Hongming (Chip) LI

Hello, I am Chip Li, a Ph.D. student in Educational Technology (EdTech) at the College of Education at the University of Florida (UF), working under the supervision of Dr. Anthony Botelho. My research focuses on Learning Analytics, Educational Data Mining, and AI in education, particularly developing explainable AI systems for educational feedback and enhancing collaborative learning environments. With solid experience in machine learning and full-stack development, I develop AI-powered educational platforms and tools. I actively contribute to research projects funded by the National Science Foundation, Gates Foundation, and OpenAI, applying my technical and research expertise. I also maintain active engagement in machine learning practice through Kaggle competitions, where I have achieved Data Science Master status with consistent performance in the top 1% globally.

Before my doctoral studies, I hold two Master's degrees: one from Johns Hopkins University, where I specialized in learning sciences and digital technology integration in education, and another from the University of Southampton (UK), where I concentrated on educational theory and research methodology. My foundation in education began at Capital Normal University (Beijing, China), focusing on elementary education and linguistics. As an educator, I have served as both a teaching assistant and a research assistant, and I have taught in both urban and rural elementary schools in China.

Email: hli3@ufl.edu

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Research Interests

My research agenda sits at the intersection of education, technology, and data science, focusing on developing and implementing AI-driven solutions that enhance teaching and learning experiences. I am committed to conducting rigorous research that addresses fundamental educational challenges while emphasizing practical applications and ethical considerations.

Currently, I am particularly interested in the following areas and welcome collaboration opportunities:

Learning Analytics and Educational Data Mining
Learning Engineering and Learning Science
Machine Learning
Natural Language Processing (NLP) and Large Language Models (LLMs)
Explainable and Human-centered Artificial Intelligence (XAI / HAI)
Human-computer Interaction（HCI）
Statistics and Causal Inference
Equity and Fairness in Education
Instructional Design / Educational Gamification and Media / Digital Learning Ecology and Online Learning

My research aims to bridge the gap between theoretical frameworks and practical applications in educational technology. By developing explainable AI systems and data-driven approaches, I strive to create more equitable, accessible, and effective learning environments. I am mainly focused on ensuring that technological advancements in education benefit all learners while maintaining transparency and ethical considerations in AI implementation.