Research on Comprehensive Classroom Evaluation System Based on Multiple AI Models

2025-06-29

AI Summary

To address the low efficiency, subjectivity, and unidimensionality of traditional classroom evaluation, this study proposes a closed-loop intelligent assessment model based on multimodal data. Methodologically, it integrates computer vision (for teacher–student behavioral analysis), automatic speech recognition (for linguistic interaction extraction), and large language models (for semantic understanding and comment generation), establishing a comprehensive, process-oriented evaluation framework covering both instructional competence and pedagogical effectiveness—and automatically generating evaluation reports and actionable improvement suggestions. Its key contribution lies in the first deep coupling of multimodal perception with large-model-based semantic reasoning, enabling a closed “data acquisition → intelligent analysis → feedback-driven optimization” loop. Experimental results demonstrate significant improvements in evaluation objectivity, timeliness, and multidimensionality. The approach provides a reusable technical pathway and practical paradigm for digital quality assurance in education.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

The promotion of the national education digitalization strategy has facilitated the development of teaching quality evaluation towards all-round, process-oriented, precise, and intelligent directions, inspiring explorations into new methods and technologies for educational quality assurance. Classroom teaching evaluation methods dominated by teaching supervision and student teaching evaluation suffer from issues such as low efficiency, strong subjectivity, and limited evaluation dimensions. How to further advance intelligent and objective evaluation remains a topic to be explored. This paper, based on image recognition technology, speech recognition technology, and AI large language models, develops a comprehensive evaluation system that automatically generates evaluation reports and optimization suggestions from two dimensions: teacher teaching ability and classroom teaching effectiveness. This study establishes a closed-loop classroom evaluation model that comprehensively evaluates student and teaching conditions based on multi-dimensional data throughout the classroom teaching process, and further analyzes the data to guide teaching improvement. It meets the requirements of all-round and process-oriented classroom evaluation in the era of digital education, effectively solves the main problems of manual evaluation methods, and provides data collection and analysis methods as well as technologies for relevant research on educational teaching evaluation.

Problem

• Develops AI-based system for comprehensive classroom teaching evaluation
• Addresses inefficiency and subjectivity in traditional evaluation methods
• Automates report generation and improvement suggestions for teaching quality

Innovation

• Uses image and speech recognition technologies
• Leverages AI large language models
• Automates evaluation report generation