Theoretically, the current research offers a new lens through which accelerated technology adoption can be analysed and guides tailored digital sustainability interventions in Confucian models of education.
Abstract
Artificial Intelligence (AI) assisted writing tools, such as ChatGPT have arisen rapidly and created a socio-technical phenomenon of fast technology adoption in higher education. Traditional models focus on early adoption but understanding the behavioral pathway that accounts for intensive AI overuse is critical. To summarize, this study enriched the Technology Acceptance Model (TAM) by developing a sequential mediation model: Perceptions (PU/PEOU) → Satisfaction → Habit → AI Overuse and investigated boundary conditions of this process regarding Confucian Cultural Values as moderator. By conducting the survey mixed-mode, from December 2024 to February 2025 data has been collected using a systematic random sampling of Chinese educators at six universities in Chongqing through both individual intercepting and online distribution. Direct, mediation and moderation effects were tested based on Partial Least Squares Structural Equation Modeling (PLS-SEM). Results confirm partial mediation: utility and ease perceptions drive user satisfaction, to the extent that it spurs behavioral automaticity (habit) behind high-intensity use the study characterizes AI Overuse as a relatively high force and goal-oriented behavioral outcome of technology acceleration, separating this from clinical addiction. As for the ability to moderate, Confucian Values firmly bolster the impact of Perceived Usefulness upon Satisfaction according to the power placed on collective welfare. Interestingly, these cultural effects are moderated when the variables Perceived Ease of Use (a cognitive barrier) and Habit (a psychological boundary) are applied. Theoretically, the current research offers a new lens through which accelerated technology adoption can be analysed and guides tailored digital sustainability interventions in Confucian models of education.
Purpose Generative artificial intelligence (AI) is increasingly used in higher education, yet students’ continued use of AI educational tools may operate differently in resource-constrained university contexts. This study examines college students’ continuance intention to use AI educational tools in local undergraduate universities across five northwestern Chinese provinces and develops a capability-centric extension of the unified theory of acceptance and use of technology (UTAUT). Methods A sequential explanatory mixed-methods design was adopted. After pilot-based construct purification, 703 valid survey responses from students with prior AI tool experience were analyzed using partial least squares structural equation modeling (PLS-SEM). Additional analyses included common method bias diagnostics, PLSpredict, cross-validated predictive ability testing, multi-group analysis, and post-hoc power analysis. Semi-structured interviews with 25 students were used to contextualize the quantitative findings. Results Self-efficacy was the strongest predictor of continuance intention, followed by performance expectancy, perceived policy signals, and social influence. Algorithmic trust showed only a weak positive association, whereas effort expectancy showed a weak but significant negative association, contrary to the original UTAUT prediction. Qualitative findings indicated that students perceived AI tools as easy to access but cognitively demanding to use effectively, due to prompt refinement, factual verification, error correction, and task adaptation. Conclusion Continued AI educational tool use in resource-constrained universities depends less on surface-level ease of use and more on students’ perceived capability to manage the full AI-assisted learning workflow. This study extends UTAUT-based AI adoption research by foregrounding self-efficacy and proposing a capability-centric perspective on generative AI continuance in higher education.
Yao Wan, Kuanmin Lu· Frontiers in Psychology· 0 citations
As AI tools are increasingly used in higher education, understanding the factors affecting students’ adoption intentions has become theoretically and practically important. Previous studies have mainly relied on traditional technology acceptance constructs, while comparatively little attention has been given to pedagogical and competence-based conditions shaping students’ cognitive evaluations of AI systems. To address this gap, the current study builds on the technology acceptance model (TAM) by proposing authentic learning (AL) and prompt engineering competence (PEC) as precursors of perceived usefulness (PU), perceived ease of use (PEU), and behavioral intention (BI) to use AI tools. The study was based on data collected from 309 undergraduate students at the University of Ha’il. A two-step structural equation modeling (SEM) approach was employed using AMOS software. Confirmatory factor analysis confirmed construct reliability, convergent validity, and discriminant validity. SEM was then conducted to test the hypotheses. The findings show that AL significantly predicts both PU and PEU, whereas PEC significantly predicts PEU but not PU. Both PU and PEU were found to be important predictors of BI. Bootstrapping results reveal that AL affects BI through PU and PEU, while PEC affects BI entirely through PEU. The results also confirm considerable explanatory power, with an R² of .71 for BI. These findings extend TAM by reconceptualizing AL as a foundational pedagogical precursor influencing AI adoption and by clarifying the unique role of PEC in improving PEU. Integrating pedagogical and competence-based determinants into AI-enabled higher education advances technology acceptance theory and explains the AI adoption mechanism more precisely. The findings provide practical guidance for educators and instructional designers by emphasizing the importance of integrating authentic learning tasks and developing students’ prompt engineering skills to enhance meaningful AI-supported learning.
S. Alshammari, Mohammed Habib Alshammari· Electronic Journal of e-Lear...· 0 citations
This study examines the determinants influencing lecturers’ intention to use and adopt artificial intelligence (AI) in higher education. As AI technologies become increasingly integrated into teaching and learning, understanding lecturers’ perceptions is critical for successful adoption. Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, this study investigates the roles of AI self-efficacy, perceived usefulness, relative advantage, and institutional support.A quantitative research design was employed using a systematic random sampling approach. Data were collected from 371 lecturers in public universities in Jordan through a structured questionnaire. All constructs were measured using validated items on a five-point Likert scale, and the data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM).The results show that AI self-efficacy, perceived usefulness, and relative advantage have significant positive effects on lecturers’ intention to use AI. Furthermore, institutional support plays a pivotal moderating role, strengthening the relationship between intention to use AI and actual adoption behaviour. The findings also confirm that intention is a significant predictor of AI adoption behaviour.Theoretically, this study extends the UTAUT model by incorporating AI-specific determinants and emphasising the moderating role of institutional support, providing deeper insights into technology adoption in educational contexts. Practically, the findings highlight the importance of institutional support in developing lecturers’ AI-related skills and competencies. Targeted training programs and supportive environments can facilitate the effective and sustained use of AI tools in higher education.
A.M. Al-Darabseh, S. F. Padlee, Siti Nur et al.· Journal of Intelligent Decis...· 0 citations
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 substantial potential to support teaching, learning, and professional development, yet evidence remains limited on how vocational teachers form adoption intentions and whether those intentions translate into broader digital competency. 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. It also tested Behavioral Intention as an antecedent of Digital Competency.
A quantitative cross-sectional survey design was employed. Data were collected from 513 private vocational teachers across Thailand using a structured questionnaire. The proposed model included AI Literacy, Performance Expectancy, Effort Expectancy, Trust, Facilitating Conditions, Perceived AI Risk, Behavioral Intention, and Digital Competency. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both the measurement model and structural relationships among constructs.
The results demonstrated that the proposed model explained 80.7% of the variance in Behavioral Intention (R² = 0.807), indicating substantial predictive power. Trust (β = 0.295, p < .001) and Effort Expectancy (β = 0.197, p = .001) were found to significantly and positively influence Behavioral Intention to use GenAI. Perceived AI Risk also exhibited a significant positive effect on Behavioral Intention (β = 0.339, p < .001), contrary to the hypothesized negative relationship. In contrast, AI Literacy, Performance Expectancy, and Facilitating Conditions did not significantly predict Behavioral Intention. Furthermore, Behavioral Intention did not significantly influence Digital Competency (β = −0.134, p = .243), suggesting that technology acceptance alone is insufficient to enhance teachers’ digital competency.
The findings contribute to technology acceptance research by integrating AI-specific constructs within an extended UTAUT framework and providing evidence from the underexplored context of vocational education. The study highlights the critical roles of trust, ease of use, and responsible AI awareness in promoting GenAI adoption. It also emphasizes that digital competency development requires structured professional learning, practical engagement, and continuous competency-building initiatives beyond mere technology acceptance. These findings offer important implications for educational institutions, policymakers, and teacher development programs seeking to foster effective and sustainable AI integration in vocational education.
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.
D. Chen, Han-Piao Lin, Tzu-Hsin Chu· Frontiers in Psychology· 1 citation