大三必修課機械設計(二)的AI導入設計
講者/Speaker： 袁長安, Cadmus Yuan
本教學設計以大三必修課「機械設計（二）」為場域，探討如何將生成式人工智慧導入工程設計教育。其出發點在於，AI 技術發展速度已超越傳統課程更新節奏，若僅透過選修課或單次工具示範，難以有效建立學生普遍性的 AI 素養。因此，本課選擇以必修課作為制度化導入平台，將 AI 使用結合機械設計中的需求分析、成本考量、限制條件、風險辨識與設計驗證，使學生不僅學會使用 AI，更能理解其在工程判斷中的適用邊界。
本課程提出四層級漸進式架構：首先建立設計共同語言與工程化描述能力；其次導入負責任 AI 使用規範，強調提問、求證與思索的循環；第三，以可驗證之機械元件設計任務訓練學生檢核 AI 產出；最後推進至複雜系統議題，培養跨域整合與設計取捨能力。同時，本課採用報告組、提問組與獨立組三軸輪替機制，使學生分別承擔論證、質疑與評量責任，提升大班分組討論品質與同儕評量信度。
初步成效顯示，學生能在課程前期掌握顧客需求分類與成本策略等基礎概念，並逐漸由單純尋找答案轉向主動提問、查證與修正。質化觀察亦顯示，學生開始將 AI 視為支援設計探索與問題分析的工具，而非取代工程判斷的答案來源。本教學設計的核心價值，在於將 AI 從工具操作提升為工程設計思維、學習責任與決策能力培養的一部分，為 AI 時代的機械設計教育提供一套可實施、可觀察且可評量的課程模式。
This instructional design explores the integration of generative artificial intelligence into Mechanical Design II, a required junior-level engineering course. The motivation arises from a growing tension in higher education: the pace of AI development now far exceeds the conventional cycle of curricular revision. If AI literacy is introduced only through electives or isolated demonstrations, many students may graduate without developing the minimum habits needed to use AI responsibly, critically, and effectively. This course therefore uses a required design course as a systematic platform for embedding AI into authentic engineering learning.
The course is organized around a four-level progressive framework. Students first establish a shared design language through the engineering description of customer needs, cost, constraints, risks, and verification. They then learn responsible AI use through a cycle of questioning, verification, and reflection. In the third stage, students apply AI to verifiable mechanical component design tasks, where AI-generated suggestions must be checked against textbooks, design standards, and engineering principles. Finally, students move toward more complex system-level design topics, where they must integrate multiple constraints and make defensible design trade-offs.
To support implementation in a large required course, the design also introduces a three-role rotation mechanism: presenting groups, questioning groups, and independent evaluating groups. This structure transforms group presentations from passive reporting into an accountable learning process involving argumentation, critique, and evaluation.
Preliminary observations suggest that students gradually shift from seeking direct answers to asking better questions, checking assumptions, and revising their reasoning. They begin to view AI not as a substitute for engineering judgment, but as a tool for exploration, comparison, and early-stage problem framing. The central contribution of this course design is its effort to move AI education beyond tool operation and toward the cultivation of design reasoning, learning responsibility, and evidence-based engineering judgment.
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