a specific concept node. The system now knows why, and picks the next explanation to fit that gap — not the generic one everyone gets.
From a wrong answer to the right explanation
Capture the signal
Not just right/wrong — also slow, hesitant, or a common mis-step pattern.
Classify the gap
A subject-specific model maps the response to a concept node, not a score.
Choose the method
Pull a fitting analogy or worked example from an approved, on-syllabus method bank.
Update mastery
A lightweight per-concept estimate re-computes, shaping the very next question.
One student, one gap, two explanations tried
A student keeps missing a particular algebra step. Watch the method switch once the first approach doesn't land.
Gap identified: distributing a coefficient across a bracket
Attempt 1 · Analogy
Didn't land → switch method
Attempt 2 · Worked example
Landed → mastery updated to 80%
The moat isn't the model. It's the data flowing through it.
The language model is commoditized. The concept-gap classifier and mastery model — trained on real usage — are not. They get sharper with every session, which a general-purpose chatbot has no reason to ever build.
Get one wrong on purpose. Watch it change method.
This is the whole idea in miniature: a wrong answer isn't just marked. The tutor finds the gap and tries a different explanation until it lands.
Question
Simplify 3(x + 2)
Pick an answer to see the tutor respond.