一刀切并不适用：面向数学教学的个性化对话辅导智能体
One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
AI总结：
针对数学教学中学习者差异被忽视的问题，本文提出 PACE 个性化对话辅导智能体，结合学习风格画像、苏格拉底式反馈与个性化训练，提升教学适应性和学习效果。
摘要：
大语言模型（LLM）已被越来越多地应用于各类智能教育系统，模拟人类辅导教师以促进有效的人机交互。然而，以往研究往往忽视识别并适应学习者个体特征的重要性。这种适应对于提升学生参与度和学习效率至关重要，尤其是在数学教学中，不同的学习风格需要个性化策略来促进理解和热情。本文提出一种用于数学教学的 PersonAlized Conversational tutoring agEnt（PACE，个性化对话辅导智能体）。PACE 基于 Felder 和 Silverman 学习风格模型模拟学生的学习风格，并与每位学生的角色画像保持一致。通过这种方式，PACE 能够有效评估学生的个性，从而制定与其独特学习风格相契合的个性化教学策略。为进一步增强学生理解，PACE 采用苏格拉底教学法提供即时反馈并鼓励深度思考。通过构建个性化教学数据并训练模型，PACE 展现出识别并适应每位学生独特需求的能力，显著改善整体学习体验和结果。此外，我们建立了多方面评估标准，并开展广泛分析以评估个性化教学的表现。实验结果表明，与现有方法相比，我们的模型在个性化教育体验和激励学生方面具有优越性。
英文摘要：
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 \textbf{P}erson\textbf{A}lized \textbf{C}onversational tutoring ag\textbf{E}nt (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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
