Understanding determinants of student behavioral intention in Singapore’s AI education: insights from the situated expectancy-value theory

Recent research in AI education has investigated motivational factors that influence students’ learning intentions and experiences. However, prior studies often combined these factors into a single construct, without differentiating the multi-dimensional facets of value beliefs. Given that each dimension of task value beliefs and cost perceptions independently predicts students’ performance and choices rather than a cumulative effect, the distinction could help researchers to gain a better understanding of motivation mechanism, which in turn, helps educators to design more effective and engaging AI courses that better align with students’ needs and expectations. Guided by situated expectancy-value theory (SEVT) and the theory of planned behavior (TPB), this study investigated the role of supportive environment and expectancy-value-cost beliefs in shaping university students’ behavioral intention in AI education. A total of 609 university students participated in this study. The findings highlighted the significant role of expectancy-value beliefs in motivating students’ intention to learn and use AI. While effort cost negatively affected this intention, emotional cost positively influenced intention to acquire AI knowledge. Additionally, background factors were identified that interact with expectancy-value-cost beliefs to impact behavioral intention. This study provides both theoretical and practical implications for AI education.