KOREAN
CHINESE
Ph.D.’s in AI Convergence
Ph.D.’s in AI Convergence
Ph.D.’s in AI Convergence Major
The Ph.D. in the AI Convergence Major prepares students to lead the next wave of AI Transformation (AX) by focusing on advanced 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 research and practical industrial application 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
- Duration3 years
- Credits36
- TypeFull-time
- FormatOffline
- LanguageEnglish
- IndustryAI Convergence
What makes our program unique?
Students gain actionable expertise through a curriculum built on multidisciplinary advanced 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 advanced practical and research 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 Leaders of AX for high-impact integration roles in the next wave of AI Transformation (AX).
Structure
The program is structured to transition students from foundational AI principles to specialized industrial application. To graduate, students must complete 36 credit hours, pass a comprehensive examination, and successfully defend a thesis.
| Credit Classification | Credit Hours |
|---|---|
| University Required Courses | 9 |
| Elective Courses | 27 |
| Dissertation |
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 |
*University Required Courses: Each student must complete all 9 required credits as part of the graduation requirements.
Major Elective Courses:
| Study Area | Course | Credits |
|---|---|---|
| Elective Courses | Business Intelligence & Data Analytics 2 ▼
Course Description
An advanced course covering comprehensive business intelligence and analytics techniques. Students learn to integrate multiple data sources, develop complex analytical models, and design advanced data pipelines. The course emphasizes applying data-driven insights to solve sophisticated business challenges and enhance strategic decision-making. |
3 |
| Advanced Human-Computer/AI Interaction ▼
Course Description
Explores advanced topics in the interface between humans and AI systems. Students study adaptive and intelligent interface design, conversational AI models, multi-modal interaction, and human-centered design strategies. The course emphasizes research-level approaches to designing AI systems that maximize usability, trust, and engagement in diverse AI-powered environments. |
3 | |
| Advanced AI in Media & Creative Industries ▼
Course Description
Emphasizes high-level AI applications in digital media and content creation. Students explore advanced generative AI methods, multi-channel personalization, recommendation systems, and analytics-driven content strategies. The course provides a framework for research-level exploration of AI in creative industries, optimizing content delivery and audience engagement. |
3 | |
| Capstone & Industry Projects 2 ▼
Course Description
An advanced, project-based course where students work on complex, real-world industry problems using AI. Students collect and process multi-source data, design sophisticated AI models, and present solutions demonstrating measurable business impact and operational excellence. |
6 | |
| Advanced Topics (Quantum Computing, AI Advanced Architecture) ▼
Course Description
A seminar-style course that explores frontier topics in advanced AI technologies. Students are introduced to quantum computing concepts, neuromorphic and accelerator architectures, and the future of high-performance AI computing. |
3 | |
| Industrial Automation 2 ▼
Course Description
Covers advanced robotics and industrial automation systems, including integration of intelligent control systems, optimization of complex workflows, and design of AI-driven industrial solutions. Students focus on research-level applications and strategies for high-impact industrial innovation. |
3 | |
| Advanced Computer Vision for Industry ▼
Course Description
Focuses on sophisticated computer vision methods applied across industrial sectors. Students study deep learning architectures, multi-modal image and video analysis, and deployment of large-scale AI solutions. The course emphasizes integrating vision-based AI systems into complex industrial and manufacturing contexts to improve efficiency, quality control, and operational effectiveness. Students learn to design AI pipelines capable of handling real-world industrial challenges. |
3 | |
| Advanced AI in Business, Finance & Customer Analytics ▼
Course Description
Focuses on complex business and financial analytics, including multi-source data integration, advanced predictive modeling, and research-level analytical techniques. Students design and implement AI-driven solutions to solve sophisticated challenges in business intelligence, finance, and customer analytics, emphasizing actionable insights and strategic decision-making. |
3 | |
| Recommenders & Personalization for Commerce ▼
Course Description
Focuses on the design and implementation of recommender systems for e-commerce and customer engagement. Students will explore algorithms for collaborative filtering, personalization, and customer retention optimization. |
3 | |
| Advanced AI for Smart Manufacturing ▼
Course Description
Focuses on sophisticated AI applications in manufacturing, including predictive maintenance, process optimization, and automation. Students study advanced sensor analytics, computer vision integration, and large-scale system deployment to improve efficiency, quality, and reliability in industrial environments. |
3 | |
| AI Innovation Venture Studio 2 ▼
Course Description
An advanced, project-based course focusing on independent design and implementation of AI-driven business solutions. Students develop integrated AI models, assess business and technological feasibility, and create high-impact prototypes for real-world applications, emphasizing innovation and strategic value creation. |
6 | |
| Advanced AI for Healthcare and Bioinformatics ▼
Course Description
Covers advanced AI applications in medical diagnosis, healthcare management, and bioinformatics. Topics include multi-modal healthcare data integration, deep learning for medical imaging, predictive modeling, and operational optimization for healthcare systems. Students develop AI solutions capable of addressing complex challenges in clinical, research, and healthcare operations. |
3 | |
| Advanced AI for Smart Cities/Infrastructure ▼
Course Description
Explores complex AI solutions for urban management, infrastructure, and public systems. Students learn multi-source data integration, predictive modeling, and advanced analytics for sustainable urban planning, traffic optimization, energy management, and environmental monitoring. |
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 | |
| Advanced AI for Supply Chain & Transportation ▼
Course Description
Covers research-level applications of AI in logistics, supply chain, and transportation systems. Students develop predictive models, optimization algorithms, and decision-support systems for large-scale operations, focusing on efficiency, cost reduction, and strategic planning. |
3 |
*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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