University students’ intentions to learn artificial intelligence: the roles of supportive environments and expectancy–value beliefs

• Autores: Faming Wang, Ronnel Bornasal King, Ching Sing Chai, Ying Zhou
• Localización: International Journal of Educational Technology in Higher Education, ISSN 2365-9440, Nº. 20, 2023
• Idioma: inglés
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• Resumen

  • Despite the importance of artifcial intelligence (AI) for university students to thrive in the future workplace, few studies have been conducted to assess and foster their intentions to learn AI. Guided by the situated expectancy–value theory, this study adopted both variable- and person-centered approaches to explore the role of supportive environments and expectancy–value beliefs in fostering university students’ intentions to learn AI. The data were drawn from 494 university students. In Study 1, the variable-centered approach of structural equation modeling showed the critical role of supportive environments and expectancy–value beliefs in promoting students’ intentions to learn AI. In Study 2, the person-centered approach of latent profle analysis identifed three subgroups of students based on their levels of supportive environments and expectancy–value beliefs. Consistent with Study 1, students who perceived more supportive environments and higher levels of expectancy–value beliefs had stronger intentions to learn AI. We also documented the infuence of study of feld, gender, and year level on students’ perceptions of supportive environments, expectancy-value beliefs and intentions to learn AI. The implications of these fndings in improving students’ intentions to learn AI are discussed.