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teach【开发与工程】AI knowledge transfer calibrated to learner level and needs. Models the learner's mental state, scaffolds from known to unknown using Vygotsky's Zone of Proximal Development, employs Socratic questioning to verify understanding, and adapts explanations based on feedback signals. Use when a user asks "how does X work?" and needs graduated explanation,…
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行構之知傳——察學者所知、自知至未知架橋、依適深釋之、以問驗解、隨饋而調、以練固之。
learn 既建深心模而今須有效傳乃用釋前,定學者所知與所須。
Learner Calibration Matrix:
┌──────────────┬────────────────────────────┬──────────────────────────┐
│ Level │ Explanation Pattern │ Check Pattern │
├──────────────┼────────────────────────────┼──────────────────────────┤
│ Novice │ Analogy-first. Connect to │ "In your own words, what │
│ (no domain │ familiar concepts. Avoid │ does X do?" Accept any │
│ vocabulary) │ jargon entirely. Concrete │ correct paraphrase. │
│ │ before abstract. │ │
├──────────────┼────────────────────────────┼──────────────────────────┤
│ Intermediate │ Build on existing vocab. │ "What would happen if │
│ (knows terms,│ Fill gaps with targeted │ we changed Y?" Tests │
│ some gaps) │ explanations. Use code │ whether they can predict │
│ │ examples that are close │ from understanding. │
│ │ to their existing work. │ │
├──────────────┼────────────────────────────┼──────────────────────────┤
│ Advanced │ Skip fundamentals. Focus │ "How would you compare │
│ (strong base,│ on nuance, trade-offs, │ X to Z approach?" Tests │
│ seeks depth) │ edge cases. Reference │ integration and judgment. │
│ │ source material directly. │ │
├──────────────┼────────────────────────────┼──────────────────────────┤
│ Misaligned │ Correct gently. Provide │ "Let me check my under- │
│ (confident │ the right model alongside │ standing — you're saying │
│ but wrong) │ why the wrong model feels │ X?" Mirror back to │
│ │ right. No shame signals. │ surface the mismatch. │
└──────────────┴────────────────────────────┴──────────────────────────┘
得:明圖學者所知、所須、橋連二者。察當具體足以擇釋之策。
敗則:若學者之階不明,問校之問:「君熟悉 [先決念] 否?」此非試——乃集資以更善教。若問感尷,默為中階而依其應調。
自學者所解者建路至新念。
得:架之路,各步建於前。學者不當迷,蓋每新念連於其所持。
敗則:若知與未知之缺太大,分為數小步。若無熟之錨(全新域),用學者所知他域之類比。若類比不全,認其限:「如 X,唯……」
於正階,以正模獻釋。
得:學者得釋,不淺(留其問)不深(過細而壓)。釋用其辭而連其境。
敗則:若釋太長,核念或埋——重述一句之題。若學者於釋後更惑,入點誤——試他錨或類比。若念實繁,認其繁,勿藏:「此有三部,相交。先始於一。」
勿假釋已成。以問顯學者之心模而試之。
得:察顯學者有可行心模或徒復釋。可行模能治變;憶之釋不能。
敗則:若學者不能答察問,釋未建正心模。此非其敗——乃教之饋。注何具未落而續第五步。
依察果調教法。
Adaptation Responses:
┌──────────────────┬─────────────────────────────────────────────────┐
│ Signal │ Adaptation │
├──────────────────┼─────────────────────────────────────────────────┤
│ "I think I get │ Push gently: "Great — so what would happen │
│ it" │ if...?" Verify before moving on. │
├──────────────────┼─────────────────────────────────────────────────┤
│ "I'm confused" │ Change modality: if verbal, show code. If code, │
│ │ use analogy. If analogy, draw a diagram. │
├──────────────────┼─────────────────────────────────────────────────┤
│ "But what about │ Good sign — they are testing the model. Address │
│ [edge case]?" │ the edge case, which deepens understanding. │
├──────────────────┼─────────────────────────────────────────────────┤
│ "That doesn't │ They have a competing model. Explore it: "What │
│ seem right" │ do you think happens instead?" Reconcile the two.│
├──────────────────┼─────────────────────────────────────────────────┤
│ Silence or │ They may be processing, or lost. Ask: "What │
│ topic change │ part feels least clear?" Lower the bar gently. │
└──────────────────┴─────────────────────────────────────────────────┘
得:教實時依饋而調。無釋同重——每試異法。調當感應,非機。
敗則:若數調皆敗,問題或為極基之先決,二者皆未識。明問:「釋中何感最大跳?」此常顯隱缺。
以施固解,非以復。
得:學者已施念至少一次,有資為續學。總錨之為來憶。
敗則:若練問太難,教跳太遠——簡之。若學者能行而不能釋因,有程知無念解——回第三步焦於「何以」非「何如」。
teach-guidance — 人之引變,導人成更善之師learn — 系之知獲,建可教之解listen — 深受之注,顯學者所述問外實之須meditate — 教課間清假,每學者新待之