activity
20242026
most citedFrom Correctness to Comprehension: AI Agents for Personalized Error Diagnosis in Education

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

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

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.CV20251 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…