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· 129 references
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
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.
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.
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.
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.
This study investigates the factors influencing university students’ behavioral intentions to use education influencers by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) and Self-Determination Theory (SDT). A quantitative survey was conducted with 335 university students in China, and the collected data were analyzed using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4. The findings showed that performance expectancy, social influence, and self-determination significantly influenced behavioral intention. In addition, effort expectancy, social influence, and facilitating conditions significantly influenced self-determination. However, effort expectancy and facilitating conditions did not directly affect behavioral intention. The results further indicated that self-determination contributed to explaining the motivational mechanisms underlying university students’ intentions to use education influencers. Overall, the study suggests that both technology-related perceptions and motivational mechanisms are important in understanding students’ engagement with education influencers in digital learning environments. The findings provide practical implications for educators, higher education institutions, and education influencers seeking to support university students’ learning experiences through social media-based educational content.
Youxue Zhou, Xin Tang· Frontiers in Psychology· 0 citations
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. To address this gap, this study developed an integrated model combining self-determination theory (SDT) with the TAM to examine the psychological mechanisms underlying art and design students’ adoption of generative artificial intelligence (AI). A total of 398 valid responses collected from art and design students were analyzed using structural equation modeling (SEM). The analysis showed that intrinsic motivation was positively associated with behavioral intention, and its standardized path coefficient was numerically larger than those of the two classical TAM variables: perceived usefulness and perceived ease of use. The results also indicated that psychological need satisfaction was indirectly associated with behavioral intention through intrinsic motivation and cognitive evaluations. Theoretically, this study contextualizes and empirically tests the integrated SDT-TAM framework within art and design education. Practically, it has implications for educators seeking to support the responsible integration of generative AI into art and design education.
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 have not explored the integrative value-formation mechanism that links these factors together to influence the intention. In this study, Value Based Adoption Model (VAM) is extended to the domain of career decision to understand how students develop their entrepreneurial intentions by cognitively integrating perceived drivers (Benefits) with perceived barriers (Sacrifices) with the mediating variable of Perceived Value (PV) in selected districts of Tamil Nadu.
A structured questionnaire was administered to 310 undergraduate and postgraduate students across five districts of Tamil Nadu. The model incorporates three benefit dimensions Perceived Usefulness (PU), Perceived Enjoyment (PE), and Perceived Immersion (PI) and three sacrifice dimensions Perceived Cost (PC), Perceived Complexity (PX), and Technicality (PT) with PV mediating the path to EI. PLS-SEM via SmartPLS 4 was used for data analysis, including PLSpredict out-of-sample assessment.
The measurement model demonstrated acceptable reliability (
ρ
c ≥ 0.808) and convergent validity (AVE ≥ 0.584). PU (
β
= 0.124,
p
= 0.004) and PI (
β
= 0.136,
p
= 0.002) positively influenced PV (H1a, H1c supported); PE was not significant (H1b not supported). PC negatively influenced PV (
β
= −0.165,
p
= 0.005; H2a supported). PX (
β
= 0.528) and PT (
β
= 0.334) showed significant but positive effects on PV, contrary to H2b and H2c, attributed to suppressor dynamics among correlated barrier constructs. PV strongly predicts EI (
β
= 0.670,
p
< 0.001; R
2
= 0.449; H3 supported) and mediates key antecedent paths (H4a, H4c, H4d supported; H4b not supported). PLSpredict confirms positive Q
2
predict values for all EI indicators.
This study is among the first PLS-SEM-based investigations to apply VAM's benefit-sacrifice-value architecture to the entrepreneurship intention domain. Nine of thirteen directional hypotheses were supported as predicted. Findings offer actionable guidance for educators, policymakers, and incubation managers in Tamil Nadu and comparable developing-economy contexts.
This study investigated the factors shaping Chinese higher education students' continued intention and self-reported use of generative artificial intelligence (GenAI) tools in academic work. Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study examined the roles of performance expectancy, effort expectancy, social influence, facilitating conditions, behavioural intention, use behaviour, and perceived knowledge. An explanatory sequential mixed-methods design was adopted. In the quantitative phase, survey data from 580 university students were analysed using structural equation modelling. In the qualitative phase, follow-up semi-structured interviews were conducted to explain and elaborate on the quantitative findings. The results showed that performance expectancy, social influence, and perceived knowledge significantly predicted students' behavioural intention to use GenAI, while facilitating conditions and behavioural intention were associated with self-reported use behaviour. Effort expectancy did not significantly predict behavioural intention, suggesting that ease of use alone may be insufficient to explain continued GenAI use among students who already perceive such tools as accessible. Interview findings further indicated that students used GenAI mainly for brainstorming, proofreading, explanation, and problem-solving, but they also expressed concerns about over-reliance, academic integrity, and the reliability of AI-generated content. The study contributes to GenAI continued-use research by showing that 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. Practical implications are provided for designing AI literacy training, classroom guidance, and institutional policies for responsible GenAI use.
L. J. Du, Zhi Liu, Ning Liao· Acta Psychologica· 0 citations
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. Survey data were collected from 312 higher-education teachers and analyzed using confirmatory factor analysis and structural equation modeling, followed by multi-group comparisons across gender, age, educational background, and teaching experience. The results indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly and positively predict educators’ intention to adopt AI-enabled teaching methods, whereas perceived risk exerts a significant negative effect. The proposed model explains 63.7% of the variance in behavioral intention. Multi-group analyses further suggest that age, educational background, and teaching experience moderate several relationships in the model, while gender does not show statistically significant moderation. These findings highlight the importance of demonstrating pedagogical value and reducing implementation barriers through institutional infrastructure, technical support, and targeted professional development, alongside transparent governance mechanisms for privacy, ethics, and data protection to mitigate risk perceptions. This study contributes to the AI-in-education adoption literature by validating an extended UTAUT model in a higher-education context and offers actionable implications for universities and policymakers aiming to promote responsible and scalable integration of AI into teaching practices.
Tian Yuan, Yue Wang, Baodong Cheng· World Journal of Educational...· 0 citations
The rapid growth of e-learning platforms in higher education has increased the need to understand the factors influencing students’ continuance intention toward online learning systems. Drawing upon Expectation Confirmation Theory and integrating individual innovativeness, this study examines the relationships among expectation confirmation, perceived usefulness, satisfaction, and continuance intention, with gender as a moderating variable.
A quantitative research design was employed. Data were collected from 166 students with prior e-learning experience in Indian higher education institutions using a structured questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyse the measurement and structural models.
The findings indicate that satisfaction is the strongest predictor of continuance intention toward e-learning platforms. Expectation confirmation and perceived usefulness significantly enhance satisfaction; however, their direct effects on continuance intention were not significant. Mediation analysis revealed that satisfaction fully mediates the relationships between expectation confirmation, perceived usefulness, and continuance intention. Additionally, individual innovativeness demonstrated a significant negative direct effect on continuance intention, while gender significantly moderated the relationship between innovativeness and continuance intention.
The study extends post-adoption e-learning research by highlighting the central role of satisfaction in sustaining continued usage of e-learning platforms. The findings further suggest that individual innovativeness and demographic characteristics shape continuance behaviour in online learning environments. The study offers theoretical contributions to continuance intention literature and practical implications for higher education institutions and e-learning platform developers seeking to improve long-term learner engagement.
N. Begum, D. Venkatesan· Frontiers in Education· 0 citations
