TY - GEN
T1 - Understanding GenAI Adoption in Higher Education
T2 - 14th International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2025
AU - Chan, Ka Ian
AU - Chan, Ngai Seng
AU - Pang, Patrick Cheong Iao
AU - Tang, Su Kit
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The rapid advancement in generative artificial intelligence (GenAI) has transformed learning, problem-solving, and creative practices in higher education, yielding a blend of both opportunities and challenges. This study investigates university students' adoption of GenAI tools, their satisfaction with these tools, and the resulting behavioural changes by integrating the Uses and Gratifications (U&G) theory with the Technology Acceptance Model (TAM). An extended model was proposed in this study, incorporating motivational needs, technology acceptance factors, and Negative Usage Tendency (NU) as a moderating factor. A quantitative survey involving 237 university students in Macao was conducted, and the data were analysed using Structural Equation Modelling (SEM) and path analysis. Results confirmed the classical TAM pathways, indicating that Perceived Ease of Use (PEOU) influenced Perceived Usefulness (PU), which subsequently predicted Behavioural Intention (BI) and Actual Use (AU). Motivational needs were shown to have a significant impact on PEOU, while NU was found to moderate the relationship between AU and satisfaction. Collectively, these findings advanced knowledge of GenAI adoption, offering theoretical insights into university students' thinking regarding GenAI use. Additionally, limitations and future research directions were further discussed.
AB - The rapid advancement in generative artificial intelligence (GenAI) has transformed learning, problem-solving, and creative practices in higher education, yielding a blend of both opportunities and challenges. This study investigates university students' adoption of GenAI tools, their satisfaction with these tools, and the resulting behavioural changes by integrating the Uses and Gratifications (U&G) theory with the Technology Acceptance Model (TAM). An extended model was proposed in this study, incorporating motivational needs, technology acceptance factors, and Negative Usage Tendency (NU) as a moderating factor. A quantitative survey involving 237 university students in Macao was conducted, and the data were analysed using Structural Equation Modelling (SEM) and path analysis. Results confirmed the classical TAM pathways, indicating that Perceived Ease of Use (PEOU) influenced Perceived Usefulness (PU), which subsequently predicted Behavioural Intention (BI) and Actual Use (AU). Motivational needs were shown to have a significant impact on PEOU, while NU was found to moderate the relationship between AU and satisfaction. Collectively, these findings advanced knowledge of GenAI adoption, offering theoretical insights into university students' thinking regarding GenAI use. Additionally, limitations and future research directions were further discussed.
KW - Educational Technology Adoption
KW - Generative Artificial Intelligence
KW - Negative Usage Tendency
KW - Technology Acceptance Model
KW - Uses and Gratifications Theory
UR - https://www.scopus.com/pages/publications/105033229140
U2 - 10.1109/TALE66047.2025.11346592
DO - 10.1109/TALE66047.2025.11346592
M3 - Conference contribution
AN - SCOPUS:105033229140
T3 - TALE 2025 - 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Proceedings
BT - TALE 2025 - 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 December 2025 through 7 December 2025
ER -