# One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction arXiv: https://arxiv.org/abs/2502.12633 · HTML: https://mlsanity.com/paper/2502.12633/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. - Builds on 17 of its 43 references that are in the map. - First posted on arXiv 18 Feb 2025. ## Placed in - LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/) ## Builds on (most cited first, 17 of 17) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023) - [Character-LLM: A Trainable Agent for Role-Playing](https://mlsanity.com/paper/2310.10158/) (2023) - [MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://mlsanity.com/paper/2305.14536/) (2023) - [SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents](https://mlsanity.com/paper/2403.08715/) (2024) - [EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education](https://mlsanity.com/paper/2308.02773/) (2023) - [CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles](https://mlsanity.com/paper/2305.13272/) (2023) - [The Oscars of AI Theater: A Survey on Role-Playing with Language Models](https://mlsanity.com/paper/2407.11484/) (2024) - [Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling](https://mlsanity.com/paper/2403.14071/) (2024) - [Book2Dial: Generating Teacher-Student Interactions from Textbooks for Cost-Effective Development of Educational Chatbots](https://mlsanity.com/paper/2403.03307/) (2024) - [Solving General Natural-Language-Description Optimization Problems with Large Language Models](https://mlsanity.com/paper/2407.07924/) (2024) - [Boosting Large Language Models with Socratic Method for Conversational Mathematics Teaching](https://mlsanity.com/paper/2407.17349/) (2024) - [Unveiling the Secrets of Engaging Conversations: Factors that Keep Users Hooked on Role-Playing Dialog Agents](https://mlsanity.com/paper/2402.11522/) (2024) - [PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traits](https://mlsanity.com/paper/2401.07363/) (2024) ## Built on by (most cited first, 17 of 17) - [A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence](https://mlsanity.com/paper/2507.21046/) (2025) - [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025) - [EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery](https://mlsanity.com/paper/2603.08127/) (2026) - [POPI: Personalizing LLMs via Optimized Natural Language Preference Inference](https://mlsanity.com/paper/2510.17881/) (2025) - [Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style](https://mlsanity.com/paper/2505.19173/) (2025) - [When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration](https://mlsanity.com/paper/2506.05579/) (2025) - [PATS: Personality-Aware Teaching Strategies with Large Language Model Tutors](https://mlsanity.com/paper/2601.08402/) (2026) - [Teaching According to Students' Aptitude: Personalized Mathematics Tutoring via Persona-, Memory-, and Forgetting-Aware LLMs](https://mlsanity.com/paper/2511.15163/) (2025) - [Confidence Should Be Calibrated More Than One Turn Deep](https://mlsanity.com/paper/2604.05397/) (2026) - [CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors](https://mlsanity.com/paper/2604.14773/) (2026) - [PEARL: Training Socratic Tutors with Pedagogically Aligned Reinforcement Learning](https://mlsanity.com/paper/2605.29582/) (2026) - [Hypotheses-Guided Self Distillation for Continual Personalization](https://mlsanity.com/paper/2609.00251/) (2026) - [EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluation](https://mlsanity.com/paper/2511.11635/) (2025) - [Enhancing Mathematical Problem Solving in LLMs through Execution-Driven Reasoning Augmentation](https://mlsanity.com/paper/2602.03950/) (2026) - [TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors](https://mlsanity.com/paper/2603.18189/) (2026) - [Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development](https://mlsanity.com/paper/2607.06074/) (2026) - [Socrates went Nuclear: Comparing Interaction Strategies for AI systems in a Learning Context using Brain Sensing](https://mlsanity.com/paper/2609.00584/) (2026) ## Abstract (arXiv) 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.