Program Overview

Introduction to the AI Convergence Education Major

The AI Convergence Education Major is a program designed to equip students with the skills and knowledge necessary to integrate AI into educational settings. The program aims to foster expertise in AI-based education, enabling graduates to contribute to the development of innovative educational systems.

Educational Objectives

The primary objectives of the AI Convergence Education Major are:

1. To establish an environment where students can systematically acquire AI foundational knowledge.
2. To cultivate creative problem-solving skills through the convergence of various subjects.
3. To enhance the capabilities of in-service teachers in leading educational innovation.

Curriculum for 2020-2021

The curriculum for the 2020-2021 academic year includes the following courses:

• 4 (4th Industrial Revolution and Future Education)
• 4 (4th Industrial Revolution and Convergence Education)
• (AI) (Understanding AI Education)
• (Convergence Education Methods)
• AI (AI-Based Education Methods and Educational Engineering)
• (Understanding Data Science through Educational Data)
• (,) (Educational Programming Basics and Advanced)
• AI (AI Convergence Education Curriculum and Instructional Design)
• AI (AI Convergence Education Materials Research and Teaching Methods)
• AI (AI Ethics Education)
• AI (AI Education Service Model Design)
• (Solving Educational Problems using Data Science)
• (Educational Applications of Machine Learning)
• STEAM (STEAM Education Based on Computational Thinking)
• AI (AI Education through Educational Programming Languages)
• (Artificial Neural Networks and Deep Learning)
• 4 (Practical Applications of 4th Industrial Revolution Technologies in Education)
• /// (Practical Applications of Presentation, Discussion, Cooperation, and Project-Based Learning)
• / (Convergence Education Project for Elementary and Middle School)
• (Convergence Education Project Across Subjects)
• (Capstone Design Project)
• (Dissertation Research)
• (Special Research)

Course Descriptions

• (Dissertation Research): No description provided.
• (Special Research): No description provided.
• 4 (4th Industrial Revolution and Future Education): This course explores the future of education in the context of the 4th industrial revolution, focusing on the role of AI and other core technologies in shaping educational systems.
• 4 (4th Industrial Revolution and Convergence Education): This course delves into the concept and methods of convergence education, emphasizing the integration of subjects to foster creative problem-solving skills.
• (AI) (Understanding AI Education): Students learn about the content and methods of AI education, including the development of lesson plans and their evaluation.
• (Convergence Education Methods): This course covers the principles and methods of convergence education, with a focus on designing effective educational strategies.
• AI (AI-Based Education Methods and Educational Engineering): Students explore the application of AI in education, including the use of educational software, apps, and websites to support personalized learning.
• (Understanding Data Science through Educational Data): This course introduces students to data science, using educational data to illustrate the principles and applications of data analysis.
• (,) (Educational Programming Basics and Advanced): Students learn the basics and advanced concepts of educational programming languages, such as Scratch or Entry.
• AI (AI Convergence Education Curriculum and Instructional Design): This course focuses on the design of AI convergence education curricula and instructional strategies.
• AI (AI Convergence Education Materials Research and Teaching Methods): Students research and develop educational materials for AI convergence education and explore effective teaching methods.
• AI (AI Ethics Education): The course discusses ethical considerations in AI education, including the potential impacts of AI on society and education.
• AI (AI Education Service Model Design): Students design service models for AI education, considering the needs of various stakeholders.
• (Solving Educational Problems using Data Science): This course applies data science to solve educational problems, promoting data-driven decision-making.
• (Educational Applications of Machine Learning): Students learn about the educational applications of machine learning, including its potential to enhance learning outcomes.
• STEAM (STEAM Education Based on Computational Thinking): This course integrates computational thinking into STEAM education, fostering problem-solving skills.
• AI (AI Education through Educational Programming Languages): Students use educational programming languages to learn AI concepts and principles.
• (Artificial Neural Networks and Deep Learning): The course covers the basics of artificial neural networks and deep learning, exploring their applications in AI.
• 4 (Practical Applications of 4th Industrial Revolution Technologies in Education): Students explore the practical applications of 4th industrial revolution technologies in educational settings.
• /// (Practical Applications of Presentation, Discussion, Cooperation, and Project-Based Learning): This course focuses on the practical implementation of project-based learning, emphasizing presentation, discussion, and cooperation.
• / (Convergence Education Project for Elementary and Middle School): Students design and implement convergence education projects that integrate elementary and middle school curricula.
• (Convergence Education Project Across Subjects): This course involves designing and implementing convergence education projects that integrate multiple subjects.
• (Capstone Design Project): Students work on a capstone design project that applies the knowledge and skills acquired throughout the program to a real-world educational problem.

Faculty Members

The faculty members of the AI Convergence Education Major include:

• **** (Professor): Expertise in Educational Technology
• **** (Professor): Expertise in Sensing Engineering, UX, and AI
• **** (Professor): Expertise in Educational Philosophy
• **** (Associate Professor): Expertise in Science Education
• **** (Professor): Expertise in Electronic Computing
• **** (Professor): Expertise in Educational Technology and HRD
• **** (Professor): Expertise in Curriculum Studies
• **** (Professor): Expertise in Machine Learning and Data Mining for Healthcare
• **** (Professor): Expertise in Database and Spatial Information Engineering
• **** (Professor): Expertise in Visual Computing and Biometrics
• **** (Professor): Expertise in Educational Technology