Skip to content
Open access

Psychological drivers and barriers affecting students' sustained usage intention of AIGC tools in educational settings: an integrated perspective of Uses and Gratifications Theory and Perceived Value Theory

Aug 2026 · Frontiers in Psychology · Vol 17 · 0 citations · 142 references
Medicine

TL;DR

An integrated model is developed to explain students' continuance intention toward AIGC tools from both motivational and inhibitory perspectives and contributes to understanding the psychological mechanisms underlying students' sustained use of AIGC tools and provide practical implications for their responsible integration into higher education.

Abstract

Introduction This study systematically examines the drivers and barriers influencing students' sustained usage intention toward artificial intelligence-generated content (AIGC) tools in educational settings. As AIGC tools are increasingly introduced into teaching and learning activities, understanding how students accept, evaluate, and continue to use these tools is essential for promoting sustainable digital learning in higher education. Grounded in the Uses and Gratifications Theory and the Perceived Value Theory, this study develops an integrated model to explain students' continuance intention toward AIGC tools from both motivational and inhibitory perspectives. Methods Questionnaire data were collected from 528 Chinese university students and analyzed using partial least squares structural equation modeling (PLS-SEM). The study also examined the moderating role of perceived trust and conducted multi-group analysis to explore differences across student groups. Results Content gratification, utilitarian gratification, and social gratification were significantly positively associated with continued usage intention. Among these factors, learning needs and perceived usefulness showed the strongest positive associations, whereas enjoyment within hedonic gratification showed no significant association. Perceived risk and perceived cost were significantly negatively associated with continued usage intention. Perceived trust significantly moderated the relationships between key gratifications, perceived sacrifices, and sustained usage intention. Multi-group analysis further revealed that postgraduates placed greater emphasis on information retrieval, undergraduates relied more on trust to reduce uncertainty, and infrequent users were more strongly associated with novelty. Discussion These findings contribute to understanding the psychological mechanisms underlying students' sustained use of AIGC tools and provide practical implications for their responsible integration into higher education. Universities and educators should enhance the functional and learning-support value of AIGC tools while establishing clear usage guidelines, strengthening privacy protection, reducing cost barriers, and providing differentiated instructional support.

Read PDF

Similar papers

Review Open access Aug 2026

What predicts university students’ behavioral intention to use education influencers in higher education? A study through the lens of the UTAUT model and self-determination theory

The study suggests that both technology-related perceptions and motivational mechanisms are important in understanding students’ engagement with education influencers in digital learning environments.

Youxue Zhou, Xin Tang · 0 citations
Open access 2026

Understanding the Behavioral Intention of Generative AI Among Art and Design Students: An Integrated Framework of Self-Determination Theory and Technology Acceptance Model

Although the Technology Acceptance Model (TAM) has been widely used to explain users’ adoption of technologies, its explanatory power in creative disciplines such as art and design remains constrained. This limitation arises mainly from the neglect of intrinsic psychological factors associated with creative engagement....

Junhui Sun, Juming Shen · 0 citations
Open access Aug 2026

Understanding the role of perceived drivers and barriers in shaping entrepreneurial intentions: the mediating effect of perceived value among higher education students

Despite the growth of entrepreneurship education and government support for start-up initiatives, Entrepreneurial Intention (EI) among higher education students is still very low in developing countries, such as India. Most of the existing models have studied the motivation and perceived barriers separately, and ha...

Ameerul Hassan S., Syed Noorul Shajar · 0 citations
Review Open access Jul 2026

Predicting higher education students' continued intention and self-reported use of generative AI tools in academic work: An extended UTAUT mixed-methods study.

Students' use of GenAI in academic contexts is shaped by expected usefulness, social influence, and their perceived knowledge of how to use and evaluate AI outputs, showing that students' use of GenAI in academic contexts is shaped by expected usefulness, social influence, and their perceived knowledge of how to use an...

L. J. Du, Zhi Liu, Ning Liao · 0 citations
Review Open access Aug 2026

EDUCATORS’ ACCEPTANCE OF AI-ENABLED TEACHING METHODS IN CHINESE HIGHER EDUCATION: EXTENDING UTAUT WITH PERCEIVED RISK AND DEMOGRAPHIC MODERATORS

This study examines educators’ behavioral intention to adopt AI-enabled teaching methods in China using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework that incorporates perceived risk to validate an extended UTAUT model in a higher-education context.

Tian Yuan, Yue Wang, Bao-Dong Cheng · 0 citations
Open access Jul 2026

Understanding continuance intention towards e-learning platforms: a structural model based on expectation confirmation and individual innovativeness

It is indicated that satisfaction is the strongest predictor of continuance intention toward e-learning platforms, and individual innovativeness and demographic characteristics shape continuance behaviour in online learning environments.

N. Begum, D. Venkatesan · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.