Expectancy-Value Beliefs as Predictors of Student Intentions in AI Learning and Application

EasyChair Preprint 15471

Abstract

Despite the growing emphasis on artificial intelligence (AI) education, there is relatively little research on the motivational factors that influence students' intention regarding AI knowledge acquisition and the utilization of AI applications. Understanding these factors not only enhances our knowledge of AI education but also helps educators and researchers to develop appropriate interventions to promote AI learning that align with students’ needs and expectations. Guided by expectancy-value theory and theory of planned behavior, we investigated the role of expectancy-value beliefs in fostering university students’ intentions to learn and use AI. 141 university students participated in this study. Our findings revealed that intrinsic and utility value beliefs played a mediating role in promoting students’ behavioral intentions in AI learning. We also found that while effort cost negatively affected these intentions, opportunity cost positively influenced intentions to acquire AI knowledge and use AI applications. Additionally, we identified gender differences in students’ expectancy-value beliefs, which can inform educators in designing gender-specific interventions to enhance female students’ motivation in AI learning.

Keyphrases: Artificial Intelligence, Theory of Planned Behavior, behavioral intention, expectancy-value theory, gender differences

@booklet{EasyChair:15471,
  author    = {Stella Xin Yin and Dion Hoe-Lian Goh},
  title     = {Expectancy-Value Beliefs as Predictors of Student Intentions in AI Learning and Application},
  howpublished = {EasyChair Preprint 15471},
  year      = {EasyChair, 2024}}