Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	354764	Generative AI technology and applications	1	3.0	3	★	Chen Chia Hsing	4IxD3	30	0	互動三和互動外生三合開,限本系30人

Course Plan：

Instructor	Chen Chia Hsing
Email	biabobo@icloud.com
Last Updated	2025-12-24 13:44:45
Course Objective	生成式人工智慧（Generative AI, GAI）的快速發展，已深刻影響內容創作、互動設計與資訊系統的建構方式。本課程以實務導向為核心，聚焦於生成式 AI 在實際專案中的應用與系統整合，強調透過程式化方式調用生成式 AI API，將生成能力轉化為可控制、可重現且可整合的系統功能。 課程依循真實專案開發流程，先以 Google AI Studio 建立生成式 AI 的快速原型與 Prompt 設計基礎，理解文字、圖像與多模態內容的生成特性，接著透過 Python 與 Gemini API 將生成流程轉化為程式模組，實作可實際部署與延伸的生成式服務，最後結合 Ollama 與 LangChain，探討本地端與雲端模型混合應用的系統架構與實務取捨。 --------------------------------------------------------------------------- The rapid development of Generative Artificial Intelligence (GAI) has profoundly transformed content creation, interaction design, and information system development. This course adopts a practice-oriented approach, focusing on the application and system integration of generative AI in real-world projects. Emphasis is placed on programmatic interaction with generative AI through APIs, enabling students to transform generative capabilities into controllable, reproducible, and integrable system functions. Following a realistic project development workflow, the course begins with Google AI Studio to support rapid prototyping and prompt design, allowing students to understand the characteristics of text, image, and multimodal content generation. The course then moves to Python-based implementation using the Gemini API, where students develop modular and reusable generative AI services suitable for system integration and deployment. In the later stage, Ollama and LangChain are introduced to explore hybrid architectures that combine local and cloud-based language models, highlighting practical considerations such as system design, performance, cost, and data privacy. This course adopts a project-based learning approach in which students continuously develop and refine a single project throughout the semester. Weekly deliverables are incrementally integrated into the final outcome, which is expected to demonstrate real-world applicability, technical completeness, and presentation readiness. Through this process, students will also address key issues including generation quality, cost control, ethical considerations, and commercial feasibility, thereby developing the ability to effectively deploy generative AI technologies within information systems and digital services.
Course Schedule	第 1 週（2/23）：課程導論與專案主題定義（生成式 AI 專案說明、分組與應用情境設定） 第 2 週（3/2）：Google AI Studio 與 Prompt 設計基礎（文字生成） 第 3 週（3/9）：Google AI Studio 多模態應用（文字 × 圖像，Imagen） 第 4 週（3/16）：Google AI Studio 應用情境與生成式服務原型設計 第 5 週（3/23）：Python 串接 Gemini API（文字生成模組） 第 6 週（3/30）：Python 串接 Imagen API（圖像生成模組） 第 7 週（4/6）：清明節補假（停課） 第 8 週（4/13）：Python 多模態整合（文字驅動圖像的生成流程） 第 9 週（4/20）：期中整合報告（專案系統化成果展示） 第10週（4/27）：Ollama 與本地端大型語言模型實作 第11週（5/4）：LangChain 基礎與生成式 AI 工作流程設計 第12週（5/11）：LangChain 與 RAG（專案資料接入與檢索增強生成） 第13週（5/18）：雲端與本地模型混合架構設計（Gemini × Ollama） 第14週（5/25）：生成品質、倫理、可商用性、測試、成本控管與系統穩定性 第15週（6/1）：專案整合與展示介面設計 第16週（6/8）：專案優化、測試與穩定性調整 第17週（6/15）：期末成果發表準備與整合 第18週（6/22）：期末專案成果發表 --------------------------------------------------------------------------- Week 1 (Feb 23): Course introduction and project topic definition (overview of generative AI projects, team formation, and application scenarios) Week 2 (Mar 2): Google AI Studio and prompt design fundamentals (text generation) Week 3 (Mar 9): Google AI Studio for multimodal applications (text and image generation with Imagen) Week 4 (Mar 16): Application scenarios and generative AI service prototyping using Google AI Studio Week 5 (Mar 23): Python integration with the Gemini API (text generation module) Week 6 (Mar 30): Python integration with the Imagen API (image generation module) Week 7 (Apr 6): Tomb-Sweeping Day make-up holiday (no class) Week 8 (Apr 13): Python-based multimodal integration (text-driven image generation pipeline) Week 9 (Apr 20): Midterm project integration and presentation Week 10 (Apr 27): Ollama and local large language model implementation Week 11 (May 4): LangChain fundamentals and generative AI workflow design Week 12 (May 11): LangChain with RAG (project data integration and retrieval-augmented generation) Week 13 (May 18): Hybrid architecture design with cloud and local models (Gemini and Ollama) Week 14 (May 25): Generation quality, ethics, commercial feasibility, testing, cost control, and system stability Week 15 (Jun 1): Project integration and presentation interface design Week 16 (Jun 8): Project optimization, testing, and stability improvements Week 17 (Jun 15): Final project preparation and integration Week 18 (Jun 22): Final project presentation
Evaluation and grading policy	一、每週專案產出（40%） 學生需依每週課程進度完成對應之專案成果，包含 Prompt 設計、生成式 AI API 串接、生成模組實作、多模態整合與系統流程建構。各週產出將持續累積，作為期末專案的重要基礎。 二、期中整合報告（20%） 期中評量以專案整合展示為主，學生需說明目前已完成之系統功能、生成流程與技術應用，並透過簡報或示範方式呈現專案進度與整體架構。 三、期末專案成果（30%） 期末評量以完整專案成果為主，評估專案之功能完整度、生成內容品質、系統整合程度與實際應用可行性，並進行成果展示與說明。 四、課堂參與（10%） 評量學生之課堂出席情形、實作參與度、討論互動與對同儕專案之回饋表現。 --------------------------------------------------------------------------- 1. Weekly Project Deliverables (40%) Students are required to complete weekly project outputs aligned with the course schedule, including prompt design, generative AI API integration, implementation of generation modules, multimodal integration, and system workflow construction. These deliverables are accumulated as the foundation of the final project. 2. Midterm Project Integration (20%) The midterm assessment focuses on project integration. Students present the current status of their project, demonstrating implemented system functions, generative workflows, and technical approaches through presentations or demonstrations. 3. Final Project Outcome (30%) The final assessment evaluates the completed project in terms of functional completeness, quality of generated content, level of system integration, and real-world applicability. A final presentation and demonstration are required. 4. Class Participation (10%) Students are assessed based on attendance, participation in hands-on activities, engagement in class discussions, and constructive feedback provided to peers.
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 使用自編教材 / Use of self-made material
The access to curricular consultation	Email: biabobo@mail.ntut.edu.tw
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG12：負責任的消費與生產（Responsible Consumption and Production）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note	1. 本課程為技術實作導向，選課同學需具備基本 Python 程式語言能力。 2. 本課程將使用 Google AI Studio 與 Google Gemini API（Gemini、Imagen、Veo）進行實作，可能產生 API 使用費用。學生須自行控管使用量與付費。以 Imagen 4 Fast 為例，生成約 100 張圖片費用約為 2 美元，實際費用依使用情況而定。 1. Students enrolling in this course are expected to have basic Python programming skills. 2. This course uses Google AI Studio and the Google Gemini API (Gemini, Imagen, Veo), which may incur usage costs. Students are responsible for managing their API usage and pay. For example, generating about 100 images with Imagen 4 Fast costs approximately USD 2.