Research on the Construction and Application of Teaching Evaluation Model Based on AI Algorithm

Abstract

Educational evaluation is a core lever for teaching reform and a key support for measuring teaching quality, optimizing teaching processes, and promoting the common development of teachers and students. Traditional teaching evaluation relies heavily on manual qualitative scoring and single-indicator quantitative assessment, resulting in fixed dimensions, subjective interference, low data utilization, delayed feedback, and insufficient personalization. To address these limitations, this study constructs a teaching evaluation model based on artificial intelligence algorithms. A combined weighting method integrating analytic hierarchy process and entropy weight method is used to determine indicator weights, while BP neural networks and random forest algorithms are introduced for fitting, classification, abnormal-data identification, and model verification. A dynamic evaluation index system covering teacher literacy, classroom implementation, student learning status, course-resource construction, and teaching effectiveness is established. The model is tested in university blended classrooms, smart classrooms, and vocational training scenarios. Smart education environments increasingly rely on wireless classroom sensing, intelligent terminals, and electromagnetic data acquisition infrastructures; therefore, the proposed model also considers real-time, multi-source, and technically reliable teaching data collection. The results support more objective, timely, and adaptive teaching evaluation.