Rubrics-enhanced multi-agent system based on LLM for teaching behavior assessment: Design and application.

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Bibliographic Details
Title:	Rubrics-enhanced multi-agent system based on LLM for teaching behavior assessment: Design and application.
Authors:	Niu, Juan¹ 2023200348@snnu.edu.cn, He, Xiuqin² xiuqing@snnu.edu.cn, Wang, Xu¹ mitm0829@snnu.edu.cn, Han, Guangxin¹ hgx@snnu.edu.cn, He, Juhou¹ juhouh@snnu.edu.cn
Source:	Educational Technology & Society. Apr2026, Vol. 29 Issue 2, p270-296. 27p.
Subject Terms:	Scoring rubrics, Behavioral assessment, Psychological feedback, Educational technology, *Teacher development, Multiagent systems, Language models
Abstract:	Consistent feedback and accurate assessments are essential for teachers’ professional development; however, the scarcity of experts often prevents teachers from receiving timely and targeted assessments in teaching practice. The emergence of multi-agent systems based on Large Language Models (LLMs) offers a promising solution to this challenge with its flexible collaboration mechanism. We developed a rubric-enhanced multi-agent system for assessing teaching behaviors, designated as AI-TBAS (AI-driven Teaching Behavior Assessment System), which encompasses the processes of rubric development, preprocessing of teaching video data, and the construction of a multi-agent system. The evaluation results of revealed that rubrics enhance the performance of AI-TBAS, and the outputs of its multi-agent framework outperformed those of the single-agent framework, aligning more closely with expert assessments. The findings of the empirical study involving 30 teachers illustrate that AI-TBAS can offer personalized feedback on teaching behaviors. Additionally, an investigation revealed that both teachers and students perceived the AI-TBAS as beneficial for improving teaching practices, exhibiting a positive attitude towards its implementation. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	Consistent feedback and accurate assessments are essential for teachers’ professional development; however, the scarcity of experts often prevents teachers from receiving timely and targeted assessments in teaching practice. The emergence of multi-agent systems based on Large Language Models (LLMs) offers a promising solution to this challenge with its flexible collaboration mechanism. We developed a rubric-enhanced multi-agent system for assessing teaching behaviors, designated as AI-TBAS (AI-driven Teaching Behavior Assessment System), which encompasses the processes of rubric development, preprocessing of teaching video data, and the construction of a multi-agent system. The evaluation results of revealed that rubrics enhance the performance of AI-TBAS, and the outputs of its multi-agent framework outperformed those of the single-agent framework, aligning more closely with expert assessments. The findings of the empirical study involving 30 teachers illustrate that AI-TBAS can offer personalized feedback on teaching behaviors. Additionally, an investigation revealed that both teachers and students perceived the AI-TBAS as beneficial for improving teaching practices, exhibiting a positive attitude towards its implementation. [ABSTRACT FROM AUTHOR]
ISSN:	11763647
DOI:	10.30191/ETS.202604_29(2).SP04