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

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arxiv 2507.05292 v1 pith:2V4O3PHQ submitted 2025-07-05 cs.CY cs.HCcs.MA

Kaiqi Yang , Hang Li , Yucheng Chu , Ahreum Han , Yasemin Copur-Gencturk , Jiliang Tang , Hui Liu This is my paper
classification cs.CYcs.HCcs.MA
keywords developmentknowledgemulti-agentplatformprofessionalframeworksintelligentinteractive
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles

    cs.HC 2026-05 unverdicted novelty 5.0 of 10

    LLM-simulated ADHD student personas show stable self-reported traits but behavioral drift in unscripted interactions that explicit task prompts fully eliminate.

  2. LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles

    cs.HC 2026-05 unverdicted novelty 5.0 of 10

    LLM student personas with ADHD show stable self-reported traits at high intensity but behavioral drift in unscripted interactions that scripted prompts eliminate.

  3. A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

    cs.AI 2025-07 accept novelty 4.0 of 10

    The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.

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