misconception-detector

name	misconception-detector
description	Use when diagnosing a repeated conceptual mistake and designing a targeted correction loop to replace the faulty mental model.
version	1.1.0
authors	["edu-agent-skills contributors"]
tags	["assessment","misconceptions","correction","mental-models"]
status	stable

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.