Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2026 362168 Interaction design and programming in a smart space 1 3.0 3 ★ Chen Chia Hsing 4IDI3 0 0 Restricted to Students of This Class Only, Max. 15 Students

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	362168	Interaction design and programming in a smart space	1	3.0	3	★	Chen Chia Hsing	4IDI3	0	0	Restricted to Students of This Class Only, Max. 15 Students

Course Plan：

Instructor Chen Chia Hsing

Email biabobo@icloud.com

Last Updated 2026-06-03 12:13:15

Course Objective This course integrates Smart Space design, the Internet of Things (IoT), Information Visualization, UAV-based Spatial Observation, and Agentic AI technologies within the context of a Smart Campus. Students will learn how to collect, analyze, interpret, and utilize spatial and environmental data to develop intelligent interactive systems capable of autonomous sensing, reasoning, planning, and decision-making. The course covers multiple sources of spatial data, including environmental sensors, mobile device sensors, and unmanned aerial vehicles (UAVs). Students will explore techniques for data acquisition, cloud-based data synchronization, information visualization, and interactive interface design. Building upon these foundations, the course introduces Generative AI and Agentic AI concepts to develop Smart Campus Agents that can observe environments, understand contexts, plan actions, and provide intelligent services. Through hands-on projects, students will utilize Arduino Nano 33 BLE Sense Rev2, JavaScript, Firebase, GitHub, and Gemini API to design and implement smart space applications. The course emphasizes interdisciplinary integration of interactive design, smart technologies, spatial computing, and artificial intelligence. Adopting a project-based learning approach, students will develop practical skills in environmental sensing, data visualization, human-AI interaction, and intelligent system development. By the end of the course, students will be able to design and build Smart Campus Agents that support applications such as environmental monitoring, campus navigation, learning space recommendation, energy management, and smart healthcare services.

Course Schedule Week 1 (09/07) Topic: Course Introduction and Class Policies Content: Course objectives, grading policy, project overview, GitHub and development environment setup Week 2 (09/14) Topic: HTML and CSS Web Design Content: Building a smart space information webpage Week 3 (09/21) Topic: Interactive Programming with JavaScript Content: DOM manipulation, event handling, and dynamic data updates Week 4 (09/28) Topic: Teacher's Day Holiday Content: No Class Week 5 (10/05) Topic: Apache ECharts Data Visualization (I) Content: Bar charts, line charts, and pie charts Week 6 (10/12) Topic: Apache ECharts Data Visualization (II) Content: Dashboard design and data integration Week 7 (10/19) Topic: Firebase Cloud Database Applications Content: Data storage, synchronization, and retrieval Week 8 (10/26) Topic: Retrocession Day Makeup Holiday Content: No Class Week 9 (11/02) Topic: Midterm Project Presentation Content: Smart Space Dashboard Week 10 (11/09) Topic: University Anniversary and Campus Fair Makeup Holiday Content: No Class Week 11 (11/16) Topic: IoT and Arduino Nano 33 BLE Sense Rev2 Content: Sensor data acquisition and transmission Week 12 (11/23) Topic: Spatial Data Collection and UAV Applications Content: UAV-based spatial observation and data sources Week 13 (11/30) Topic: Generative AI and Gemini API Content: AI-powered data analysis for smart space applications Week 14 (12/07) Topic: Prompt Engineering Content: Context analysis and prompt design Week 15 (12/14) Topic: Agentic AI and Smart Space Agent Design Content: Observe–Reason–Plan–Act framework Week 16 (12/21) Topic: Agent Integration and Autonomous Analysis Content: Gemini API integration, external data sources, and agent development Week 17 (12/28) Topic: Final Project Preparation Content: System testing, project refinement, presentation preparation, and rehearsal Week 18 (01/04) Topic: Final Project Presentation Content: System demonstration and project presentation

Evaluation and grading policy Attendance and Participation: 20% Weekly Assignments: 20% Midterm Project Presentation: 30% Final Project Presentation: 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Use of self-made materials

The access to curricular consultation Email: biabobo@mail.ntut.edu.tw

Expanding teaching and resources ● 參與式創新教學 (Participatory & Innovative Teaching)
強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。
Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.
● 數位科技融入 (Digital Technology Integration)
運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。
Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）

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）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）

Note
Students are encouraged to bring their own laptops to class to avoid repeatedly setting up the development environment.

Instructor	Chen Chia Hsing
Email	biabobo@icloud.com
Last Updated	2026-06-03 12:13:15
Course Objective	This course integrates Smart Space design, the Internet of Things (IoT), Information Visualization, UAV-based Spatial Observation, and Agentic AI technologies within the context of a Smart Campus. Students will learn how to collect, analyze, interpret, and utilize spatial and environmental data to develop intelligent interactive systems capable of autonomous sensing, reasoning, planning, and decision-making. The course covers multiple sources of spatial data, including environmental sensors, mobile device sensors, and unmanned aerial vehicles (UAVs). Students will explore techniques for data acquisition, cloud-based data synchronization, information visualization, and interactive interface design. Building upon these foundations, the course introduces Generative AI and Agentic AI concepts to develop Smart Campus Agents that can observe environments, understand contexts, plan actions, and provide intelligent services. Through hands-on projects, students will utilize Arduino Nano 33 BLE Sense Rev2, JavaScript, Firebase, GitHub, and Gemini API to design and implement smart space applications. The course emphasizes interdisciplinary integration of interactive design, smart technologies, spatial computing, and artificial intelligence. Adopting a project-based learning approach, students will develop practical skills in environmental sensing, data visualization, human-AI interaction, and intelligent system development. By the end of the course, students will be able to design and build Smart Campus Agents that support applications such as environmental monitoring, campus navigation, learning space recommendation, energy management, and smart healthcare services.
Course Schedule	Week 1 (09/07) Topic: Course Introduction and Class Policies Content: Course objectives, grading policy, project overview, GitHub and development environment setup Week 2 (09/14) Topic: HTML and CSS Web Design Content: Building a smart space information webpage Week 3 (09/21) Topic: Interactive Programming with JavaScript Content: DOM manipulation, event handling, and dynamic data updates Week 4 (09/28) Topic: Teacher's Day Holiday Content: No Class Week 5 (10/05) Topic: Apache ECharts Data Visualization (I) Content: Bar charts, line charts, and pie charts Week 6 (10/12) Topic: Apache ECharts Data Visualization (II) Content: Dashboard design and data integration Week 7 (10/19) Topic: Firebase Cloud Database Applications Content: Data storage, synchronization, and retrieval Week 8 (10/26) Topic: Retrocession Day Makeup Holiday Content: No Class Week 9 (11/02) Topic: Midterm Project Presentation Content: Smart Space Dashboard Week 10 (11/09) Topic: University Anniversary and Campus Fair Makeup Holiday Content: No Class Week 11 (11/16) Topic: IoT and Arduino Nano 33 BLE Sense Rev2 Content: Sensor data acquisition and transmission Week 12 (11/23) Topic: Spatial Data Collection and UAV Applications Content: UAV-based spatial observation and data sources Week 13 (11/30) Topic: Generative AI and Gemini API Content: AI-powered data analysis for smart space applications Week 14 (12/07) Topic: Prompt Engineering Content: Context analysis and prompt design Week 15 (12/14) Topic: Agentic AI and Smart Space Agent Design Content: Observe–Reason–Plan–Act framework Week 16 (12/21) Topic: Agent Integration and Autonomous Analysis Content: Gemini API integration, external data sources, and agent development Week 17 (12/28) Topic: Final Project Preparation Content: System testing, project refinement, presentation preparation, and rehearsal Week 18 (01/04) Topic: Final Project Presentation Content: System demonstration and project presentation
Evaluation and grading policy	Attendance and Participation: 20% Weekly Assignments: 20% Midterm Project Presentation: 30% Final Project Presentation: 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Use of self-made materials
The access to curricular consultation	Email: biabobo@mail.ntut.edu.tw
Expanding teaching and resources	● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification. ● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities）
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） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
Note	Students are encouraged to bring their own laptops to class to avoid repeatedly setting up the development environment.