Rural Teachers’ Information-Based Teaching Ability in AI-Supported: A Survey Study

Abstract

Artificial intelligence (AI) technology is rapidly transforming the educational landscape. In this context, enhancing rural teachers' pedagogical competence in intelligent environments has become pivotal for the effective integration of AI into rural education. Drawing on the TPACK framework, this study conducted a questionnaire survey among rural teachers in China, aiming to systematically investigate the current status and structural differences in their AI-integrated instructional competence. The findings reveal that rural teachers possess a strong foundation in traditional disciplinary content understanding and pedagogical application, but their competence in effectively integrating AI technology into instruction remains relatively underdeveloped. Analysis of group differences indicates no significant disparities across dimensions based on gender. Teaching experience exhibited non-linear characteristics, with early-career teachers demonstrating higher scores in AI-related technological dimensions, while mid-career teachers performed relatively weaker. Professional title significantly influenced AI-related technological knowledge and pedagogical integration competence, showing a certain advantage for junior teachers. Educational background mainly affected technological knowledge, with minimal impact on other dimensions. Consequently, the findings highlight that significant group disparities exist in rural teachers' pedagogical competence development within the AI context. Therefore, future teacher training initiatives should adopt a tiered and differentiated approach, focusing on the specific application of AI technologies in teaching scenarios, and precisely enhancing the AI instructional competence of teachers across different seniority and professional title groups, to facilitate the effective application and deep integration of AI technology in rural educational settings.