KOREAN
CHINESE
Master’s in AI Convergence
Master’s in AI Convergence
The Master’s in the AI Convergence Major prepares students to lead the next wave of AI Transformation (AX) by focusing on practical, industry-ready solutions. While traditional AI programs often prioritize purely theoretical and technical development, this program is designed for multidisciplinary "Convergent Talents" who can integrate artificial intelligence into specialized practical fields—such as Business, Finance, Healthcare, Bioinformatics, Logistics, Transportation, Media, Creative Industries, Smart Cities, Smart Manufacturing, Industrial Automation, and other industries—to solve complex, real-world problems through capstone and venture projects.
Overview
- IntakeSpring & Fall
- Duration2 years
- Credits27
- TypeFull-time
- FormatOffline
- LanguageEnglish
- IndustryAI Convergence
What makes our program unique?
Students gain actionable expertise through a curriculum built on multidisciplinary AI applications and sector-specific projects. We transform students into AX convergence experts by blending core theory with venture simulations and the AI Innovation Venture Studio. Taught by faculty with deep industrial experience, the program emphasizes practical skills over passive learning through mandatory Capstone Projects and direct corporate collaboration. Graduates finish with a professional portfolio of real-world AI solutions, ensuring they are uniquely prepared as AX Leaders for high-impact integration roles in the next wave of AI Transformation (AX).
Structure
The AI Convergence Major Master’s is structured around University Required Courses and Major Electives, completed within two years. Students must earn 24 credits (8 courses) for the thesis track, which includes a thesis and oral defense, or 27 credits (9 courses) for the non-thesis track, which requires a comprehensive examination. All students complete three core AI courses and select electives from sector-specific advanced topics, culminating in a mandatory, industry-linked Capstone and Industry Projects course.
| Credit Classification |
Thesis | Non-thesis |
|---|---|---|
| University Required Courses | 9 | 9 |
| Elective Courses | 15 | 18 |
Curriculum
Required Courses:
| Credit type | Name of the Course | Credits |
|---|---|---|
| University Required Courses | Principles of AI ▼
Course Description
This course introduces the fundamental concepts, history, and key technologies of artificial intelligence. Students will learn about various applications of AI, including machine learning, natural language processing, and computer vision, while understanding the design and implementation processes of AI systems. The course provides hands-on experience in implementing simple AI models. |
3 |
| Ethical AI / Decision-Making ▼
Course Description
This course examines the ethical, social, and governance implications of AI in business and society. Students will learn frameworks for responsible AI use, addressing bias, fairness, and compliance issues. The course also explores how generative AI can enhance strategic decision-making by evaluating risks, optimizing choices, and improving business outcomes. |
3 | |
| Generative AI for Business ▼
Course Description
This course is designed to help students from various majors easily understand the concepts and business applications of Generative Artificial Intelligence. It introduces the basic principles and key technologies of generative AI, exploring practical use cases such as text and image generation for business innovation. Students will also gain hands-on experience applying generative AI in marketing, customer analysis, and content creation, developing foundational problem-solving skills using AI tools. |
3 |
*Common Required courses; each student must register for a minimum of 6 credits as part of the graduation requirement.
Elective Courses :
| Study Area | Course | Credits |
|---|---|---|
| Elective Courses | Computer Vision for Industry ▼
Course Description
This course explores computer vision applications across industrial sectors, including object recognition, defect detection, visual inspection, and intelligent video analytics. Students learn to develop and deploy vision-based AI solutions that enhance efficiency, safety, and quality control in business and manufacturing contexts. |
3 |
| Business Intelligence & Data Analytics 1 ▼
Course Description
This course covers essential tools and techniques for extracting, visualizing, and interpreting business data. Topics include data mining, big data analytics, and business intelligence systems such as databases, data platforms, dashboards, and experimentation tools. Students will learn to translate data insights into strategic business decisions and develop skills in managing and utilizing large-scale data systems. Case studies and hands-on exercises provide opportunities to apply analytical methods to real-world business problems, emphasizing actionable insights and operational effectiveness. |
3 | |
| AI in Business, Finance & Customer Analytics ▼
Course Description
Covers AI applications in business intelligence, financial modeling, and customer behavior analytics. Topics include fraud detection, credit scoring, risk prediction, and personalized financial services. Students will implement predictive models to solve real business challenges. |
3 | |
| Human Computer Interaction for AI ▼
Course Description
Examines the interface between humans and AI systems. The course covers user experience (UX) design for AI, adaptive interfaces, conversational AI, and human-centered AI design principles. Students will analyze and prototype systems that improve usability, trust, and engagement in AI-powered environments. |
3 | |
| AI in Media & Creative Industries ▼
Course Description
Explores AI’s role in digital media, content creation, and entertainment. Students learn to apply generative AI for content generation, personalization, recommendation systems, and digital marketing optimization through A/B testing and audience analytics. |
3 | |
| AI for Healthcare and Bioinformatics ▼
Course Description
Introduces AI applications in medical diagnosis, healthcare management, and bioinformatics. Topics include disease prediction, medical image analysis, patient data modeling, and operational optimization for healthcare systems. |
3 | |
| Industrial Automation 1 ▼
Course Description
Introduces AI-driven robotics systems and industrial automation. Topics include sensor integration, robotic perception, intelligent control systems, and process automation. Students design and simulate robotic workflows to solve real-world challenges in manufacturing, logistics, and service environments. |
3 | |
| Capstone & Industry Projects 1 ▼
Course Description
A culminating, project-based course where students collaborate with real companies to define, analyze, and solve business problems using AI. Students collect and process data, develop AI models, and present implementable solutions that demonstrate measurable business impact. The project integrates technical, analytical, and strategic dimensions of AI. |
6 | |
| AI for Smart Manufacturing ▼
Course Description
Examines predictive maintenance, process optimization, and automation in smart factories. Students apply AI techniques using sensor data, computer vision, and quality control analytics to improve efficiency and reliability in manufacturing. |
3 | |
| AI for Supply Chain & Transportation ▼
Course Description
Focuses on applying AI to logistics, supply chain forecasting, and transportation systems. Students learn about route optimization, demand prediction, and decision-support models to enhance operational efficiency and reduce costs. |
3 | |
| Advanced Research Design and Methodology ▼
Course Description
This course focuses on independent, research-driven learning in preparation for the student’s thesis. Students engage in extensive literature review, the development of research questions, the selection of research methods, data collection, analysis, and thesis drafting. In addition, students complete written examinations, reports, projects, research papers, portfolios, or similar assignments designed to demonstrate competency in the stated learning objectives. All work is conducted independently outside the formal (directly supervised) classroom environment and is directly related to the student’s academic discipline. Research activities may include experiential learning, directed reading, or independent study under the supervision of a faculty advisor, with the research topic and scope approved by the department chair. |
3 | |
| AI for Smart Cities/Infrastructure ▼
Course Description
Covers AI technologies that enhance urban management systems, infrastructure, and public services. Students explore use cases in traffic optimization, energy management, environmental monitoring, and sustainable urban development. |
3 | |
| AI Innovation Venture Studio 1 ▼
Course Description
A hands-on, project-based course where students conceptualize, design, and prototype AI-driven startup ideas. Through startup simulations and product studios, teams develop viable AI business models and pitch real-world solutions using tools like no-code ML and LLM-based applications. |
6 |
*Courses are offered on a semester basis. In each semester, only a limited number of courses are opened, depending on demand and other practical considerations. As a result, students are not required to register for all courses at the same time.
Course Inquiries: Dr. Tumennast Erdenebold, Program Coordinator of the AI Convergence Master’s and PhD Programs (tumennast@wsu.ac.kr)
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