Status: Hackathon-ready prototype
Core idea: Identify the learner’s misconception, target it, and verify recovery.
Traditional learning systems are often optimized for correctness, scores, and topic-level progress.
But the same wrong answer can stem from different mental models.
Key question:
Can an AI tutor identify the reasoning error beneath an incorrect response and adapt the next learning step to that specific misconception?
TutorTrap creates a closed-loop diagnostic tutoring experience:
Challenge → Reason → Diagnose → Intervene → Recover
The system deliberately presents a plausible incorrect claim to elicit reasoning before surfacing the misconception.
Wrong is not the diagnosis. The misconception is.
TutorTrap treats misconceptions as the unit of adaptation.