Construction of an Educational Teaching Evaluation System under the Context of Deep Integration of Artificial Intelligence and Teaching Practices - Tianru Zhang; Huangjia Wu; Yixin Feng; Quang Zhang; Myeongsu Seong; Jie Sun; Mark Leach; Eng Gee Lim (Tecno Scientifica Publisher, 2026)
In an era marked by the deep integration of artificial intelligence (AI) with educational practices, this study explores the transformation and optimization of educational teaching evaluation systems. Recognizing the pivotal role of AI in reshaping teaching and learning environments, the research delves into the design of a comprehensive evaluation framework that aligns with the dynamic nature of contemporary education. It emphasizes the integration of AI-driven tools and methodologies to enhance the accuracy, efficiency, and fairness of educational teaching evaluation. The study proposes a multifaceted approach, including categorized indicator setting, process-oriented evaluation, multi-stakeholder involvement, broadened evaluation perspectives, and dynamic student performance monitoring. Through a critical analysis of existing practices and theoretical frameworks, a model is proposed to foster a more adaptive, equitable, and student-centered educational landscape. The ultimate goal is to harness AI’s potential to elevate educational outcomes and promote continuous improvement in teaching practices.