Understanding secondary school students’ intentions to learn artificial intelligence: a multigroup structural equation modeling analysis

As artificial intelligence (AI) continues to play an expanding role in everyday activities, AI-related knowledge and skills are recognized as essential 21st-century competencies. Although K-12 AI curricula have been prioritized by educational authorities worldwide, limited research has examined the motivational mechanisms that underpin students’ intentions to engage in AI learning. The present study broadens the Theory of Planned Behavior by integrating self-efficacy, attitude toward using AI, subjective norms, perceived usefulness, and AI literacy to examine students’ intention to participate in AI learning at the secondary level. The study also examines whether gender, grade level, school location, and extracurricular AI learning experience shape these relationships. Participants consisted of 632 secondary school students from Zhejiang Province, China. Students’ intention to learn AI was significantly explained by self-efficacy, attitudes toward AI use, and perceived usefulness according to SEM results. Self-efficacy and perceived usefulness also exerted significant positive effects on attitudes toward AI use. By contrast, while AI literacy and subjective norms significantly affected self-efficacy and perceived usefulness, their direct effects on attitudes toward AI use were not significant. The multigroup analysis further revealed that gender did not function as a significant moderator, whereas grade level, school location, and extracurricular AI learning experience did. Practical implications for educators and policymakers emerge from these findings, particularly for the development of inclusive AI learning programs.