Findings provide theoretical support for context-sensitive strategies in adult learning and the educational application of GenAI, and different gender groups exhibit significant differences in most structural paths of the research model.
Abstract
Introduction Generative AI (GenAI) technology has been rapidly integrated into diverse domains, including education. It has fundamentally reshaped the global learning landscape, exerting a significantly positive influence on students’ learning experience. However, the factors influencing adult learners’ behavioral intention to use GenAI have not yet been fully understood. Therefore, this study aims to explore key factors influencing the behavioral intention to use GenAI among adult learners from central Taiwan. Based on the unified theory of acceptance and use of technology (UTAUT) model, this study integrated two constructs—GenAI identity and trust—to extend the model. Methods A structured questionnaire survey was performed to collect data from 718 adult learners who were aged 50 or above and from central Taiwan. Partial least squares (PLS) and Partial least squares-Multi-group analyses (PLS-MGA) were employed for data analysis. Results The following results were obtained: (1) social influence and facilitating conditions are significant antecedent factors influencing adult learners’ behavioral intention to use GenAI—conversely, performance expectancy has a negative influence on such intention; (2) GenAI identity and trust are key predictive variables of performance expectancy and effort expectancy; (3) different gender groups exhibit significant differences in most structural paths of the research model. Discussion These findings provide theoretical support for context-sensitive strategies in adult learning and the educational application of GenAI.
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.
This study examined GenAI acceptance among private vocational teachers in Thailand by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) with three AI-specific constructs: AI Literacy, Trust, and Perceived AI Risk and tested Behavioral Intention as an antecedent of Digital Competency.
Generative artificial intelligence (GenAI) has shown potential in supporting academic writing, yet limited research addressed the intention and actual use of this technology by foreign language students. To address this gap, the study employs an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model by integrating AI self-efficacy and AI trust to investigate the factors influencing EFL students’ intention and actual use of GenAI in academic writing. An online survey using a cross-sectional design involving 481 purposively selected EFL students from Indonesian universities was analyzed using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4. The results revealed that performance expectancy, effort expectancy, social influence, habit, and AI self-efficacy significantly shaped EFL students’ intentions to use GenAI for academic writing, while behavioral intention, habit, and AI self-efficacy significantly influence actual use. In contrast, AI trust, facilitating conditions, and hedonic motivation show no significant effect on either intention or actual use. The model demonstrated strong explanatory power, with R² values of 0.806 for behavioral intention and 0.617 for actual use. The study enhances the explanatory power of UTAUT2 in GenAI-supported academic writing and offers practical insights for pedagogy and policy to promote GenAI literacy, critical evaluation skills, and responsible use in academic writing.
Generative artificial intelligence (GenAI) is rapidly reshaping higher education and students’ academic writing practices. However, existing research has largely focused on technology acceptance and usage intention, with less attention to whether and how GenAI use can be sustained. This study investigates the determinants and dimensions of sustainable GenAI use among university students through a mixed-methods approach, combining an online survey of 1,084 Chinese university students with semi-structured interviews. An extended Unified Theory of Acceptance and Use of Technology (UTAUT) model was employed to examine the quantitative relationships. The results indicated that performance expectancy (
β
= 0.195,
p
< 0.001), effort expectancy (
β
= 0.094,
p
= 0.030), social influence (
β
= 0.147,
p
< 0.001), perceived enjoyment (
β
= 0.224,
p
< 0.001), and perceived creativity support (
β
= 0.211,
p
< 0.001) positively affected behavioral intention, whereas perceived risk had a negative effect (
β
= −0.044,
p
= 0.019). Facilitating conditions (
β
= 0.355,
p
< 0.001) and behavioral intention (
β
= 0.531,
p
< 0.001) were positively associated with actual usage behavior. Additionally, gender, educational level, usage experience, and disciplinary background exerted significant moderating effects. Qualitative findings identified three interconnected dimensions of sustainable GenAI use: long-term continuance, balanced and moderate use, and responsible and ethical use. By extending UTAUT beyond technology acceptance toward a multidimensional conceptualization of sustainable use, this study showed that sustainable GenAI use depended not only on technology acceptance but also on students’ ability to engage with GenAI critically, responsibly, and autonomously. The findings provide practical insights for fostering AI literacy and the appropriate integration of GenAI in higher education.
Unknown authors· Frontiers in Education· 0 citations
Generative artificial intelligence (GenAI) has entered classrooms faster than most institutions have been able to formulate policy, yet its instructional value ultimately depends on whether teachers choose to use it. This study examined the determinants of teachers' behavioral intention (BI) to use GenAI in teaching, drawing on an integrated Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Five predictors were specified: perceived ease of use (PEoU), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), and anxiety (ANX). A cross-sectional survey was administered to 500 in-service teachers drawn from three Chinese educational institutions spanning medical higher education, vocational higher education, and primary education. Each construct was operationalized as a composite mean of its constituent items, and the model was estimated using the Regression module of SmartPLS 4 with bootstrapping (5,000 subsamples) to obtain confidence intervals. Collinearity diagnostics were acceptable (VIF = 1.046-1.238). The model accounted for 32.7% of the variance in behavioral intention (adjusted R² = .320). Perceived usefulness was the strongest predictor (β = .228, p < .001), followed by facilitating conditions (β = .206, p < .001), perceived ease of use (β = .198, p < .001), and social influence (β = .195, p < .001). Contrary to expectations, anxiety exerted no significant effect (β = .018, p = .631), with a bootstrap confidence interval that spanned zero. The findings indicate that teachers' adoption decisions are jointly driven by instrumental value and institutional provisioning, and that generalized technology anxiety is not, in itself, a barrier among teachers who already have practical exposure to GenAI. Implications for professional development design and institutional AI policy are discussed.
Generative AI presents significant potential for improving students' writing, and its acceptance is influenced by varied factors. The present study aims at exploring the factors by examining six vital constructs of Technology Acceptance Model (TAM) 3 (Venkatesh & Bala, 2008)—academic relevance, output quality, self-efficacy, playfulness, anxiety, and perceived enjoyment—and their impact on perceived usefulness, perceived ease of use, and behavioral intention of students. Using a quantitative research approach, a sample size of 345 university students from Computer Science, Management Science, and Arts and Humanities voluntarily participated in an online cross-sectional survey questionnaire. The data was analyzed by SPSS 23.0 and SmartPLS 4.0 for exploratory and confirmatory factor analysis. Academic relevance (β = .378***) and perceived ease of use (β = .456***) were identified as significant predictors of students' perceived usefulness of (Gen) AI in their writing as compared to the output quality of (Gen) AI, which was found to be an insignificant factor of perceived usefulness. The study also reveals that playfulness (β = 0.332**) and perceived enjoyment (β = 0.449***) were the primary factors influencing students' perceived ease of use, unlike anxiety and self-efficacy, which showed statistically insignificant effects. The findings of the study further suggest that perceived usefulness (β = 0.606***) and ease of use (β = 0.227*) are key enablers of the behavior of students' intention to adopt (Gen) AI in the future.
Unknown authors· Khazar Journal of Humanities...· 0 citations
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