Yeo Jin Kim

Orcid: 0000-0001-5760-2959

According to our database1, Yeo Jin Kim authored at least 13 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
The Role of LLM-Powered Conversational Agents in Supporting Inquiry in a Narrative-Centered Learning Environment: A Learning Analytics Study.
Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference, 2026

Collaborative Dialogue Analysis for Productive Problem Solving.
Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference, 2026

A Dialogue-Based Learning Analytics Framework for Collaborative Game-Based Learning.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
LLM-Based Student Plan Generation for Adaptive Scaffolding in Game-Based Learning Environments.
Int. J. Artif. Intell. Educ., June, 2025

Collaborative Problem-Solving Dialogue Analysis with Interpretable Temporal Clustering.
Proceedings of the Artificial Intelligence in Education - 26th International Conference, 2025

2024
Dual Process Masking for Dialogue Act Recognition.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Online Reinforcement Learning-Based Pedagogical Planning for Narrative-Centered Learning Environments.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Time-aware deep reinforcement learning with multi-temporal abstraction.
Appl. Intell., September, 2023

Language Model-Based Player Goal Recognition in Open World Digital Games.
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2023

Language Modeling for Plan Generation in Game-Base Learning Environments.
Proceedings of the Workshop on Empowering Education with LLMs, 2023

2021
Multi-Temporal Abstraction with Time-Aware Deep Q-Learning for Septic Shock Prevention.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

To Reduce Healthcare Workload: Identify Critical Sepsis Progression Moments through Deep Reinforcement Learning.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020
Automatic, Qualitative Scoring of the Interlocking Pentagon Drawing Test (PDT) Based on U-Net and Mobile Sensor Data.
Sensors, 2020


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