37 citations · 67 across the 8 of their papers we have counts for
8 papers
Knowledge Tagging with Large Language Model based Multi-Agent System
Hang Li, Tianlong Xu, Ethan Chang +1
Knowledge tagging for questions is vital in modern intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course conten…
Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever
Hang Li, Tianlong Xu, Jiliang Tang +1
Knowledge tagging for questions plays a crucial role in contemporary intelligent educational applications, including learning progress diagnosis, practice question recommendations,…
Large Language Models for Education: A Survey and Outlook
Shen Wang, Tianlong Xu, Hang Li +5
The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education. This survey paper summarizes the various technologies of LLMs in edu…
Automate Knowledge Concept Tagging on Math Questions with LLMs
Hang Li, Tianlong Xu, Jiliang Tang +1
Knowledge concept tagging for questions plays a crucial role in contemporary intelligent educational applications, including learning progress diagnosis, practice question recommen…
Content Knowledge Identification with Multi-Agent Large Language Models (LLMs)
Kaiqi Yang, Yucheng Chu, Taylor Darwin +6
Teachers' mathematical content knowledge (CK) is of vital importance and need in teacher professional development (PD) programs. Computer-aided asynchronous PD systems are the most…
Are Large Language Models (LLMs) Good Social Predictors?
Kaiqi Yang, Hang Li, Hongzhi Wen +3
The prediction has served as a crucial scientific method in modern social studies. With the recent advancement of Large Language Models (LLMs), efforts have been made to leverage L…
Bringing Generative AI to Adaptive Learning in Education
Hang Li, Tianlong Xu, Chaoli Zhang +6
The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including sci…
Graph-level Representation Learning with Joint-Embedding Predictive Architectures
Geri Skenderi, Hang Li, Jiliang Tang +1
Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning. They aim to learn an energy-ba…