What motivates university students to learn AI?: The role of competency and interest in educational demand

Abstract

As artificial intelligence (AI) becomes increasingly embedded in university curricula, identifying factors associated with students’ motivation to learn AI has become an important issue in higher education. This study surveyed 1,474 undergraduate students and used structural equation modeling with a validated five-construct framework to examine how AI knowledge and practical use of AI tools were related to students’ perceived demand for AI learning. The results showed that foundational AI knowledge was positively associated with educational demand both directly (β = 0.401, p < 0.001) and indirectly, primarily mediated by learning interest (β = 0.226, p < 0.001). In contrast, practical AI tool competency was not directly associated with educational demand; however, it was indirectly related to learning interest, whereas general optimism about AI was not necessarily associated with a stronger demand for formal AI learning. Multi-group analyses also indicated experience-based differences: students without prior AI learning experience showed a stronger association between hands-on tool interaction and interest, whereas students with prior AI learning experience showed a stronger association between existing AI knowledge and educational motivation. Overall, the findings suggest that higher-education AI literacy programs may benefit from cultivating students’ interest in and engagement with learning, rather than assuming that tool exposure or future-oriented attitudes alone will lead to stronger demand for AI learning.

Keywords

University students / Artificial intelligence literacy / Prior artificial intelligence experience / Learning interests / Learning motivation

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