AI 讓資深專家實現「數位反超」：跨世代臨床教師的視覺化備課賦能實踐
講者/Speaker： 李明芬, Ming-Fen Lee
背景與目的： 在醫療臨床教學中，資深專家擁有珍貴的「隱性臨床智慧」，卻常因缺乏排版能力而面臨「設計能力高牆」。傳統觀點往往將年輕世代視為數位原住民，資深教師則為數位移民。本教學應用教案旨在打破此「數位落差」迷思，探討以「邏輯駕馭 AI」的工作流，賦能資深臨床教師，實現跨世代的「數位反超」。
方法： 本教學應用教案提出建構於多媒體學習認知理論 (CTML) 之 AI 視覺化備課模組。包含三大階段：(1) 邏輯梳理：運用 ChatGPT 或 Gemini 將發散的臨床文字轉化為結構化脈絡；(2) 視覺設計：透過 Napkin.ai 將文本一鍵轉譯為專業流程圖；(3) 數位助教：利用 HeyGen 生成個人化教學影片。本教學應用教案針對 174 位跨職類醫療人員進行實證數據評估。
結果： 量化數據顯示，資深教師的「AI 自我效能感」飆升，成長幅度達 64.19%，超越年輕世代的 49.6%。在「流程圖設計能力」上，資深專家（7.17分）不僅跨越技術鴻溝，更「反超」年輕世代（6.80分）。此外，備課時間縮減逾 50%；資深教師整體滿意度高達 9.64 分，學生對教材的「臨床實戰價值」亦給予 9.78 分極高評價。
結論： 教案證實 AI 是醫學教育中的「數位平權器」。當技術門檻被移除，教學品質的競爭回歸「內容深度」。資深專家憑藉深厚領域知識，透過 AI 產出更精準的教材，消弭了世代技術壁壘，完美實現醫學教育視覺設計的民主化與數位反超。
Background & Objective: Senior clinical experts possess invaluable "tacit clinical wisdom" but often face a "design-ability barrier" due to limited software skills. Traditional views stereotype younger generations as "digital natives" and seniors as "digital immigrants". This teaching application plan aims to dismantle this "digital divide" myth. By introducing a "logic-driven AI" workflow, it empowers senior teachers to achieve cross-generational "digital overtaking".
Methods: This teaching application plan proposes an AI-integrated visual lesson preparation module based on the Cognitive Theory of Multimedia Learning (CTML). The workflow includes: (1) Logic Structuring: Using ChatGPT or Gemini to transform clinical text into structured contexts; (2) Visual Design: Employing Napkin.ai to translate text into professional flowcharts; (3) Digital Assistant: Using HeyGen for personalized teaching videos. This teaching application plan evaluated 174 interprofessional healthcare providers pre- and post-course.
Results: Senior teachers' "AI self-efficacy" surged by 64.19%, outpacing the younger generation's 49.6% growth. In "flowchart design skills," senior experts (7.17) successfully "overtook" digital natives (6.80). Preparation time was cut by over 50%. Senior teachers reported an overall satisfaction of 9.64, while students rated the materials' "workplace impact" at 9.78/10.
Conclusion: This teaching application plan confirms AI as a "digital equalizer". By removing software hurdles, teaching quality returns to "content depth". Armed with domain knowledge, senior experts leverage AI to produce precise materials, eliminating generational technology barriers and realizing the democratization of visual design in medical education.
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