ABSTRACT

The study examines the application of Artificial Intelligence (AI) tools in the evaluation of teaching and learning for the enhancement of instructional and administrative practices in junior high schools in Beijing. The primary objective is to evaluate the efficacy of adaptive learning technologies, automated assessment systems, and advanced predictive analytics in improving student learning outcomes, optimizing classroom management, and facilitating informed decision-making and resource allocation. A quantitative research design was employed, utilizing closed-ended questionnaires to gather data from 250 junior high school teachers and administrators in Beijing. The findings indicate that AI tools contribute to enhanced teaching and learning by personalizing instruction to meet students’ specific needs, streamlining grading through objective analysis, and supporting strategic planning and forecasting through data-driven insights. These tools alleviate the workload of educators by providing timely and consistent feedback and enabling the early identification and intervention for students requiring additional support. This study contributes to the theoretical frameworks of Constructivist Learning Theory and Systems Theory, emphasizing the interdependence of educational components and learner engagement. Practically, the study advocates for the integration of AI technologies in schools and recommends that policymakers ensure adequate support for AI adoption. Future research directions include narrowing studies to specific geographic regions, utilizing self-report instruments, and investigating the ethical implications and potential adverse effects of AI in education. The results of this study provide a foundational basis for the future integration of AI to enhance educational effectiveness on a global scale.