More than skills: How AI literacy shapes student motivation in the age of education 4.0

Abstract

This study examines the relationship between students’ AI literacy and their motivations for utilizing AI in academic contexts. Based on Expectancy-Value Theory (EVT), this study examines how students' expectancy beliefs and task value components (attainment, utility, intrinsic or interest, and cost) are influenced by their AI literacy, encompassing awareness, usage, evaluation, and ethics.  Data were collected from Filipino college students who are enrolled in a general education science course and subjected to descriptive statistics, T-tests, ANOVA, correlation, and multiple regression analysis. Results show that the students possess high AI literacy across all its domains, with the highest literacy in terms of awareness. Motivation was generally strong across task value subdomains, but only moderate in terms of expectancy and cost. Female students' intrinsic motivation, evaluative, and ethical awareness were significantly higher than those of male students. However, no significant differences were observed in either AI literacy or motivation across academic programs. Correlation results revealed that all AI literacy dimensions are positively correlated with motivational beliefs, except for cost, which had a negative correlation. Regression analysis showed that usage was the only significant predictor of task value components, revealing significant motivational effects of hands-on AI experience. These findings underscore the significance of experiential, reflective, and inclusive approaches to AI learning in higher education. Institutions are encouraged to design curricula that foster not only technical competencies but also ethical awareness, self-efficacy, and long-term motivation for the responsible and meaningful use of AI in academic settings.