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	354774	Generative AI technology and applications	1	3.0	3	★	Chen Chia Hsing	4IDI3	9	0	Joint class for Interaction Design Year 3 and International Program Year 3, with a limit of 13 students from this program.

Course Plan：

Instructor	Chen Chia Hsing
Email	biabobo@icloud.com
Last Updated	2025-12-16 16:49:03
Course Objective	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	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	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 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. 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. For example, generating about 100 images with Imagen 4 Fast costs approximately USD 2. 3. Gemini API Pricing: https://ai.google.dev/gemini-api/docs/pricing?hl=zh-tw