This study investigates factors that enhance teachers' intention to accept AI learning in order to effectively apply AI technology in instruction and prepare students for future-oriented education. A total of 148 secondary school teachers from Seoul, the metropolitan area, and the Chungcheong region participated, and analyses were conducted using the Process Macro model.
The findings revealed several key points. First, teaching experience did not significantly influence teachers' AI learning acceptance intention or actual instructional behavior. In contrast, both learning agility and the frequency of AI training significantly increased acceptance intention, though learning agility alone did not directly predict instructional behavior. Second, when teachers were categorized into developmental stages—novice, growth, maturity, and mastery—based on years of teaching experience, no significant differences emerged in training frequency. However, notable differences appeared in developmental characteristics, such as tendencies toward survival and frustration. Third, AI learning acceptance intention was found to mediate the relationship between learning agility and instructional behavior, demonstrating its central role. Fourth, among teachers who reported frequent AI training, the indirect effect on instructional behavior through acceptance intention was particularly strong, highlighting the importance of training as a facilitator of AI-related practice.
These findings underscore that enhancing AI-integrated instructional competence requires more than simply increasing the number of training sessions. Instead, training programs should be designed to reflect teachers' developmental characteristics and professional contexts. Furthermore, cultivating learning agility is essential, as it strengthens the intention to engage with AI and thereby fosters the transition from intention to practice. This study provides meaningful insights for designing teacher professional development and for shaping educational policies that promote instructional expertise in the era of AI-based educational innovation