Research Projects

The 'Assistant' component integrates Retrieval-Augmented Generation (RAG) with AI Agent technology to overcome the limitations of immediate feedback in traditional classroom settings. Through dynamic decision-making, the system autonomously identifies query types and selects optimal tools to deliver curriculum-based Q&A and content summaries. This approach significantly enhances interactivity, establishing a more efficient and responsive environment for personalized guidance.

This component focuses on visualizing the student's cognitive state through real-time feedback and behavioral analysis. Unlike conventional dashboards that merely display completion statistics, the system integrates error distribution, performance metrics, and accuracy-completion contrasts to transform data into motivation for self-reflection. By intuitively presenting these learning trajectories, the portfolio enables students to accurately pinpoint weak areas and optimize the effectiveness of their self-directed learning.

This project aims to develop a personalized intelligent tutoring system through a large language model, encompassing two components: automatic question generation, grading, and personalized feedback. Unlike most intelligent tutoring systems which interact with users in a preset manner, this system focuses on individualized feedback based on each student's performance. The system strives to gain a deeper understanding of students' learning conditions, aiding them in adjusting their learning pace and providing more targeted guidance.