TY - GEN
T1 - One Size doesn't Fit All
T2 - 34th ACM Web Conference, WWW Companion 2025
AU - Liu, Ben
AU - Zhang, Jihai
AU - Lin, Fangquan
AU - Jia, Xu
AU - Peng, Min
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2025/5/23
Y1 - 2025/5/23
N2 - Large language models (LLMs) have been increasingly employed in various intelligent educational systems, simulating human tutors to facilitate effective human-machine interaction. However, previous studies often overlook the significance of recognizing and adapting to individual learner characteristics. Such adaptation is crucial for enhancing student engagement and learning efficiency, particularly in mathematics instruction, where diverse learning styles require personalized strategies to promote comprehension and enthusiasm. In this paper, we propose a PersonAlized Conversational tutoring agEnt (PACE) for mathematics instruction. PACE simulates students' learning styles based on the Felder and Silverman learning style model, aligning with each student's persona. In this way, our PACE can effectively assess the personality of students, allowing to develop individualized teaching strategies that resonate with their unique learning styles. To further enhance students' comprehension, PACE employs the Socratic teaching method to provide instant feedback and encourage deep thinking. By constructing personalized teaching data and training models, PACE demonstrates the ability to identify and adapt to the unique needs of each student, significantly improving the overall learning experience and outcomes. Moreover, we establish multi-aspect evaluation criteria and conduct extensive analysis to assess the performance of personalized teaching. Experimental results demonstrate the superiority of our model in personalizing the educational experience and motivating students compared to existing methods.
AB - Large language models (LLMs) have been increasingly employed in various intelligent educational systems, simulating human tutors to facilitate effective human-machine interaction. However, previous studies often overlook the significance of recognizing and adapting to individual learner characteristics. Such adaptation is crucial for enhancing student engagement and learning efficiency, particularly in mathematics instruction, where diverse learning styles require personalized strategies to promote comprehension and enthusiasm. In this paper, we propose a PersonAlized Conversational tutoring agEnt (PACE) for mathematics instruction. PACE simulates students' learning styles based on the Felder and Silverman learning style model, aligning with each student's persona. In this way, our PACE can effectively assess the personality of students, allowing to develop individualized teaching strategies that resonate with their unique learning styles. To further enhance students' comprehension, PACE employs the Socratic teaching method to provide instant feedback and encourage deep thinking. By constructing personalized teaching data and training models, PACE demonstrates the ability to identify and adapt to the unique needs of each student, significantly improving the overall learning experience and outcomes. Moreover, we establish multi-aspect evaluation criteria and conduct extensive analysis to assess the performance of personalized teaching. Experimental results demonstrate the superiority of our model in personalizing the educational experience and motivating students compared to existing methods.
KW - Large Language Model Agent
KW - Learning-Style
KW - Personalized Teaching
UR - https://www.scopus.com/pages/publications/105009217216
U2 - 10.1145/3701716.3717527
DO - 10.1145/3701716.3717527
M3 - Conference contribution
AN - SCOPUS:105009217216
T3 - WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025
SP - 2401
EP - 2409
BT - WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025
PB - Association for Computing Machinery, Inc
Y2 - 28 April 2025 through 2 May 2025
ER -