mlsanityData to 14 Oct 2026

A LLM-Driven Multi-Agent Systems for Professional Development of Mathematics Teachers

arXiv 2507.05292 ↗First posted 5 Jul 2025cs.CY cs.HC cs.MAcited by 1

On the map: LLM agents and tool use → Educational agents and math problem solving

Abstract (arXiv)

Professional development (PD) serves as the cornerstone for teacher tutors to grasp content knowledge. However, providing equitable and timely PD opportunities for teachers poses significant challenges. To address this issue, we introduce I-VIP (Intelligent Virtual Interactive Program), an intelligent tutoring platform for teacher professional development, driven by large language models (LLMs) and supported by multi-agent frameworks. This platform offers a user-friendly conversational interface and allows users to employ a variety of interactive tools to facilitate question answering, knowledge comprehension, and reflective summarization while engaging in dialogue. To underpin the functionality of this platform, including knowledge expectation analysis, response scoring and classification, and feedback generation, the multi-agent frameworks are leveraged to enhance the accuracy of judgments and mitigate the issue of missing key points.

Key facts. Cited by 1 paper 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 5 of its 16 references that are in the map. First posted on arXiv 5 Jul 2025.

Builds on

5 of 16 references.

References · 5

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  3. 2022A Survey on In-context Learningcited by 721
  4. 2024A LLM-Powered Automatic Grading Framework with Human-Level Guidelines Optimizationcited by 16
  5. 2024Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retrievercited by 6

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligencecited by 170

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  1. 2024Content Knowledge Identification with Multi-Agent Large Language Models (LLMs)
  2. 2025Multi-Agent Collaborative Framework For Math Problem Generation
  3. 2025Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design
  4. 2025FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets
  5. 2025A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents
  6. 2025EduPlanner: LLM-Based Multi-Agent Systems for Customized and Intelligent Instructional Design
  7. 2024Simulating Classroom Education with LLM-Empowered Agents
  8. 2025EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework