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misconception-detector

Use when diagnosing a repeated conceptual mistake and designing a targeted correction loop to replace the faulty mental model.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(3)

  • SKILL.md3.9 KB
  • examples.md2.3 KB
  • README.md782 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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.

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