Innovation and Adaptation of Artificial Intelligence in Teaching and Learning of Smart Agricultural Economics Education

This study explores the integration of Artificial Intelligence (AI) in teaching and learning Smart Agricultural Economics, examining its impact on student engagement, learning outcomes, and educational efficiency. A mixed-methods approach was employed to collect data for the study, combining surveys, interviews, and content analysis to select 200 respondents among educators, students, and other experts. Data were analyzed using descriptive statistics, multiple regression, and ANCOVA. The results revealed that the coefficient for age was negative and significant at 5% (p = 0.0341), indicating that older educators tend to have less favorable perceptions toward AI-powered learning platforms, while the coefficient for teaching experience is positive and significant at 1% (p = 0.0093), suggesting that educators with more teaching experience tend to have more favorable perceptions toward AI-powered learning platforms. The high R-squared value (0.816) indicates that the independent variables explain about 81.6% of the variation in perceptions toward AI-powered learning platforms. The study concludes that the integration of AI in teaching and learning Smart Agricultural Economics has the potential to revolutionize the educational landscape. The findings suggest that AI-powered learning platforms and tools can enhance student engagement, learning outcomes, and educational efficiency, noting the technical, pedagogical, and institutional challenges. The study recommends the development of AI-powered learning platforms and tools, capacity building for educators, and policy support for AI integration in Agricultural Economics education.

Keywords: Artificial Intelligence, Smart Agricultural Economics, Teaching and Learning, Educational Efficiency, Student Engagement.