教師研究社群推動AI融入材料課程

為回應高等教育數位轉型趨勢，國立勤益科技大學化工與材料工程系於114年度成立「人工智慧於材料科學與工程概論課程中的整合與應用」教師專業成長研究社群，由黃逸仁副教授擔任召集人，集結校內外10位材料與工程領域教師，共同探索AI數位工具於材料科學教學中的應用實踐。

本次社群活動邀請業界專家－道成資訊股份有限公司張賜賢總經理蒞校分享，以「二氧化鈦工業應用」為案例，透過講座實際示範AI工具如何應用於學術資料搜尋、文獻回顧、簡報製作與圖像生成，協助教師理解如何將AI技術轉化為具體教材與教學資源。活動現場亦由社群召集人協助外籍教師共同參與實務操作，展現跨語言、跨領域的共學氛圍。

從講座到課堂實作：導入Granta Selector建立「資料驅動選材」學習模式

社群進一步將AI理念落實於課程設計，導入國際常用材料資料庫工具 Ansys Granta Selector，發展出三大教學主軸：資料驅動選材、指標導向材料決策、非程式型AI應用於材料分析教學。教師團隊製作教學影片與數位教材，帶領學生實際操作材料數據資料庫，學習以數據與指標進行材料特性分析與預測。

本社群的推動，正逐步形塑材料教育的新樣貌——從傳統理論學習，邁向以數據驅動、AI輔助決策的創新學習模式，為學生未來進入產業與研究領域預作準備。

Faculty Research Community Promotes the Integration of AI into Materials Science Education

In response to the trend of digital transformation in higher education, the Department of Chemical and Materials Engineering at National Chin-Yi University of Technology established a faculty professional development research community in 2025 titled “Integration and Application of Artificial Intelligence in the Introduction to Materials Science and Engineering Course.” Led by Associate Professor Yi-Jen Huang, the community brings together ten faculty members from materials and engineering fields, both within and outside the university, to explore practical applications of AI digital tools in materials science education.

As part of this initiative, the community invited an industry expert, Mr. Sih-Hsien Chang, General Manager of Triplets Information Co., Ltd., to share insights using the industrial applications of titanium dioxide as a case study. Through the lecture, AI tools were demonstrated in real time for academic data searching, literature review, presentation design, and image generation, helping faculty members understand how AI technologies can be translated into concrete teaching materials and instructional resources. During the session, the community leader also assisted an international faculty member in participating in hands-on practice, creating a collaborative learning environment across languages and disciplines.

From Lecture to Classroom Practice: Establishing a “Data-Driven Material Selection” Learning Model with Granta Selector

The community further incorporated AI concepts into course design by introducing the widely used international materials database tool, Ansys Granta Selector, and developed three core teaching themes: data-driven material selection, indicator-based material decision-making, and non-programming AI applications in materials analysis education. The teaching team produced instructional videos and digital learning materials to guide students in operating the materials database, enabling them to analyze and predict material properties using data and performance indicators.

Through these efforts, the research community is gradually shaping a new paradigm in materials education—moving from traditional theory-based learning toward an innovative, data-driven, AI-assisted decision-making learning model that better prepares students for future careers in industry and research.

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