Intelligent Evaluation of Classroom Teaching Language: Effectiveness, Application, and Optimization

Quantitative analysis of classroom teaching language traditionally relies on manual coding, presenting significant challenges such as complexity and labor-intensive workloads, which hinder its applicability to large-scale classroom evaluations. Rapid advancements in artificial intelligence technology have recently offered promising solutions to these issues. Therefore, this study aims to foster the development of high-quality pre-service teachers by providing a technical pathway and empirical evidence for intelligent classroom language evaluation from three key dimensions: effectiveness evaluation, practical application, and intelligent optimization. Initially, an encoding system specifically tailored to the instructional language characteristics of pre-service teachers was developed. Then, an automated encoding model was constructed and tested for feasibility and effectiveness in language coding tasks. Subsequently, this automated encoding model was applied to analyze instructional language differences between pre-service teachers' training videos and exemplary teaching videos, systematically identifying problems and proposing generalized optimization recommendations. Finally, an instructional language optimization assistant was preliminarily established using large language models, offering personalized language guidance to pre-service teachers based on identified optimization strategies. The findings demonstrate that artificial intelligence technologies effectively address the challenges of quantitative instructional language analysis and show significant potential in automation and personalized educational evaluation.