Factors Influencing College Students’ Willingness to Use Generative Artificial Intelligence Tools ——Based on the UTAUT Model

Sigan Li, Hongfeng Zhang, Zhijun Du

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2025 11th International Conference on Education and Training Technologies, ICETT 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-70
Number of pages14
ISBN (Electronic)9798331513511
DOIs
Publication statusPublished - 2025
Event11th International Conference on Education and Training Technologies, ICETT 2025 - Macau, China
Duration: 23 May 202525 May 2025

Publication series

Name2025 11th International Conference on Education and Training Technologies, ICETT 2025

Conference

Conference11th International Conference on Education and Training Technologies, ICETT 2025
Country/TerritoryChina
CityMacau
Period23/05/2525/05/25

Keywords

  • College students
  • Generative AI tools
  • PLS-SEM
  • UTAUT model

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