Kim, Juho

How video production affects student engagement: an empirical study of MOOC videos

Guo, P. J., Kim, J., & Rubin, R. (2014) · Proceedings of the first ACM conference on Learning @ scale conference

Online Learning and AnalyticsVisual and Cognitive Learning Processes

Cited by 1,850

Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works

Ko, H.-K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J. (2023) · Proceedings of the 28th International Conference on Intelligent User Interfaces

Digital Media and Visual ArtGenerative Adversarial Networks and Image Synthesis

Cited by 189

CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI

Choi, D., Hong, S., Park, J., Chung, J. J. Y., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Digital Media and Visual ArtDesign Education and Practice

Cited by 124

Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education

Jin, H., Lee, S., Shin, H., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Intelligent Tutoring Systems and Adaptive LearningTeaching and Learning Programming

Cited by 99

EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria

Kim, T. S., Lee, Y., Shin, J., Kim, Y.-H., & Kim, J. (2023) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 82

Cells, Generators, and Lenses: Design Framework for Object-Oriented Interaction with Large Language Models

Kim, T. S., Lee, Y., Chang, M., & Kim, J. (2023) · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology

Usability and User Interface DesignInnovative Human-Technology Interaction

Cited by 56

Personalizing Ambience and Illusionary Presence: How People Use “Study with me” Videos to Create Effective Studying Environments

Lee, Y., Chung, J. J. Y., Song, J. Y., Chang, M., & Kim, J. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Innovative Teaching and Learning MethodsVisual and Cognitive Learning Processes

Cited by 52

SolutionChat: Real-time Moderator Support for Chat-based Structured Discussion

Lee, S.-C., Song, J., Ko, E.-Y., Park, S., Kim, J., & Kim, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Data Visualization and AnalyticsExpert finding and Q&A systems

Cited by 49

One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations

Lee, Y., Son, K., Kim, T. S., Kim, J., Chung, J. J. Y., Adar, E., & Kim, J. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency

Information Retrieval and Search BehaviorTopic Modeling

Cited by 36

DAPIE: Interactive Step-by-Step Explanatory Dialogues to Answer Children’s Why and How Questions

Lee, Y., Kim, T. S., Kim, S., Yun, Y., & Kim, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Speech and dialogue systemsIntelligent Tutoring Systems and Adaptive Learning

Cited by 35

Bridging Learnersourcing and AI: Exploring the Dynamics of Student-AI Collaborative Feedback Generation

Singh, A., Brooks, C., Wang, X., Li, W., Kim, J., & Wilson, D. (2024) · Proceedings of the 14th Learning Analytics and Knowledge Conference

Intelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

Cited by 18

VIVID: Human-AI Collaborative Authoring of Vicarious Dialogues from Lecture Videos

Choi, S., Lee, H., Lee, Y., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive Learning

Cited by 17

Can Fans Build Parasocial Relationships through Idols' Simulated Voice Messages?: A Study of AI Private Call Users' Perceptions, Cognitions, and Behaviors

Kang, E. J., Kim, H., Kim, H., Fussell, S. R., & Kim, J. (2025) · Proceedings of the ACM on Human-Computer Interaction

Innovative Human-Technology InteractionSocial Robot Interaction and HRI

Cited by 9

Human-Centered Evaluation and Auditing of Language Models

Liu, Y. L., Deng, W. H., Lam, M. S., Eslami, M., Kim, J., Liao, Q. V., Xu, W., Novikova, J., & Xiao, Z. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 7

Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews

Shin, H., Tang, J., Lee, Y., Kim, N., Lim, H., Cho, J. Y., Hong, H., Lee, M., & Kim, J. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 5

Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment

Kim, Y., Son, K., Kim, S., & Kim, J. (2024) · arXiv

Speech and dialogue systemsLanguage, Discourse, Communication Strategies

Cited by 2

Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews

Shin, H., Tang, J., Lee, Y., Kim, N., Lim, H., Cho, J. Y., Hong, H., Lee, M., & Kim, J. (2025) · arXiv

Academic Writing and PublishingTopic Modeling

Cited by 0

Using LLMs to Investigate Correlations of Conversational Follow-up Queries with User Satisfaction

Kim, H., Choi, Y., Yang, T., Lee, H., Park, C., Lee, Y., Kim, J. Y., & Kim, J. (2024) · arXiv

Information Retrieval and Search BehaviorRecommender Systems and Techniques

Cited by 0