TEACHING EXPERIENCES

Seoul National University 

This graduate seminar integrates learning sciences, learning analytics, and AI to develop students’ ability to understand, analyze, and apply learning data in research and system design. The course begins with core concepts in the learning sciences and an overview of learning analytics, then examines how learning data differ across laboratory, classroom, and online contexts. Students practice the fundamentals of learning data analysis, including preprocessing, indicator design, analysis, and interpretation. The seminar also builds a principled understanding of AI and human–AI interaction in educational settings, and explores AI-enabled learning systems and automated assessment. Students refine a research project plan, complete a staged AI-based learning system development project, and present their final outcomes while drawing integrated implications for learning, data, and AI.

Seoul National University 

This module introduces learning sciences through neuroscience, data, and AI. It examines learning processes (attention, memory, emotion, motivation), outlines key educational neuroscience methods (e.g., EEG, fNIRS) and their limits, and surveys learning analytics and data across lab, classroom, and online contexts. It also explores AI-enabled learning systems and personalized learning, emphasizing applications and core considerations such as fairness, transparency, and human–AI collaboration.