Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250400056-7.doi: 10.11896/jsjkx.250400056

• Artificial Intelligence • Previous Articles     Next Articles

Cognitive LLM Agent for Mater Education Based on Hybrid Reasoning

ZHENG Jiaqi1, PENG Shihao1, ZHAO Junjie2, HONG Daocheng1, ZHU Dandan1, SANG Jinqiu1, ZHANG Guixu1   

  1. 1 School of Computer Science and Technology,East China Normal University,Shanghai 200062,China
    2 School of Information and Electronic Engineering,Zhejiang Gongshang University,Hangzhou 310000,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:ZHENG Jiaqi,born in 2002,postgra-duate.His main research interests include large language model and saliency prediction.
    HONG Daocheng,born in 1981,Ph.D,associate professor,is a member of CCF(No.E5370M).His main research interest is intelligent information proces-sing and application.
  • Supported by:
    National Natural Science Foundation of China(62577023,61977025,L2224002).

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Abstract: LLM-driven agents have led a cognitive revolution in education,transforming traditional static Q&A systems into digital tutors with dynamic knowledge integration and intelligent interaction capabilities.However,existing general-purpose LLMs face significant challenges in the context of master popularization education,including knowledge hallucination,static knowledge representation limitations,and insufficient adaptability of teaching strategies.To address these challenges,this paper proposes the Master Education Cognitive LLM Agent(MECA),chatMaster,based on a three-layer cognitive architecture comprising a perception layer,reasoning layer and action layer.MECA establishes a closed-loop decision-making paradigm of “Intention Perception → Knowledge Reasoning → Educational Execution.”It introduces a dynamic cognitive enhancement mechanism for improving the precision of knowledge generation and personalized adaptation capabilities.In the proposed framework,the perception layer employs a lightweight language model to analyze user intent,while the reasoning layer incorporates an intent-enhanced hybrid reasoning mechanism that integrates domain knowledge,user needs,and teaching strategies for multi-dimensional reasoning.To build a solid data foundation,it develops the first high-quality Q&A dataset specifically for master education information in China,leveraging both human review and LLM-based language understanding.This dataset covers renowned scholars,educators and academicians in Shanghai,focusing on key figures with significant contributions.It encompasses multiple dimensions,including biographical details,academic ideologies,and educational contributions,addressing the lack of high-quality domain-specific corpora in this field.Experimental results demonstrate that chatMaster achieves significant improvements across multiple evaluation metrics,enhancing the cognitive interaction capabilities and precision of knowledge dissemination in master popularization education information.This research provides a generalizable paradigm for constructing educational agents,promoting the evolution of intelligent education towards greater specialization and dynamism.

Key words: Agent system, Hybrid reasoning, Master popularization education, Prompt engineering, Intent analysis

CLC Number: 

  • TP182
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