Model card · QLM category creation

Misconception Classifier Model Card

Misconception classification turns student reasoning traces into intervention signals teachers can inspect.

Research brief

Intended use

The classifier helps identify likely misconception patterns from tutor transcripts, simulation behavior, and student explanations.

Evidence captured

Evidence includes incorrect strategy patterns, explanation fragments, repeated stuck points, task attempts, and revision behavior.

Limitations and review

Misconception labels should be treated as decision support, not diagnosis. Teachers should review evidence before acting on individual-level claims.

Published by Quantum Learning Machines · 2026-06-02

Related QLM paths

Keep building the category map.

GlossaryMisconception GraphA misconception graph maps likely misunderstandings so tutoring and intervention can target the cause of an error.Category hubTeacherOSTeacherOS helps teachers identify misconceptions, prioritize micro-interventions, and use AI evidence without replacing teacher judgment.GuideMisconception-Based TutoringHow misconception-based tutoring identifies likely causes of errors and supports targeted intervention.

Next step

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