TY - GEN
T1 - Factors Influencing College Students’ Willingness to Use Generative Artificial Intelligence Tools ——Based on the UTAUT Model
AU - Li, Sigan
AU - Zhang, Hongfeng
AU - Du, Zhijun
N1 - Publisher Copyright:
©2025 IEEE.
PY - 2025
Y1 - 2025
N2 - In the wave of technological innovation, generative artificial intelligence tools provide college students with personalized learning experience, promote autonomous learning and exploratory learning, and improve learning outcomes through intelligent tutoring and instant feedback. However, existing research shows that generative artificial intelligence tools still face multiple obstacles in practical application, such as differences in students' acceptance of new technologies, privacy protection challenges, and difficulties in integrating with the existing education system. This study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT), a technology acceptance model, and specifically introduces perceived risk as a moderating variable. Combining key factors such as college students' cognitive characteristics and technological environment, this study constructs a theoretical model that affects college students' willingness to use generative artificial intelligence tools. The study found that performance expectations, effort expectations, social influence, and convenience conditions have a significant positive impact on college students' willingness to use generative artificial intelligence tools; effort expectations have a positive impact on performance expectations; convenience conditions have a positive impact on effort expectations; effort expectations mediate the relationship between convenience conditions and willingness to use; and perceived risk negatively moderates the impact of convenience conditions on willingness to use. It is expected that through this empirical study, the key factors that affect college students' willingness to use generative artificial intelligence tools will be deeply explored, and valuable suggestions and references will be provided for guiding technology optimization and education integration practices, which will help develop generative artificial intelligence tools that better meet the needs of college students and promote innovation and improvement of online education models.
AB - In the wave of technological innovation, generative artificial intelligence tools provide college students with personalized learning experience, promote autonomous learning and exploratory learning, and improve learning outcomes through intelligent tutoring and instant feedback. However, existing research shows that generative artificial intelligence tools still face multiple obstacles in practical application, such as differences in students' acceptance of new technologies, privacy protection challenges, and difficulties in integrating with the existing education system. This study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT), a technology acceptance model, and specifically introduces perceived risk as a moderating variable. Combining key factors such as college students' cognitive characteristics and technological environment, this study constructs a theoretical model that affects college students' willingness to use generative artificial intelligence tools. The study found that performance expectations, effort expectations, social influence, and convenience conditions have a significant positive impact on college students' willingness to use generative artificial intelligence tools; effort expectations have a positive impact on performance expectations; convenience conditions have a positive impact on effort expectations; effort expectations mediate the relationship between convenience conditions and willingness to use; and perceived risk negatively moderates the impact of convenience conditions on willingness to use. It is expected that through this empirical study, the key factors that affect college students' willingness to use generative artificial intelligence tools will be deeply explored, and valuable suggestions and references will be provided for guiding technology optimization and education integration practices, which will help develop generative artificial intelligence tools that better meet the needs of college students and promote innovation and improvement of online education models.
KW - College students
KW - Generative AI tools
KW - PLS-SEM
KW - UTAUT model
UR - https://www.scopus.com/pages/publications/105016565006
U2 - 10.1109/ICETT66247.2025.11136950
DO - 10.1109/ICETT66247.2025.11136950
M3 - Conference contribution
AN - SCOPUS:105016565006
T3 - 2025 11th International Conference on Education and Training Technologies, ICETT 2025
SP - 57
EP - 70
BT - 2025 11th International Conference on Education and Training Technologies, ICETT 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Education and Training Technologies, ICETT 2025
Y2 - 23 May 2025 through 25 May 2025
ER -