For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| HAI5006 | Computational Social Science | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| This course deals with how programming can be applied to modern social science. Python, R, and other relevant programming languages are used for descriptive statistics, inferential statistics, data visualization, machine learning, and deep learning. Multiple examples, quizzes, and projects are conducted in the course. | |||||||||
| HAI5007 | AI-based User Research | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| With the development of AI, now it is possible to collect and analyze massive amount of data. In this class, students will learn how to communicate the results of big data with users more effectively and persuasively and how to design UX accordingly. | |||||||||
| HAI5008 | Human-Centered Machine Learning | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| In this lecture, students first can learn basic knowledge on machine learning including classification, clustering, and prediction. Also, basic algorithms on machine learning such as SVM, Random Forest, and Neural Networks will be covered. Students then learn how to apply machine learning in human-generated data or human-centered system with diverse case studies. | |||||||||
| HAI5009 | Special Seminar: Interaction Big Data | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| This course introduces ‘interaction big data’ and presents how this new field deals with data science, user, and interface in terms of theory and method. Moving from data structure-oriented data science to a new approach of encompassing users and interfaces in data science may lead to enhanced understanding of data-driven thinking. | |||||||||
| 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 | |||||||||
| HAI5017 | HCI and AI practice | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| In this class, students will be guided to explore research topics using artificial intelligence and make creative interface design with user experience insights. This class is based on the Human-Center Computing method. At the end of the semester, students will be guided to present their own research topic and results. This class combines lectures and student presentations. | |||||||||
| 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. | |||||||||
| HIC5004 | Biometric Cognition and Emotional Care | 3 | 6 | Major | Master/Doctor | 1-4 | Human ICT Convergence | - | No |
| This course focuses on 1) obtaining various biomedical signals such as electrocardiogram, electroencephalogram, skin response by employing appropriate sensors, 2) analyzing the obtained signal using pattern recognition, and 3) exploring how the obtained information can be used for healthcare and life style enhancement | |||||||||
| HIC5005 | Perceptual Computing | 3 | 6 | Major | Master/Doctor | 1-4 | Human ICT Convergence | - | No |
| This course cover following topics of perceptual computing; 1) use of big data, 2) computation and storage of data centers, 3) Human Computer Interaction, 4)artificial intelligence & machine learning, 5)stream computing, and 6)neuromorphic architectures and neural processing units. | |||||||||