Research on AI-Empowered Precision Training Strategies for Rural Teachers

Abstract

With the rapid development of information technology, artificial intelligence (AI) has become increasingly prevalent in education, yet rural teacher training still faces challenges such as insufficient resources, homogenized content, and difficulty matching training to individual needs. Focusing on AI-empowered precision training for rural teachers, this study constructs an indicator system grounded in precision-training theory, designs an intelligent training framework, and pilots a system prototype in model rural schools. First, we gathered teachers' needs and existing pain points through questionnaires and interviews, then applied machine-learning algorithms to develop multidimensional profiles of teaching ability, subject-matter knowledge, and professional aspirations. Based on these profiles, we used recommendation systems and intelligent instructional-analytics technologies to deliver customized courses and practical guidance. Finally, an empirical analysis compared experimental and control groups on teaching effectiveness, satisfaction, and professional-growth rates. Results indicate that introducing an AI-driven precision training mechanism significantly enhanced teaching ability, increased participation by 25%, improved course-match accuracy by 30%, and effectively supported teachers' ongoing professional development. Theoretical and practical optimization strategies and paths for broader adoption are proposed, offering reference for training-program innovators.