Student motivation to use generative artificial intelligence in higher education: a grounded theory approach

Abstract

Purpose – This paper aims to understand students’ motivation toward generative artificial intelligence (GAI) in their academic activities, thereby identifying the need for creating a roadmap for the responsible use of GAI by students in higher education. The paper also examines the use of Victor Vroom’s theory as an appropriate model over other adoption model. Design/methodology/approach – The study uses a grounded theory approach wherein the qualitative findings are considered to understand the student motivation, which is driven by Victor Vroom’s theory of motivation. Under this, the properties and the different categories were developed in three phases – open coding, axial coding and selective coding. A total of 48 management students from India were interviewed through a semi-structured approach. Findings – The findings emphasise that the motivation to use GAI tools can be intrinsic, extrinsic or forced. Based upon the coding of the data, the core categories related to motivation, expectancy, instrumentality and valence were created as supported by Victor Vroom’s theory. Practical implications – The current study supports that GAI tools have considerable benefits for higher education management students. The students prefer to use GAI tools which are easily accessible and convenient to use, and improve academic performance to contribute to academic success. These platforms support personalised learning and query handling, enhance student engagement and learning efficiency as well as provide timely and specific solutions to the students. Originality/value – The theoretical gap that is aligned with the need for creating a roadmap of acceptance of GAI by the students in the higher education field has been addressed, wherein the focus is to understand the motivation of higher education students to use GAI tools. To the best of the authors’ knowledge, this study is one of the pioneers to use Victor Vroom’s motivational theory in the education domain. The paper proposes an integrated model that can be used by academic institutions to build a robust AI interface for students.