{
  "schema": "vedokrok.public-item.v1",
  "release_id": "MHC-RPUB-20260920-75ad787a",
  "url": "/knowledge/fade-help-when-performance-earns-it",
  "id": "MHC-D-RESEARCH-9004",
  "version": "0.1.0",
  "title": "Fade help when performance earns it",
  "summary": "Support should leave because evidence changed, not because the lesson reached page ten.",
  "kind": "protocol",
  "body": "Begin with enough guidance to produce a correct model of the task. Remove one layer of assistance after repeated accurate use of the relevant decision or procedure, then check independent performance on a fresh case. If the check collapses, restore the specific missing support rather than restarting everything.",
  "limits": [
    "Prior knowledge is domain-specific. Independence in one class of cases does not justify removing support from a new or higher-risk task."
  ],
  "topics": [
    "union-skill-acquisition-career-dominant-focus"
  ],
  "intents": [],
  "source_ids": [
    "RS-6958B9284DD609A1",
    "RS-05CB28C0C7CF56B3"
  ],
  "evidence": [
    {
      "claim": "A 2025 meta-analysis found that lower-prior-knowledge learners benefited more from higher instructional assistance, while higher-prior-knowledge learners benefited more from lower assistance.",
      "source_id": "RS-6958B9284DD609A1",
      "role": "supports",
      "note": "The effect is moderated by domain and educational status, and does not prescribe a single assistance threshold.",
      "locator": "Abstract and highlights"
    },
    {
      "claim": "A meta-analysis of adaptive training found that effects varied by instructional intervention, with adaptive difficulty showing the strongest results among the examined categories.",
      "source_id": "RS-05CB28C0C7CF56B3",
      "role": "supports",
      "note": "The literature was heterogeneous and much of the context was military training; this does not validate every adaptive-learning product.",
      "locator": "Abstract"
    }
  ],
  "use_when": [
    "When a learner succeeds with examples, hints or AI assistance but independent performance is the real goal."
  ],
  "avoid_when": [
    "Prior knowledge is domain-specific. Independence in one class of cases does not justify removing support from a new or higher-risk task."
  ],
  "example": "An AI coding assistant first suggests the test structure; later the developer writes the test plan unaided and uses the assistant only to critique edge cases.",
  "check": "The learner can perform the targeted step on a fresh case without the removed support.",
  "steps": [
    "Identify the assistance currently carrying part of the task.",
    "Check which step the learner can already perform correctly.",
    "Remove one support layer on a fresh case.",
    "Restore only the missing support if performance breaks."
  ],
  "sources": [
    {
      "id": "RS-6958B9284DD609A1",
      "title": "A cornerstone of adaptivity – A meta-analysis of the expertise reversal effect",
      "url": "https://www.sciencedirect.com/science/article/pii/S0959475225000660"
    },
    {
      "id": "RS-05CB28C0C7CF56B3",
      "title": "Adaptive training instructional interventions: A meta-analysis",
      "url": "https://pubmed.ncbi.nlm.nih.gov/39083372/"
    }
  ],
  "relations": [
    {
      "from": "MHC-D-RESEARCH-9004",
      "to": "MHC-D-RESEARCH-9005",
      "type": "compare_with",
      "url": "/knowledge/restore-help-when-errors-stop-teaching-you-anything"
    }
  ],
  "collections": [
    {
      "id": "RC-EA04F47374E04549",
      "title": "Learn new skills, turn them into career evidence, and protect the main development edge",
      "url": "/collections/learn-new-skills-turn-them-into-career-evidence-and-protect-the-main-development-edge"
    }
  ]
}