Saudi EFL Preparatory-Year Students’ Perceptions of AI Tools for Vocabulary Learning: Insights from TAM and CLT

Abstract

The integration of AI technologies into language learning has become increasingly popular, offering new possibilities and challenges. However, the use of AI tools for vocabulary learning in EFL contexts remains underexplored. Grounded in the Technology Acceptance Model (TAM) and Cognitive Load Theory (CLT), this study explored Saudi female preparatory-year EFL students’ views on using AI tools for vocabulary learning. Using an explanatory sequential mixed-methods approach, a survey was administered to 165 Saudi female preparatory-year students, followed by semi-structured interviews. Quantitative findings showed favorable perceptions of ease of use and usefulness, along with high ratings for germane cognitive load and effective management of intrinsic cognitive load. However, some students reported occasional overload from irrelevant information. Qualitative findings revealed that students’ perceptions were shaped by individual, technological, and social and institutional factors. The findings suggest that AI tools have the potential to optimize vocabulary learning and support cognitive load management. Nevertheless, effective integration requires addressing concerns related to overreliance, information overload, and unclear institutional guidelines through explicit instruction and clear policies.