THE USE OF ARTIFICIAL INTELLIGENCE IN TEACHING AND LEARNING OF STUDENTS IN AGRICULTURAL EDUCATION DEPARTMENT, KING MONGKUT’S INSTITUTE OF TECHNOLOGY LADKRABANG

Abstract

This research investigates the general characteristics, behaviors, and factors influencing the use of Artificial Intelligence (AI) in learning and teaching practicum among students in the Department of Agricultural Education at King Mongkut's Institute of Technology Ladkrabang. The research population consisted of 43 fourth-year undergraduate students enrolled in the Bachelor of Science in Industrial Education program, majoring in Agricultural Education, during the 2025 academic year. The research instrument was a four-part questionnaire. Data were analyzed by means of frequency, percentage, mean, standard deviation, and correlation coefficient. The results revealed that most respondents were female (74.42%) with a cumulative GPA (grade point average) ranging from 3.00 to 3.49. They primarily accessed the internet at home via smartphones for more than 60 minutes per session. Most students agreed that AI tools enhance learning efficiency (95.35%), with the critical thinking factor yielding the highest mean score. The use of AI during learning activities was at a frequent level (mean = 3.54), particularly in online learning contexts. In contrast, AI usage during teaching practicum was at a moderate level (mean = 3.07), mainly for assessment and information searching. The results of the factor analysis indicated that gender, subject group, internet access location, social influence, and rational thinking had a statistically significant impact on the use of artificial intelligence in different aspects of learning at the .05 level. In contrast, expenses, family income, internet access location, perceived usefulness, social influence, and achievement motivation were found to have a statistically significant effect on AI use during teaching practicum at the .05 significance level. To sum up, internet access location, social influence, and achievement motivation were identified as key variables contributing to statistically significant differences in AI use during both learning and teaching practicum. These findings highlight the importance of contextual access and intrinsic motivation in integrating AI into educational practice.