For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AAI2021 | Machine Learning for Understanding Humans and Society | 3 | 6 | Major | Bachelor | 2-4 | Applied Artificial Intelligence | English | Yes |
| This course is designed to cultivate integrated competencies that combine data-driven reasoning, machine learning techniques, and critical understanding of human behavior and social phenomena. Aligned with the Human Track of the AI Convergence major, the course explores how machine learning can be applied to analyze individuals, groups, and societal patterns, while emphasizing a human-centered approach to AI. Using R as the primary analytical tool, students learn practical skills in data preprocessing, visualization, model building, and evaluation, all within the context of real-world human and social data. Throughout the semester, students work with datasets related to society, culture, media, digital platforms, and behavioral patterns. They apply key machine learning methods—including regression, classification, clustering, and dimensionality reduction—to investigate how algorithmic models can reveal insights about human actions and social dynamics. Beyond technical skills, the course incorporates discussions on algorithmic bias, fairness, interpretability, and the societal implications of predictive modeling, encouraging students to critically engage with the broader impact of AI systems. Through hands-on projects and iterative data assignments, learners formulate research questions, select appropriate models, interpret outputs, and connect analytical findings to human and social understanding. | |||||||||
| AAI3011 | Introduction to Natural Language Processing | 3 | 6 | Major | Bachelor | Applied Artificial Intelligence | English | Yes | |
| The concept, technique and model of natural fish processing are discussed. It deals with the theory and practice of natural fish treatment and analysis techniques, especially deep learning based natural fish treatment. It also learns creative design and development contents through projects related to natural fish processing. The main contents include word-embedding, sub-word, sentence-embedding programming, natural language-processing programming using CNN, and natural language-processing programming using DNN. | |||||||||
| BUS3045 | Business Analytics and Applications | 3 | 6 | Major | Bachelor | 2-4 | Business Administration | Korean,English | Yes |
| This course covers big data mining from the perspective of management. Therefore, the course title is business mining to represent that our main task is focused on the business analytics. In this sense, realms of the topics which this course deals with include, for example, ● AI theories and methods ● Business mining with big data analytics ● AI-based machine learning and deep learning methods ● Big data mining based on huge datasets collected from sharing economies and crowdfunding online companies ● Online review based affective computing by using deep neural networks ● Deep learning based business model development The course format combines lectures, exercises with either WEKA, R or Python. | |||||||||
| CLA7101 | AI-Assisted Research Methods for Linguistic Data | 1 | 2 | Major | Bachelor/Master/Doctor | Liberal Art | Korean | Yes | |
| AI-Assisted Research Methods for Linguistic Data is an interdisciplinary course designed for undergraduate and graduate students who wish to conduct research using linguistic data. Through step-by-step, hands-on activities, students engage in the entire research process—from identifying a research topic to collecting and preprocessing data, conducting experiments, visualizing results, and writing an academic paper—with the support of generative AI and a variety of AI-assisted tools. Upon completing the course, students will be able not only to independently carry out the entire process of linguistic data research but also to effectively select and utilize appropriate AI tools at each stage. They will also develop the ability to critically evaluate AI-generated outputs and apply them to their research in an informed, responsible, and independent manner. | |||||||||
| CON3043 | Digital Divide and Consumer Inequality | 3 | 6 | Major | Bachelor | 2-4 | Consumer Science | English | Yes |
| This course examines how digital technologies, including financial platforms, wellness apps, artificial intelligence systems, and algorithmic infrastructures, shape new forms of inequality in consumer life. By focusing on issues of access, usage, and outcomes, the course explores how digital divides emerge across socioeconomic, generational, and geographic lines, and how these divides influence individual well-being and collective participation in the digital economy.Students will engage with interdisciplinary theories and perspectives to critically analyze the drivers and consequences of digital inequality. The course further investigates how inequality is both reinforced and mitigated in key domains such as digital finance, health technology, platform governance, data justice, and sustainable consumption.Through readings, discussions, and case analyses, students will examine existing policy responses and consider alternative approaches for fostering a more equitable and inclusive digital future.The course is organized into five thematic modules: 1. Conceptual Foundations andTheoretical Frameworks 2. Cases of Inequality in DigitalConsumer Life 3. Consumer Rights and Risks inthe Digital Age 4. Policy and Governance Responses 5. Toward Digital Justice: Visionsfor the Future | |||||||||
| CON4015 | Data-Based Quantitative Research Methods | 3 | 3 | Major | Bachelor/Master | Consumer Science | English | Yes | |
| Data-Based Quantitative Research Methods is a methodological course that introduces the core principles of quantitative research and the procedures of data-driven empirical analysis used across the social sciences. The course is designed to help students understand the full process through which quantitative research formulates research questions, organizes and analyzes data, interprets statistical results, and ultimately derives evidence-based conclusions. Students will learn essential concepts in quantitative inquiry, including research design, variable measurement, sampling strategies, and assessments of validity and reliability. Through hands-on work with Stata, they will conduct key stages of empirical analysis such as data cleaning, descriptive statistics, exploratory data analysis, and regression modeling. This practical engagement will enhance their applied research skills. By the end of the course, students will be able to recognize data structures and patterns, interpret analytical outputs, and use empirical evidence to explain social phenomena. The course focuses on building a strong conceptual understanding of quantitative research and developing students’ capacity to apply data effectively across a wide range of social science research contexts. | |||||||||
| ENG2025 | Data Science for Language Analysis | 3 | 6 | Major | Bachelor | English Language and Literature | English | Yes | |
| This course introduces students to key methods and findings in data science for language analysis. We will begin the course by examining motivations for using big data (rather than just intuition) to understand and analyze language, culture and social change. We then introduce students to the key programing language Python and statistical methods/modules used to analyze language and social science data including pandas, numpy, and matplotlib. | |||||||||
| ENG4034 | Introduction to Digital Humanities | 3 | 6 | Major | Bachelor/Master | English Language and Literature | English | Yes | |
| In this course, you will explore some digital tools and technologies used in the humanities and study digital projects available in current humanities research. You will learn about the history of digital humanities, data-cleaning, data-visualization and mapping, website-management, and text-mining. The goal of this course is to give you a chance to expose yourself to a variety of possibilities emerging from the convergence between humans and artificial intelligence, developing before and after Generative AI. | |||||||||
| GER3041 | AI and Language Education | 3 | 6 | Major | Bachelor | 3-4 | German Language and Literature | English | Yes |
| This course explores the integration of Artificial Intelligence (AI) in German language education. It begins with a comprehensive examination of key AI technologies, including Natural Language Processing (NLP), Machine Learning (ML), and conversational AI chatbots. The course then combines theoretical analysis with practical application, focusing on how these technologies can be effectively utilized in the context of teaching German. Through hands-on projects, students will work with AI-based tools such as ChatGPT, Brisk Teaching, Suno, and VREW to design and implement innovative German language learning and assessment tools. The primary objective of this course is to equip students with the practical skills necessary to apply AI technologies in real-world German language teaching environments. In doing so, students will explore the potential of AI to drive innovation in language education, enhancing the quality of German language instruction and improving the overall learning experience for German learners. | |||||||||
| GLD3005 | Understanding of Public Finance | 3 | 6 | Major | Bachelor | 3-4 | Public Affairs | English | Yes |
| An economic analysis of government policy in market economies, examines efficiency and equity arguments for government intervention, economic theories of government decision making, and empirical evidence on government programs. Discussion of the economics of taxation, public expenditures, fiscal policy, budgeting, eudcation, and social security. Focus on public sector economics, taxation in efficiency and equity, expenditure structure, effects fiscal policy upon economic stability and growth, and evaluation of policy effectiveness. | |||||||||
| GSP4007 | Artificial Intelligence and Government | 3 | 6 | Major | Bachelor/Master | Public Administration | English | Yes | |
| This course aims to develop practical and analytical competencies for applying artificial intelligence (AI) in public administration and public policy by introducing core AI concepts and technical foundations and examining their applications in government and the public sector. The first half covers fundamental machine learning and deep learning methods-including supervised and unsupervised learning, regularization models (Lasso, Elastic Net), decision trees and ensemble methods (Random Forest), neural networks, recurrent neural networks (RNN), and support vector machines (SVM)-while the second half focuses on empirical studies and policy cases illustrating how these algorithms are used in governmental operations, public services, regulation, policy evaluation, and policy areas such as welfare, labor, and health. Throughout the course, students will engage in discussions on key issues at the intersection of government and AI, including decision-support systems, administrative efficiency, policy targeting, predictive analytics using public data, and concerns related to algorithmic bias and accountability. | |||||||||
| LIS3060 | Domain Analysis for Information Organization | 3 | 6 | Major | Bachelor | Library and Information Science | English | Yes | |
| This course aims to cultivate the ability to interpret and design knowledge structures through both qualitative and quantitative approaches to domain analysis. Students will develop a theoretical foundation for understanding the meaning, structure, and form of knowledge, as well as for classification, indexing, and information retrieval. Through the analysis of knowledge organization tools designed via domain analysis, students will also gain a critical perspective. | |||||||||
| PAD2019 | Strategic Planning and Performance Management | 3 | 6 | Major | Bachelor | 2-3 | Public Administration | English | Yes |
| The course aims to understand the foundation (concepts, models, operational principles) of strategic planning and performance management, develop practical capacity through analyzing various case studies and exploring viable future options, and improve research capacity by investigating the realities/data relating to strategic planning and performance management. | |||||||||
| PAD3020 | Policy Implementation | 3 | 6 | Major | Bachelor | 3-4 | Public Administration | English | Yes |
| A policy implementation studies has the shorter history than a policy making studies. Most people think a policy implementation carries out mechanically. But a policy implementation occupies extensive part of public administration in reality and unexpected situations make the its direction wrong. Therefore a policy implementation studies is very important. The lecture of a policy implementation seeks for below. - It is necessary that practical experiences and studies of policy implementation are reflected policy making. - It is necessary to understand policy implementation's participants - We will investigate various tendencies of theory and case studies to extend our knowledge of policy implementation | |||||||||
| PRS3013 | Public Conflic Management | 3 | 6 | Major | Bachelor | Population Change and Resilience Societies | - | No | |
| This undergraduate course focuses on the theory and practice of conflict management and collaboration for resolving public issues, particularly in the context of complex social problems arising from demographic changes. The course explores methods for resolving conflicts and reaching consensus in public policy development and program implementation processes. Students will enhance their ability to analyze conflicts involving multiple stakeholders and evaluate negotiation and mediation processes. As demographic changes accelerate, the conflicts between various stakeholders around public issues are becoming increasingly complex. This course is designed to strengthen students’ problem-solving capabilities in addressing today's multifaceted challenges by emphasizing the importance of collaboration and negotiation among public, private, and non-profit sectors. The course fosters an understanding that trust-based cooperation and mediation among stakeholders are essential for effective problem-solving in contemporary society. The course recognizes that social conflicts arising from demographic shifts are likely to become more frequent, and it is crucial for public sectors to address these challenges effectively. By integrating insights from various academic fields, this course adopts an interdisciplinary approach to improve problem-solving skills, enabling students to tackle complex social conflicts from multiple perspectives. The goal is to empower students to analyze and resolv | |||||||||