隨著科技快速發展與數位教育政策推動，生成式人工智慧(generative artificial intelligence，GenAI)逐漸成為教師備課的重要輔助工具，然而教師如何有效運用 AI 並將其轉化為可落地之教學設計，仍缺乏具體方法與實務指引。
本研究以國小二年級數學教學為場域，採設計研究觀點，將生成式 AI 視為進入教師既有備課工作的科技媒介，探討教師運用 ChatGPT 輔助備課之設計歷程、教學轉化與實務應用，並建構生成式 AI 輔助備課工作流，以降低教師導入 AI 備課之門檻。研究採設計本位行動研究法(design-based action research，DBAR)，歷經準備規劃、教學實施與分析反思三個階段，蒐集 ChatGPT 對話紀錄、教學筆記、課堂錄影、學生學習成果、課程問卷及紙筆評量等資料，並透過多元資料交叉驗證進行分析。
研究結果顯示：(i) AI 輔助備課之首要條件在於生成內容能否「對齊課本」，其適配程度並隨教材屬性而異，圖像比重較高之單元需教師更多修正；教師所需之 AI 素養並非僅止於工具操作，而是結合教材分析、提示詞設計與內容檢核之綜合能力。(ii) 人機協作歷程中，教師逐漸由初期較依賴 AI 生成建議，轉變為更具主導性之教學設計者，能依專業知識與課堂需求對 AI 生成內容進行判斷、修正與教學轉化，此歷程並具體化為「準備—生成—執行」三階段 AI 備課工作流。(iii) 備課負荷呈現短期增加、長期收斂之特性，效率呈現U型發展。而量化結果顯示，AI 與教師設計之課程在學習成效上未達顯著差異，學生課程喜好則與平板載具使用具顯著關聯。
本研究之設計成果包含前述備課工作流，以及《AI 備課，今天就開始》教師入門手冊，其使用者定位為已察覺 AI 教學潛力、卻缺乏具體操作方法的教師。整體而言，生成式 AI 的價值並非取代教師，而在於透過教師專業判斷與轉化形成可持續迭代的備課流程；AI 融入教學的關鍵不在工具本身，而在於建立符合教學情境的設計方法與工作流，使 AI 成為支持教師課程設計與教學創新的協作夥伴。本研究結果亦可供數位教學政策推動之實務參考。

Driven by rapid technological advances and digital education policy, generative artificial intelligence (GenAI) has become an important tool for lesson preparation, yet teachers still lack practical approaches to transforming AI-generated content into implementable instructional designs. From a design research perspective, this study treats GenAI as a technological medium entering teachers’ existing preparation practices and constructs a GenAI-assisted workflow for second-grade mathematics. Design-based action research (DBAR) was conducted through iterative planning, implementation, observation, and reflection, with multiple data sources triangulated.
The results indicate that: (i) textbook alignment is a critical condition, and suitability varies by material attributes, with image-heavy units requiring more teacher revision; (ii) the teacher gradually shifted from relying on AI suggestions to actively judging, revising, and directing AI output according to professional knowledge and classroom needs, a process formalized as a three-stage “Preparation–Generation–Execution” workflow; and (iii) preparation workload initially increased and later decreased as effective strategies were internalized. Quantitatively, learning outcomes showed no significant differences attributable to AI-assisted lesson planning or tablet use, while course preference was significantly associated with tablet use.
The design outcomes include this workflow and the teacher guide AI Lesson Preparation: Start Today, intended for teachers who recognize AI’s potential but lack practical methods. GenAI’s value lies not in replacing teachers but in supporting iterative lesson preparation sustained by professional judgment, with implications for digital teaching policy.

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