AI-Supported STEM Learning
STEM learning and science and engineering practices, including inquiry, modeling, argumentation, and classroom-based research.
PhD Student | Science Education | University of Georgia
Zhaoji Wang
I am a Ph.D. student in Science Education whose research examines how artificial intelligence, learning analytics, and educational technology can support STEM learning, scientific argumentation, modeling, and assessment.
Academic Profile
My work brings together science education, AI4Education, learning analytics, and technology-supported STEM teaching and learning. I am especially interested in AI-supported scientific argumentation, educational assessment, and data-informed approaches to understanding learners' progress.
Main Areas
Research Interests
Learning Analytics
Educational data mining, student modeling, and machine learning for learning progressions.
AI4Education
Text analysis, natural language processing, LLM-assisted assessment, and multi-agent systems for STEM education.
Educational Technology
Technology-supported teaching, AI chatbots, human-computer interaction, and learner behavior in digital environments.
Methods
Methods and Technical Skills
I use quantitative, computational, and AI-enabled methods to study learning processes, classroom interaction, and educational assessment.
NLP and text mining
Educational data mining
Knowledge tracing
HLM and SEM
IRT and CAT
SNA and ENA
LLM-assisted assessment
Multi-agent AI systems