For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| HAI5010 | Cognition/Emotion and AI | 3 | 6 | Major | Master/Doctor | 1-4 | Human-Artificial Intelligence Interaction | - | No |
| This course addresses several issues on users’ cognitive and emotional characteristics in interacting with AI-based products/systems. Topics of the course include human information processing, decision-making models, and emotional design. The course emphasizes human perceptions/judgments and user cognitive performance/errors in interactive applications. At the end of the course, students are required to understand the current situations and limitations of AI systems and to find viable solutions to overcome them in users’ cognitive and emotional perspectives. | |||||||||
| HAI5011 | Analysis of IT corporate data and efficiency | 3 | 6 | Major | Master/Doctor | 1-4 | Human-Artificial Intelligence Interaction | Korean | Yes |
| In this class, we analyze the efficiency of companies with the financial data of IT companies. We study Data Envelope Analysis (DEA) and Stochastic Frontier Analysis (SFA) methodologies that are most widely used in enterprise efficiency analysis and compare efficiency with corporate financial data. Efficiency research is one of the key research areas covered in technology management. | |||||||||
| HAI5012 | Research Methods and Intro to statistics | 3 | 6 | Major | Master/Doctor | 1-4 | Human-Artificial Intelligence Interaction | English | Yes |
| This course offers an overview of research methodology including basic concepts employed in quantitative research methods. Students will have training to write a research paper using quantitative research methods. In the first half of the semester, we will cover various research methodologies, overall research processes, the structure of research paper, and the key elements of research including independent/ dependent variables, validity and reality. In the second half of the semester, we will learn statistical analysis including both descriptive and inferential statistics. | |||||||||
| HAI5013 | AI design and human psychology | 3 | 6 | Major | Master/Doctor | 1-4 | Human-Artificial Intelligence Interaction | English | Yes |
| This course discusses how AI should be designed to provide better user experiences. It introduces the basic concepts in Human-AI interaction and Human-computer interaction and provides a deep understanding through seminar and class discussion regarding the psychological effects of AI and user experience from the research in various disciplines including communication, psychology, and computer science. | |||||||||
| HAI5014 | Human AI Interaction | 3 | 6 | Major | Master/Doctor | 1-4 | Human-Artificial Intelligence Interaction | - | No |
| This course explores theoretical foundations of human-AI interaction and students are encouraged to find her own perspectives on the new mode of interactions of people, robots, and AI. | |||||||||
| HAI5015 | Experimental design and statistical analysis | 3 | 6 | Major | Master/Doctor | 1-4 | Human-Artificial Intelligence Interaction | - | No |
| This course addresses some issues on quantitative methods based on experiments and questionnaires in studying human-AI interaction phenomena empirically. The course contents roughly consist of statistical analysis, experimental design, and case study. The statistical analysis mainly includes regression analysis and analysis of variance (ANOVA), and the experimental design reviews various designs depending on experimental conditions. In addition, the case study addresses hypothesis testing and result analysis/interpretation for human-AI interaction based on the review of existing studies and several phenomena in our daily lives. Specifically, it focuses on designing experiments and analyzing data by focusing on the sustainable communication with AI-based systems. | |||||||||
| HAI5016 | Human-AI Interaction and Data Science | 3 | 6 | Major | Master/Doctor | 1-4 | Human-Artificial Intelligence Interaction | - | No |
| The purpose of this course is to understand data science techniques required to solve Human-AI Interaction problems, and to acquire the ability to apply them to real problems. Throughout this course, the students will develop a perspective to interpret Human-AI Interaction, and they will understand the decision-making process of the current AI algorithms, and learn data science techniques to address the issues. The use of AI is spreading, and in particular, AI algorithms choose the information that users are experiencing. This gate-keeping role was traditionally played by media experts in newspapers and TV networks, but it is now being replaced by AI. More specifically, the AI service recommends content that is judged to be most suitable for users, based on the user's behavioral pattern, thoughts, and relevance between content and other users. Users consume content while providing data for AI's learning, and in return, they receive improved services from AI. However, despite the fact that AI algorithms are widely used in various content platforms such as movies, music, news, and online shopping, a huge amount of exploration and improvement is required to develop the ideal interaction between human users and AI services. For example, the AI decision-making process is not transparent to users, and interface features of AI services may alter the level of transparency of AI services. Users will not believe in secretive services, and the user's distrust of AI may discourage | |||||||||
| HAI7001 | Human-AI Interaction Capstone Project | 3 | 6 | Major | Bachelor/Master/Doctor | 2-8 | Human-Artificial Intelligence Interaction | English | Yes |
| On the graduate school level, long-lived unsolved problem, major unresolved problems should be challenged using extant knowledge, artificial intelligence, and one-on-one interaction between faculty and students. The problem-solving includes academic, engineering, and interaction-driven processes. In the initial stage of the semeter, exploring and defining problem is conducted. In the mid-semester, relevant information and data are captured. In the final stage, direct attempts to solve the issue is to be made. | |||||||||
| SIC5011 | Collaborative Research Workshop | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| Individual research project on closing multiple gaps including economic gap, health/life cycle gap, and AI information technology gap. The topic will be determined under the guidance of an advisor. | |||||||||
| SIC5012 | Social Innovation Convergence Internship | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course is intended to teach students how to translate knowledge learned from the first two semesters to social innovation practices. Students will learn methods for applying theoretical knowledge to real-world industrial settings and making decisions using information. | |||||||||
| SIC5028 | Machine Learning with Python | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course aims that students implement machine learning algorithms with Python programming. In the beginning of this course, students will learn the basics about Python programming. In the latter part, students will implement various machine learning algorithms such as supervised and unsupervised learning with Python so that they could exactly understand the algorithms. | |||||||||
| SOA4002 | AI & Art | 3 | 6 | Major | Bachelor/Master | 1-4 | Art | Korean | Yes |
| The course aims to help understand artificial intelligence and deep learning as a cultural technology(CT) beyond simply understanding artificial intelligence as a new technology, including the definition and trend of artificial intelligence and the possibility of changes in the art field using artificial intelligence technology, and to practice it directly. In order to cultivate core technology-art convergence talent in the field of art, I would like to learn knowledge about artificial intelligence, machine learning and deep learning and actually utilize it in this class. | |||||||||
| WIS5019 | Interaction Science Project I | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | - | No |
| PBL Class. Students will engage in real life problems related with human technology interaction, and will be asked to provide solutions to problems within a semester through research, design, or technology development. | |||||||||
| WIS5032 | Media and MInd | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | - | No |
| The class reviews the state of the art in psychological and human computer interaction research bearing on the two aspects of the interaction of media and mind: (1) the influence of media technology, form and techniques on human cognition and (2) ways in which human cognition and behavior affect the modeling and design of media interfaces. | |||||||||
| WIS5037 | End-User Information Usage Behavior Studies | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | - | No |
| End-users mean those users who are working in the business processes of a certain company, and supposed to use various kinds of computer systems to solve their own problems. Therefore they are very sensitive about how to use the computer systems to their personal purposes, playing a crucial role in shaping future kinds of DSSs. In this regard, this course is supposed to provide theoretical backgrounds of end-users' information usage patterns, and case studies. | |||||||||