Practical Approaches of Artificial Intelligence Empowering Teaching Evaluation

Abstract

With the deepening reform of educational assessment, exploring the pathways for deep integration of technology and education has become particularly urgent. Against the backdrop of digital technology’s profound integration into educational evaluation, this study focuses on the practical approaches through which artificial intelligence empowers teaching evaluation, addressing both policy directions and real-world challenges. By analyzing the limitations of traditional evaluation models in terms of methods and content, it clarifies how AI drives a shift in the core logic of evaluation from “judgment” to “development,” manifested in data-driven multidimensional dynamic assessment, personalized diagnosis, and the construction of instant feedback loops. Furthermore, it systematically examines innovative practices in technology integration and adaptation to educational contexts across the data layer, algorithm layer, and application layer. Simultaneously, the study emphasizes risk mitigation, proposing strategies such as establishing ethical frameworks and safeguarding human agency, aiming to provide theoretical reference and practical guidance for advancing educational evaluation toward greater scientization, personalization, and diversification.