Predicting postgraduate students’ technology acceptance using artificial intelligence: a technology acceptance model perspective

The COVID-19 pandemic caused a rapid transition to digital learning which significantly impacted how postgraduate students engaged with technology in their research. This study explores the alignment between postgraduate research and information communication technology strategies, using modified Technology Acceptance Model (TAM) and Activity Theories. It aims to predict postgraduates’ intention to use ICT tools for data collection post-pandemic, offering educators and tech decision-makers insights into technology acceptance, examining the primary, secondary and tertiary factors by utilising artificial intelligence. Participants included Masters, MPhil and PhD students from 2020–2022. Using seven Machine Learning techniques, the study demonstrates the efficacy of Random Forest (RF), Extra Tree (ET) and Logistic Regression (LR) in predicting postgraduates’ intention, with accuracies of 82.58%, 81.82% and 78.03%, respectively. Findings indicate that age, experience (convenience, ICT quality, transaction quality), and interest in legal and security aspects influence postgraduates’ intention to use online learning post-COVID-19. Notably, age emerged as a reliable predictor, contrasting with the limited impact of online mentoring on intention to use ICTs.