Adoption of artificial intelligence in higher education: a diffusion of innovation approach

Abstract

Purpose Quality education is part of the United Nations’ goals for 2030, and artificial intelligence (AI) poses both an opportunity and a challenge to achieve it. This study aims to understand the landscape and level of innovativeness when adopting AI. We use the diffusion of innovation theory (DOI) to understand the adoption of AI among students in a Latin American university. Design/methodology/approach A sample of 280 university students at a business school was used to test the hypotheses using Structural Equation Modeling (SEM). Findings The study found that the second largest group, after the “late majority”, is “early adopters.” The results also show that there is a positive relationship between relative advantage, observability, trialability, compatibility and AI adoption. The factor of complexity, however, did not result in significant results, indicating that it does not represent an obstacle for students to adopt AI for learning purposes. Research limitations/implications The findings have important implications for higher education institutions and professionals. This is particularly relevant in a context where students easily adopt digital technology. Practical implications Centennials are a generation that changes slightly the DOI curve. With a good number of early adopters, HEIs need to be prepared in terms of infrastructure and training. Professors should be aware of the size of this group, which is no longer one or two exceptional students. Although they may be used now to use AI for administrative tasks, their instructional and learning use can be more exploited due to the apparent easy adoption by students. Social implications To facilitate the acceptance of AI, the opportunities and challenges presented by the AI revolution must be addressed through collaboration among all stakeholders, including academics, educators, policymakers and professionals. Originality/value Our study focuses on bringing insights from Latin America, a region where few quantitative studies on AI and education have taken place so far. In particular, our study focuses on students and uses SEM (going beyond descriptive statistics). The study also emphasizes the importance of belonging to a generational cohort for technology adoption.

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