37 citations · 50 across the 11 of their papers we have counts for
12 papers
Can MLLMs Read Students' Minds? Unpacking Multimodal Error Analysis in Handwritten Math
Dingjie Song, Tianlong Xu, Yi-Fan Zhang +6
Assessing student handwritten scratchwork is crucial for personalized educational feedback but presents unique challenges due to diverse handwriting, complex layouts, and varied pr…
Aligning Multimodal LLM with Human Preference: A Survey
Tao Yu, Yi-Fan Zhang, Chaoyou Fu +14
Large language models (LLMs) can handle a wide variety of general tasks with simple prompts, without the need for task-specific training. Multimodal Large Language Models (MLLMs),…
From Correctness to Comprehension: AI Agents for Personalized Error Diagnosis in Education
Yi-Fan Zhang, Hang Li, Dingjie Song +3
Large Language Models (LLMs), such as GPT-4, have demonstrated impressive mathematical reasoning capabilities, achieving near-perfect performance on benchmarks like GSM8K. However,…
Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions
Hang Li, Tianlong Xu, Kaiqi Yang +5
The rise of large language models (LLMs) offers new opportunities for automatic error detection in education, particularly for math word problems (MWPs). While prior studies demons…
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection
Yibo Yan, Shen Wang, Jiahao Huo +13
As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particularly promising, especially in addr…
AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction
Tianlong Xu, Yi-Fan Zhang, Zhendong Chu +2
Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time-consuming and labor-intensive. This paper introduces a…
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,…
Foundation Models for Education: Promises and Prospects
Tianlong Xu, Richard Tong, Jing Liang +3
With the advent of foundation models like ChatGPT, educators are excited about the transformative role that AI might play in propelling the next education revolution. The developin…
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…
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…