activity
20242026
most citedLarge Language Models for Education: A Survey and Outlook

37 citations · 56 across the 13 of their papers we have counts for

collaborators

14 papers

cs.MA2026

REAT: A Reflective Experience-Augmented Tutoring Framework for Multi-turn Mathematical Instruction

Jianheng Zhou, Chaoli Zhang, Xingjun Wei +6

Current Large Language Models (LLMs) excel at solving complex mathematical problems, yet this proficiency does not inherently translate into effective tutoring. While advanced LLM…

cs.HC2026

Designing and Evaluating Next-Generation Learning Interfaces: Linking AI, HCI, and the Learning Sciences

Meng Xia, Yan Chen, Qiao Jin +5

This workshop addresses this gap by bringing together researchers and practitioners from AI, HCI, and the learning sciences to explore how interactive systems can better support le…

cs.AI2026

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…

cs.CL2025★ 2 cited

A Survey on Post-training of Large Language Models

Guiyao Tie, Zeli Zhao, Dingjie Song +23

The emergence of Large Language Models (LLMs) has fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational system…

cs.CV2025★ 1 cited

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,…

cs.CL2024

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…

cs.CV2024

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…

cs.CL2024

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…

cs.CL2024★ 1 cited

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,…

cs.CY2024

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…

cs.CL2024★ 37 cited

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…

cs.CL2024★ 3 cited

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…

cs.CY2024★ 5 cited

Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions

Richard Tong, Haoyang Li, Joleen Liang +1

The adoption of Artificial Intelligence in Education (AIED) holds the promise of revolutionizing educational practices by offering personalized learning experiences, automating adm…

cs.CY2024★ 7 cited

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…