The Impact of Big Data and AI on Teacher Performance Reviews: A Study of Private Higher Vocational Colleges

Abstract

In the quick-changing world of education, the integration of big data analytics and artificial intelligence (AI) has become a revolutionary force. However, it is still completely unknown how these technologies affect teacher performance, particularly in the setting of China's educational system. The purpose of this study was to thoroughly evaluate the effects of using big data analytics and implementing AI on teacher effectiveness in China. In order to provide a complete picture of the intricate dynamics at play, the study set out to clarify both direct effects and the potential interaction of mediating and moderating factors. To collect data, 750 teachers from various Chinese private higher vocational colleges were questioned using a cross-sectional methodology. Participants were chosen using convenience sampling, and data was collected using a standardized survey. To analyze the data, statistical tools were utilized along with descriptive statistics, multiple regression analysis, and moderation analysis. The findings demonstrated that big data analytics and AI adoption had a direct positive impact on teacher performance across multiple aspects of instructional effectiveness, student engagement, and professional development. Additionally, it was shown that data accuracy was a key mediator, suggesting that accurate data-driven insights can magnify the effects of technology on teacher performance. Furthermore, technical literacy appeared as an important moderator, impacting the amount to which technology integration translates to improved educator performance. This study contributes to academic discourse by resolving a research gap and highlighting the relationship between technology and teacher performance. For educators, administrators, and policymakers, the findings have real-world applications that may be used to inform integration plans for technology in the classroom. The study's limitations include potential sample bias due to restricted participant recruitment, reliance on self-reported data susceptible to social desirability bias, and the cross-sectional design, which hinders establishing causal relationships between variables. The study underscores the need for teacher training in technology and data literacy for optimal use of big data analytics and AI in education. Institutions must also prioritize accurate data infrastructure and equitable access to enhance teaching practices and student outcomes. The study shows how accurate data and technological literacy mediate and moderate technology's impact on teaching, providing new theoretical insights. It encourages research into the relationship between data correctness, technological skill, and effective teaching to better comprehend these dynamics.