Modeling AI Tool Adoption in Higher Education: The Role of Authentic Learning and Prompting Competence

Abstract

As AI tools are increasingly used in higher education, understanding the factors affecting students’ adoption intentions has become theoretically and practically important. Previous studies have mainly relied on traditional technology acceptance constructs, while comparatively little attention has been given to pedagogical and competence-based conditions shaping students’ cognitive evaluations of AI systems. To address this gap, the current study builds on the technology acceptance model (TAM) by proposing authentic learning (AL) and prompt engineering competence (PEC) as precursors of perceived usefulness (PU), perceived ease of use (PEU), and behavioral intention (BI) to use AI tools. The study was based on data collected from 309 undergraduate students at the University of Ha’il. A two-step structural equation modeling (SEM) approach was employed using AMOS software. Confirmatory factor analysis confirmed construct reliability, convergent validity, and discriminant validity. SEM was then conducted to test the hypotheses. The findings show that AL significantly predicts both PU and PEU, whereas PEC significantly predicts PEU but not PU. Both PU and PEU were found to be important predictors of BI. Bootstrapping results reveal that AL affects BI through PU and PEU, while PEC affects BI entirely through PEU. The results also confirm considerable explanatory power, with an R² of .71 for BI. These findings extend TAM by reconceptualizing AL as a foundational pedagogical precursor influencing AI adoption and by clarifying the unique role of PEC in improving PEU. Integrating pedagogical and competence-based determinants into AI-enabled higher education advances technology acceptance theory and explains the AI adoption mechanism more precisely. The findings provide practical guidance for educators and instructional designers by emphasizing the importance of integrating authentic learning tasks and developing students’ prompt engineering skills to enhance meaningful AI-supported learning.