I am currently a first-year PhD student in Manning College of Information & Computer Sciences, University of Massachusetts Amherst, fortunately advised by Prof. Andrew Lan.
Before that, I was an applied scientist at TAL Education Group (NYSE: TAL) and gratefully supervised by Prof. Zitao Liu.
Prior to TAL, I got my M.S. degree in the NLP2CT Lab at the University of Macau, thankfully advised by Prof. Derek F. Wong.
My research interests include AI-powered tutoring systems and personalized large language models (LLMs), particularly in education domain. My work focuses on developing adaptive and effective AI systems that enhance student learning outcomes. More specifically, I am exploring the following topics:
- Intelligent Tutoring Systems: designing adaptive tutoring strategies to improve learning outcomes;
- Learner Modeling: inferring students’ learning behaviors from their learning histories;
- Personalized LLMs: adapting LLM behavior to individual user's preferences.
Additionally, I am one of the main developers of a python library for training deep learning based knowledge tracing models pyKT (GitHub 400+ ⭐ and 60k+ 📥).
I am always open to discussing research ideas and potential collaborations. If you are interested in my work or would like to collaborate, please feel free to reach out.
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🏆 Top 100 Open Source Achievement Award2023
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🥇 First Prize, The Quest for Quality Questions2023
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🌟 Best Paper Honorable Mention (Resource Track), CIKM2022
Services
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PC Member of WWW2026, 2024
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PC Member of AAAI2026, 2027
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PC Member of SIGIR2025
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PC Member of AIED2025, 2024, 2023
News
Selected Publications (view all )
Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
Shuyan Huang, Alexander Scarlatos, Jaewook Lee, Andrew Lan
21st BEA workshop at ACL 2026
An interpretable difficulty-aware conversational KT framework built upon LLMs, which explicitly models students' abilities and the difficulties of tutor-posed tasks turn by turn.
Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
Shuyan Huang, Alexander Scarlatos, Jaewook Lee, Andrew Lan
21st BEA workshop at ACL 2026
An interpretable difficulty-aware conversational KT framework built upon LLMs, which explicitly models students' abilities and the difficulties of tutor-posed tasks turn by turn.
Knowledge-enhanced large language models for automatic lesson plan generation
Ying Zheng*, Shuyan Huang*, Xiaoli Zeng, Yaying Huang, Zitao Liu, Weiqi Luo (* equal contribution)
Nature Humanities and Social Sciences Communications 2025
Enhancing large language models with educational domain knowledge to improve the accuracy of generated lesson plans.
Knowledge-enhanced large language models for automatic lesson plan generation
Ying Zheng*, Shuyan Huang*, Xiaoli Zeng, Yaying Huang, Zitao Liu, Weiqi Luo (* equal contribution)
Nature Humanities and Social Sciences Communications 2025
Enhancing large language models with educational domain knowledge to improve the accuracy of generated lesson plans.
Dual-attentional time-aware fusion networks for knowledge tracing
Shuyan Huang, Zitao Liu, Qiongqiong Liu, Jiahao Chen, Yaying Huang
Information Fusion 2025
A dual-attentional time-aware fusion network for knowledge tracing that captures both temporal dynamics and question-level dependencies.
Dual-attentional time-aware fusion networks for knowledge tracing
Shuyan Huang, Zitao Liu, Qiongqiong Liu, Jiahao Chen, Yaying Huang
Information Fusion 2025
A dual-attentional time-aware fusion network for knowledge tracing that captures both temporal dynamics and question-level dependencies.
Enhancing deep knowledge tracing with auxiliary tasks
Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Boyu Gao, Weiqi Luo, Jian Weng
The Web Conference (WWW) 2023
Boosting knowledge tracing performance through the integration of multi-task learning with auxiliary educational objectives.
Enhancing deep knowledge tracing with auxiliary tasks
Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Boyu Gao, Weiqi Luo, Jian Weng
The Web Conference (WWW) 2023
Boosting knowledge tracing performance through the integration of multi-task learning with auxiliary educational objectives.
simpleKT: a simple but tough-to-beat baseline for knowledge tracing
Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang#, Weiqi Luo (# corresponding author)
ICLR 2023
A surprisingly effective and simple baseline for knowledge tracing that consistently outperforms complex deep learning models.
simpleKT: a simple but tough-to-beat baseline for knowledge tracing
Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang#, Weiqi Luo (# corresponding author)
ICLR 2023
A surprisingly effective and simple baseline for knowledge tracing that consistently outperforms complex deep learning models.
Improving interpretability of deep sequential knowledge tracing models with question-centric cognitive representations
Jiahao Chen, Zitao Liu, Shuyan Huang, Qiongqiong Liu, Weiqi Luo
AAAI 2023
Improving the interpretability of deep knowledge tracing by learning question-centric cognitive representations.
Improving interpretability of deep sequential knowledge tracing models with question-centric cognitive representations
Jiahao Chen, Zitao Liu, Shuyan Huang, Qiongqiong Liu, Weiqi Luo
AAAI 2023
Improving the interpretability of deep knowledge tracing by learning question-centric cognitive representations.
pyKT: a python library to benchmark deep learning based knowledge tracing models
Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang#, Jiliang Tang, Weiqi Luo (# corresponding author)
NeurIPS 2022
A comprehensive Python library designed to provide a standardized benchmark for deep learning-based knowledge tracing.
pyKT: a python library to benchmark deep learning based knowledge tracing models
Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang#, Jiliang Tang, Weiqi Luo (# corresponding author)
NeurIPS 2022
A comprehensive Python library designed to provide a standardized benchmark for deep learning-based knowledge tracing.