Learning that builds on what came before.
The best teachers remember where each student is, what tripped them up last time, and how they learn best. AI tutors can do the same, but only if they have persistent memory. Hebbrix gives every student their own learning context that grows with every session.
What your AI tutor knows about each learner
Every session adds to the student's learning profile: their strengths, their gaps, the concepts that clicked, and the ones that need revisiting.
Opens where the student left off
"Welcome back. Last session we were working on u-substitution and you were finding it tricky to identify the right substitution. Want to do one more practice problem before we move on?"
Remembers what clicked
When the student understood derivatives through visualizations, the tutor remembers that. Next time it explains a concept, it reaches for a visual approach first.
Catches recurring mistakes
The chain rule issue has come up three times. The tutor knows this, and proactively asks "did you apply the chain rule here?" before the student submits their answer.
AI tutors that actually adapt to the individual learner
Every session builds on the last. Students don't re-introduce themselves. The tutor already knows where they are.
Strong on derivatives, weak on limits? The tutor adjusts time allocation based on the full learning picture, not just this session.
Recurring mistakes across sessions get noticed and addressed head on, not just corrected in the moment.
The knowledge graph tracks mastery across topics. Students see progress that actually means something, not just time spent in a session.
The Ebbinghaus forgetting curve in reverse. The tutor knows when a topic should be revisited based on when it was last practiced.
Collections let you scope memory per student, per subject, per class. One student, many subjects, all the context correctly partitioned.
AI tutors that remember every learner
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