Understanding the usefulness–risk paradox in AI writing tool adoption: An extended technology acceptance model study in Chinese University L2 writing contexts
Aug 2026· PLoS ONE· Vol 21, pp. e0355494· 0 citations· 93 references
Medicine
TL;DR
It is shown that functional value and risk awareness may coexist rather than offset one another in L2 writing contexts, extending TAM in AI-mediated learning by showing that functional value and risk awareness may coexist rather than offset one another in L2 writing contexts.
Abstract
Generative artificial intelligence (GenAI) is rapidly reshaping higher education, and AI writing tools have become increasingly prominent in second language (L2) writing contexts. However, the mechanisms underlying students’ adoption of these tools, as well as the perceived consequences of their use, remain insufficiently understood. Grounded in an extended Technology Acceptance Model (TAM), this study examines how AI self-efficacy (ASE), multidimensional perceived risk, and behavioral engagement (BE) are associated with Chinese university students’ adoption of AI writing tools in L2 writing-related tasks and their perceived writing-related outcomes. A total of 518 valid questionnaires were collected from undergraduate students with prior experience using AI writing tools for English writing, revision, translation, or other L2 writing-related tasks, and structural equation modeling was employed to test the proposed acceptance–use–outcome framework. The results show that ASE was positively associated with perceived ease of use (PEOU) and perceived usefulness (PU). While PEOU was negatively associated with overall perceived risk (OPR), PU was positively associated with OPR, indicating that students who perceived AI writing tools as useful also tended to report stronger awareness of potential risks. OPR was not significantly associated with attitudes toward AI writing tools. Attitude was strongly associated with behavioral engagement, which in turn was positively associated with both perceived positive and perceived negative writing-related outcomes, with the positive association being substantially stronger. These findings extend TAM in AI-mediated learning by showing that functional value and risk awareness may coexist rather than offset one another in L2 writing contexts. Because the study used cross-sectional self-report data, the findings should be interpreted as associations among perceived constructs rather than evidence of direct causal effects or objective improvement in writing performance. The study also offers practical implications for the responsible integration of AI writing tools in higher education.
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 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
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
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.
The findings indicate that critical thinking is predictively associated with students’ self-reported behavioral intention to use AI chatbots and shows a larger standardized association than traditional TAM factors, including perceived usefulness, perceived ease of use, and attitude.
My-Duyen Thi Nguyen, Diep-Ngoc Le· International Journal of Inf...· 0 citations
This study investigates students’ acceptance of AI-assisted translation tools by proposing an extended Technology Acceptance Model (TAM-AI) for AIassisted translation learning contexts. Unlike prior additive approaches, the proposed model explains how translation-specific perceptions influence behavioral intention (BI) through underlying cognitive mechanisms. A survey was conducted among undergraduate translation students and analyzed using Structural Equation Modeling (SEM). The results indicate that perceived usefulness (PU) and perceived ease of use (PEOU) significantly predict BI. In addition, translation-specific factors influence technology acceptance indirectly: perceived translation quality operates through trust, feedback clarity through cognitive understanding, and cognitive load reduction through effort reduction. The TAM-AI model demonstrates greater explanatory power than the baseline TAM. These findings provide a deeper understanding of technology acceptance in AI-assisted learning environments and offer practical implications for the design of AI-assisted translation tools. Future studies with larger and more diverse samples are encouraged to further validate the proposed model.
Unknown authors· Computer Science and Informa...· 0 citations
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