Enhancing Chinese Character Writing Learning: The Role of MLLM-based Intelligent Tutoring Systems

MLLM-based Intelligent Tutoring System (ITS) improves learning performance by offering corrective feedback. However, a few studies have focused on its role in teaching Chinese as a foreign language. Given the significant impact of feedback on second language acquisition, this study aims to assess the performance of an Intelligent Tutoring System as a Chinese character learning tool. Specifically, we designed an Intelligent Tutoring System for Chinese character learning, which is grounded in a Multimodal Large Language Model with a feedback mechanism based on feedback literacy principles. We then conducted a Chinese teaching experiment to validate the system's effectiveness. Our findings indicate that students using ITS receive more timely corrective feedback and perform better in writing Chinese characters than in the traditional teaching method. These findings offer insights into the importance of corrective feedback in Chinese character learning and the combination of an Intelligent Tutoring System and a Large Language Model. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Funding 1: This research was financially supported by the General Project of the National Social Science Foundation of Education (Project No: BBA23006 3). Funding 2: This paper was the phase research result of the General Project of Education of the National Social Science Foundation of China in 2023, “Cross -contextual Statistical Learning Mechanisms in the Symbiotic Development of Adolescents' Verbal Reasoning and Mental Theory” (No. BBA230063). Funding 3: This paper was supported by the National Innovation and Entre-preneurship Training Program for Undergraduate (No. 202410559051). Funding 4: This paper was supported by the Jinan University Zhong Chen Yulan Research Innovation Project Program for Undergraduate (No. ZC2405).

Authors

Institutions

Publication Details

Published
2025-05-14
DOI
https://doi.org/10.1109/icaie64856.2025.11158428
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Chinese Character Writing Learning: The Role of MLLM-based Intelligent Tutoring Systems

Baohua Su, Wen Tan, Qing Chen, Liting Wang et al.
Intelligent Tutoring Systems and Adaptive Learning
article

Enhancing Chinese Character Writing Learning: The Role of MLLM-based Intelligent Tutoring Systems

Baohua Su, Wen Tan, Qing Chen, Liting Wang, Jun Peng
article en

Abstract

MLLM-based Intelligent Tutoring System (ITS) improves learning performance by offering corrective feedback. However, a few studies have focused on its role in teaching Chinese as a foreign language. Given the significant impact of feedback on second language acquisition, this study aims to assess the performance of an Intelligent Tutoring System as a Chinese character learning tool. Specifically, we designed an Intelligent Tutoring System for Chinese character learning, which is grounded in a Multimodal Large Language Model with a feedback mechanism based on feedback literacy principles. We then conducted a Chinese teaching experiment to validate the system's effectiveness. Our findings indicate that students using ITS receive more timely corrective feedback and perform better in writing Chinese characters than in the traditional teaching method. These findings offer insights into the importance of corrective feedback in Chinese character learning and the combination of an Intelligent Tutoring System and a Large Language Model. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Funding 1: This research was financially supported by the General Project of the National Social Science Foundation of Education (Project No: BBA23006 3). Funding 2: This paper was the phase research result of the General Project of Education of the National Social Science Foundation of China in 2023, “Cross -contextual Statistical Learning Mechanisms in the Symbiotic Development of Adolescents' Verbal Reasoning and Mental Theory” (No. BBA230063). Funding 3: This paper was supported by the National Innovation and Entre-preneurship Training Program for Undergraduate (No. 202410559051). Funding 4: This paper was supported by the Jinan University Zhong Chen Yulan Research Innovation Project Program for Undergraduate (No. ZC2405).

Ningbo University (CN), Jinan University (CN)
Openalex Percentile: Top 86%
Intelligent Tutoring Systems and Adaptive Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.