KOREAN
CHINESE
Ph.D in Artificial Intelligence
Ph.D in Artificial Intelligence
Artificial Intelligence (AI) has become a core driver of innovation across business, industry, and society. To meet the growing demand for advanced AI research capacity, the Endicott College of International Studies (ECIS), Woosong University offers the PhD of Science in Artificial Intelligence, a doctoral program designed to prepare highly qualified researchers, university faculty candidates, and AI leaders capable of conducting independent, high-impact scholarly work.
Overview
- IntakeSpring & Fall
- Duration3 years
- Credits36
- TypeFull-time
- FormatOffline
- LanguageEnglish
What makes our program unique?
The program provides a structured doctoral curriculum combining theoretical depth, applied research methods, and advanced AI specialization. Students complete university-required courses offered every semester, including Ethical AI / Decision-Making, Principles of AI, and Generative AI for Business, ensuring strong foundations in responsible AI development, core AI principles, and real-world AI applications
Structure
The program includes a rigorous sequence of department-required doctoral courses
distributed across six semesters. Early coursework strengthens doctoral research
competence through modules such as AI Research Methodology, Applications of Artificial
Intelligence, Advanced Natural Language Processing, and Intelligent Systems. Students
then advance to specialized training in Advanced Statistics, AI-Based Decision Making,
Intelligent Databases, and Advanced Machine Learning, developing the analytical and
technical expertise required for doctoral-level inquiry.
The later semesters emphasize deeper research specialization and AI system development
through courses such as Data Science, Algorithm Design, Cloud AI Solutions, and Deep
Neural Networks (Deep Reinforcement Learning), followed by higher-level themes including
AI Project Management, AI and Economics (Social Impact of AI), Data-Driven Decision
Making, and AI-Based Robotic Systems. Advanced doctoral topics include AI and Law,
Large Language Models (LLM), Social Media Analytics, AI System Design (Evaluation),
(Advanced) Data Mining, Artificial Intelligence Optimization, and Multi-Agent Systems
(Multimodal Learning). The program culminates in Thesis Research, supporting
independent scholarly contribution under faculty supervision.
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.
Curriculum
| Credit type | Semester | Name of the Course | Credits |
|---|---|---|---|
| University Required Courses | Every Semester | 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 |
| 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 | ||
| 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 | ||
| Department Required Courses | 1st semester | AI Research Methodology ▼
Course Description
This course provides a comprehensive overview of research methodologies specific to the field of artificial intelligence. Students will learn about various research designs, data collection techniques, and analytical methods used in AI research. The course emphasizes critical thinking and the evaluation of existing literature, enabling students to formulate research questions, design experiments, and analyze data effectively. Through hands-on projects and case studies, students will gain practical experience in applying these methodologies to real-world AI problems. |
3 |
| Applications of Artificial Intelligence ▼
Course Description
This course examines the diverse applications of AI across various industries. Students will analyze case studies in sectors such as healthcare, finance, and manufacturing, gaining insights into how AI technologies drive innovation and efficiency. |
3 | ||
| Advanced Natural Language Processing ▼
Course Description
This course offers an in-depth examination of advanced techniques in natural language processing (NLP). Students will study topics such as deep learning for NLP, sentiment analysis, and machine translation. Practical exercises will enable them to apply theoretical knowledge using contemporary NLP frameworks and tools. |
3 | ||
| Intelligent Systems ▼
Course Description
This course introduces students to the principles of intelligent systems, including autonomy, adaptability, and learning capabilities. Students will explore various intelligent system architectures and applications, enhancing their understanding of how these systems function in dynamic environments. |
3 | ||
| 2nd semester | Advanced Statistics ▼
Course Description
This course provides an in-depth understanding of advanced statistical theories and techniques. Students will study regression analysis, multivariate analysis, and Bayesian statistics, solidifying their foundation for data analysis and interpretation. |
3 | |
| AI-Based Decision Making ▼
Course Description
Focusing on decision-support systems that leverage AI, this course teaches students about data analysis, predictive modeling, and optimization techniques. Students will learn how to implement AI-driven decision-making processes in practical scenarios. |
3 | ||
| Intelligent Databases ▼
Course Description
This course covers the design and implementation of intelligent database systems utilizing AI technologies. Students will learn about data management, query optimization, and automated data processing, enhancing their database development skills. |
3 | ||
| Advanced Machine Learning ▼
Course Description
This course delves into advanced concepts and techniques in machine learning. Students will explore sophisticated algorithms, model evaluation, and optimization methods, along with hands-on projects using real-world datasets. Topics include ensemble methods, support vector machines, and neural networks, providing a comprehensive understanding of cutting-edge machine learning practices. |
3 | ||
| 3rd semester | Data Science ▼
Course Description
This course introduces the fundamental principles and techniques of data science. Students will learn about the data science lifecycle, including data collection, cleaning, analysis, and visualization. The course covers various tools and programming languages commonly used in data science, such as Python and R. Students will work on hands-on projects that involve analyzing real datasets to extract meaningful insights, enabling them to make data-driven decisions. Emphasis will be placed on statistical methods, machine learning techniques, and data storytelling to effectively communicate findings. |
3 | |
| Algorithm Design ▼
Course Description
This course focuses on the principles of efficient algorithm design and analysis. Students will learn about various algorithmic strategies and optimization methods, equipping them to tackle complex computational problems. |
3 | ||
| Cloud AI Solutions ▼
Course Description
Focusing on the development and deployment of AI solutions in cloud environments, this course covers cloud service models and data processing technologies. Students will engage in hands-on projects to implement AI applications in cloud infrastructures. |
3 | ||
| Deep Neural Networks (Deep Reinforcement Learning) ▼
Course Description
This course covers deep neural networks and reinforcement learning, focusing on their theoretical foundations and practical applications. Students will learn to design, train, and evaluate deep reinforcement learning models to solve complex problems, with hands-on projects to reinforce learning. |
3 | ||
| 4th semester | AI Project Management ▼
Course Description
This course equips students with essential skills for managing AI projects effectively. Topics include project planning, execution, risk management, and stakeholder communication. Students will engage in case studies and simulations to gain practical insights into successful AI project management. |
3 | |
| AI and Economics (Social Impact of AI) ▼
Course Description
This course examines the economic and social impacts of AI technologies. Students will analyze the economic value of AI, implications for the job market, and related societal issues, preparing them to understand the broader effects of AI on society. |
3 | ||
| Data-Driven Decision Making ▼
Course Description
This course emphasizes the importance of data analysis in decision-making. Students will learn statistical techniques and data visualization methods to develop skills for making informed decisions based on empirical evidence. |
3 | ||
| AI-Based Robotic Systems ▼
Course Description
This course focuses on the integration of AI technologies in robotic systems. Students will learn about robotic perception, decision-making, and control mechanisms, including path planning and autonomous navigation. Hands-on projects will allow students to design and implement AI-driven robotic applications. |
3 | ||
| 5th semester | AI and Law ▼
Course Description
This course explores the legal and ethical implications of AI technologies. Students will examine issues such as data privacy, algorithmic accountability, and regulatory frameworks. Discussions will involve case studies that highlight the intersection of AI and law, preparing students to navigate legal challenges in AI applications. |
3 | |
| Large Language Model ▼
Course Description
The Large Language Models (LLM) course explores the foundational concepts, architectures, and applications of large-scale language models in natural language processing (NLP). This course is designed for students interested in understanding the mechanics of LLMs and their impact on various domains. |
3 | ||
| Social Media Analytics ▼
Course Description
This course teaches analytical techniques for interpreting social media data. Students will study sentiment analysis, trend detection, and user behavior analysis to understand the implications of social media on society and marketing. |
3 | ||
| AI System Design (Evaluation) ▼
Course Description
Focusing on the design and evaluation of AI systems, this course covers system architecture, performance metrics, and testing methodologies. Students will learn how to create robust AI solutions that meet user requirements and industry standards. Case studies and practical applications will enhance their understanding of effective system design. |
3 | ||
| 6th semester | (Advanced) Data Mining ▼
Course Description
This course covers advanced data mining techniques for extracting valuable insights from large datasets. Students will learn about clustering, classification, and association rule mining, with practical projects that apply these techniques to real-world data scenarios. |
3 | |
| Artificial Intelligence Optimization ▼
Course Description
This course addresses optimization techniques specifically for AI models. Students will study various optimization algorithms, loss functions, and hyperparameter tuning methods, applying these concepts to enhance the performance of AI systems. |
3 | ||
| Multi-Agent Systems (Multimodal Learning) ▼
Course Description
This course explores the concepts and applications of multi-agent systems, emphasizing cooperation and competition among agents. Students will also learn about multimodal learning techniques to process diverse data types effectively. |
3 | ||
| Thesis Research ▼
Course Description
This course is an area of study or research necessitating a high level of self-directed learning. This learning requires students to read, conduct research, complete written examinations, reports, projects, research papers, portfolios, or similar assignments that are designed to measure competency in the stated educational objectives. The work will be related to an academic discipline done outside of the formal (directly supervised) classroom. This research may be experiential, directed reading, or independent research supervised by a faculty advisor and approved by the chairperson of the department under which the course is listed. |
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. Hasan Tinmaz, Program Coordinator of the AI Master’s and PhD Programs (htinmaz@endicott.ac.kr)
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