Aug 2026· World Journal of Educational Studies· Vol 4, pp. 34-50· 0 citations· 22 references
TL;DR
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.
Abstract
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.
Artificial Intelligence (AI) has emerged as a transformative technology with significant implications for teaching, learning, and academic administration. As AI-enabled tools become increasingly integrated into educational environments, understanding the factors that shape users’ intention to adopt these technologies has become imperative, particularly in developing countries where empirical evidence remains limited. Grounded in the Technology Acceptance Model (TAM), this study investigates the behavioral intentions toward AI adoption among students and educators in Nepalese secondary schools and higher education institutions. The proposed model extends TAM by incorporating trust and perceived risk as additional determinants of adoption intention. A quantitative research design was employed using a structured questionnaire administered to 321 respondents comprising secondary-level students, university students, school teachers, and faculty members from public and private educational institutions in Nepal. Data were analyzed using descriptive statistics and Structural Equation Modeling (SEM) to examine the relationships among perceived usefulness, perceived ease of use, trust, perceived risk, and behavioral intention toward AI adoption. The findings reveal that perceived usefulness, perceived ease of use, and trust exert significant positive effects on behavioral intention, whereas perceived risk negatively influences AI adoption intentions. The results indicate growing acceptance of AI technologies among educational stakeholders, although concerns regarding privacy, ethical issues, academic integrity, and overdependence on AI remain substantial challenges. This study extends the applicability of TAM in the educational context of a developing economy. It offers valuable implications for policymakers, educational institutions, and technology developers to foster digital literacy, promote responsible AI use, and establish supportive regulatory frameworks for sustainable AI integration in education.
S. Adhikari· Nepalese Journal of Manageme...· 0 citations
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.
Qianghong Huang, Junping Xu, Ru Zhang et al.· Frontiers in Psychology· 0 citations
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
The results show that AI self-efficacy, perceived usefulness, and relative advantage have significant positive effects on lecturers’ intention to use AI, and institutional support plays a pivotal moderating role, strengthening the relationship between intention to use AI and actual adoption behaviour.
A.M. Al-Darabseh, S. F. Padlee, Siti Nur et al.· Journal of Intelligent Decis...· 0 citations
Purpose
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This study integrates the Theory of Planned Behavior and the Technology Acceptance Model to examine factors influencing physical education (PE) teachers’ intention to use digital technology in teaching.
Method
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Survey data were collected from 734 in-service primary and secondary school PE teachers in China and analyzed using structural equation modeling.
Results
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Subjective norms, behavioral attitudes, and perceived usefulness significantly predicted PE teachers’ intention to use digital technology, whereas perceived behavioral control was not a significant direct predictor of intention. Perceived ease of use did not directly predict intention, but it influenced it indirectly through perceived usefulness and behavioral attitudes.
Conclusion
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These findings partially support the integrated Theory of Planned Behavior and Technology Acceptance Model model in PE, highlighting social, individual, and technological influences and offering implications for policy, leadership, teacher education, and technology development.
Hongqin Chai, Bo-Wei Zhang, Rui Xue et al.· Journal of teaching in physi...· 0 citations
This study examined factors shaping school teachers’ intention to adopt Digital Educational Resources (DER) in Kathmandu Valley, Nepal, based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Factors included were performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention. Fifty-six (56) teachers completed an online survey measuring those factors. Among all the factors, facilitating conditions were the marginally significant positive predictor of behavioural intention to adopt DER. Male teachers reported marginally higher behavioural intention (p =.05) to adopt DER compared to female teachers. However, gender was not a significant moderator in relationship between behavioural intention and its predictors. These findings highlight the role of infrastructural support in encouraging teachers to adopt DER and suggest that gender differences in intention may benefit from further exploration to help guide more inclusive approaches.
Bijaya Shrestha, Virginia Clinton-Lisell· Journal of Learning for Deve...· 0 citations
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