AI-Powered Math Tutoring: Platform for Personalized and Adaptive Education
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Abstract (arXiv)
The growing ubiquity of artificial intelligence (AI), in particular large language models (LLMs), has profoundly altered the way in which learners gain knowledge and interact with learning material, with many claiming that AI positively influences their learning achievements. Despite this advancement, current AI tutoring systems face limitations associated with their reactive nature, often providing direct answers without encouraging deep reflection or incorporating structured pedagogical tools and strategies. This limitation is most apparent in the field of mathematics, in which AI tutoring systems remain underdeveloped. This research addresses the question: How can AI tutoring systems move beyond providing reactive assistance to enable structured, individualized, and tool-assisted learning experiences? We introduce a novel multi-agent AI tutoring platform that combines adaptive and personalized feedback, structured course generation, and textbook knowledge retrieval to enable modular, tool-assisted learning processes. This system allows students to learn new topics while identifying and targeting their weaknesses, revise for exams effectively, and practice on an unlimited number of personalized exercises. This article contributes to the field of artificial intelligence in education by introducing a novel platform that brings together pedagogical agents and AI-driven components, augmenting the field with modular and effective systems for teaching mathematics.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 14 Jul 2025.
Builds on
12 of 25 references.
References · 12
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
- 2025rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinkingcited by 313
- 2023MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problemscited by 131
- 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generationcited by 67
- 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insightscited by 53
- 2023CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principlescited by 48
- 2024Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modelingcited by 34
- 2023Opportunities and Challenges in Neural Dialog Tutoringcited by 33
- 2024Jill Watson: A Virtual Teaching Assistant powered by ChatGPTcited by 24
- 2024Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructionscited by 12
- 2025ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Predictioncited by 9
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Citing papers per year: 2025: 4; 2026: 2.
6 papers.
- 2025Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluationcited by 8
- 2026Heterogeneous Debate Engine: Identity-Grounded Cognitive Architecture for Resilient LLM-Based Ethical Tutoringcited by 5
- 2025Teaching According to Students' Aptitude: Personalized Mathematics Tutoring via Persona-, Memory-, and Forgetting-Aware LLMscited by 3
- 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agentscited by 2
- 2026When Verification Hurts: Asymmetric Effects of Multi-Agent Feedback in Logic Proof Tutoringcited by 2
- 2025EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluationcited by 0
- 2026Heterogeneous Debate Engine: Identity-Grounded Cognitive Architecture for Resilient LLM-Based Ethical Tutoring28 Mar 2026
- 2026When Verification Hurts: Asymmetric Effects of Multi-Agent Feedback in Logic Proof Tutoring28 Mar 2026
- 2025Teaching According to Students' Aptitude: Personalized Mathematics Tutoring via Persona-, Memory-, and Forgetting-Aware LLMs19 Nov 2025
- 2025EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluation8 Nov 2025
- 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents20 Oct 2025
- 2025Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation29 Jul 2025