Motivation Meets Technology: An Integrated SDT–TAM Framework for Artificial Intelligence Adoption in Higher Education

Abstract

The rapid advancement of artificial intelligence (AI), particularly through large language models, has significantly transformed learning practices in higher education. Although AI technologies offer substantial potential to enhance academic performance, learning personalisation, and graduate employability, students’ adoption and sustained use of AI remain uneven, especially within developing higher education contexts. Existing studies on AI adoption have largely relied on the Technology Acceptance Model (TAM) or Self-Determination Theory (SDT) in isolation and have primarily focused on initial adoption using quantitative approaches, leaving long-term motivational mechanisms underexplored. Addressing this gap, this conceptual paper proposes an integrative framework that synthesises TAM and SDT to explain students’ behavioural intention to use AI and their actual AI usage in higher education. Drawing on TAM, the framework highlights the roles of perceived usefulness and perceived ease of use as proximal cognitive determinants of AI adoption. Complementing this perspective, SDT introduces autonomy, competence, and relatedness as distal motivational drivers that shape students’ technology-related perceptions and sustained engagement. By positioning autonomous motivation as an antecedent to TAM beliefs, the integrated framework provides a more comprehensive explanation of both the “why” and “how” of AI adoption. The framework further links behavioural intention to actual AI usage and academic performance, emphasising the role of motivation in fostering meaningful and persistent engagement with AI tools.