misconception-detector

# Purpose Identify the exact type and root cause of a misconception, then design a correction loop that *replaces* the faulty model rather than re-explaining the same material. Surface misconceptions need a better example; structural and deep misconceptions need targeted deconstruction before reconstruction. # Activation - Learner makes the same conceptual error repeatedly. `check-understanding` or `challenge-generator` flagged a pattern. Learner's explanation reveals a plausible but incorrect mental model. Learner believes they understand but consistently applies it wrong. - **Skip if**: one-time execution mistake with no conceptual root. Concept hasn't been taught yet → `teach-concept`. Issue is environmental → `debug-teacher`. - **Routing**: run before `check-understanding` recheck when persistent error detected. Pair with `socratic-mode` for deep misconceptions. Log to `weak-area-tracker`. # Inputs - Learner's incorrect statement/reasoning, concept being misunderstood, prior error history, correct mental model. # Misconception Types - **Surface**: wrong terminology/label, underlying model partially correct → fix with clear definition + contrast example. - **Structural**: wrong causal model — knows vocabulary but has mechanism wrong → fix with step-by-step worked trace. - **Deep**: fundamentally wrong model conflicting with multiple related concepts → fix with `socratic-mode` to expose contradiction first, then correct. # Workflow 1. **Classify** — Determine type (surface/structural/deep) with supporting evidence. State classification before proceeding. 2. **Articulate** — Restate the learner's incorrect model precisely and without judgment. Confirm with learner that this represents their belief. 3. **Root Cause** — Identify what produced the misconception: overgeneralization, ambiguous terminology, bad analogy, missing prerequisite. 4. **Deconstruct** — Surface: correct definition + contrast. Structural: step-by-step mechanism trace. Deep: `socratic-mode` questions to expose contradiction, then provide correct model. 5. **Install Correct Model** — State the replacement model explicitly. Provide a concrete example that only makes sense under the correct model. Contrast with what the incorrect model would have predicted. 6. **Verify** — Ask learner to apply corrected model to a novel scenario with explanation. If error persists: escalate to `socratic-mode`. 7. **Reinforce** — Log to `weak-area-tracker`. Recommend a `challenge-generator` challenge targeting the corrected model. # Rules - DO: classify before correcting — type determines strategy. - DO: restate and confirm the learner's incorrect model before correcting it. - DO: provide an explicit replacement model, not just negation ("that's wrong"). - DO: require learner to apply corrected model to a novel case before closing. - DON'T: correct by repeating the original explanation louder or longer. - DON'T: assume the misconception — confirm with learner first. - DON'T: repeat the same correction strategy 3 times — if second attempt fails, escalate to `socratic-mode`. - DON'T: frame misconceptions as failures — they're signs of engaged learning. # Output Responses should contain: concept + observed error + pattern, misconception type with evidence, learner's incorrect model (restated), root cause, targeted correction, replacement model + contrast example, verification scenario, and reinforcement plan. Format naturally. # Checklist - [ ] Misconception type classified with evidence. - [ ] Learner's incorrect model restated and confirmed. - [ ] Replacement model explicitly provided with contrast example. - [ ] Verification requires novel application.