Factors Influencing the Acceptance and Application of Artificial Intelligence Technologies among University Lecturers in Thailand

Abstract

Aim/Purpose: The primary objective of this study was to examine the factors that influence the acceptance and practical application of Artificial Intelligence (AI) technologies among university lecturers in Thailand. By adopting the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as the analytical framework, the study sought to identify how individual perceptions, motivational factors, social influences, and institutional conditions jointly shape lecturers’ behavioral intention to use AI and their actual usage behavior. The findings are expected to provide empirical insights that support strategic planning for sustainable AI integration in higher education.

Introduction/Background: Artificial Intelligence has increasingly emerged as a transformative force in higher education, reshaping instructional practices, research processes, and academic administration. AI-driven applications such as adaptive learning systems, intelligent tutoring, automated assessment, and learning analytics have expanded the potential for personalized and data-informed education. In Thailand, national development agendas, particularly Thailand 4.0 and the National Artificial Intelligence Strategy (2022–2027), have emphasized the importance of AI adoption to enhance workforce competencies and educational quality. Within this policy context, university lecturers play a critical role as key agents who translate technological initiatives into classroom practice and institutional outcomes. However, the successful integration of AI in higher education depends not solely on technological availability but primarily on lecturers’ acceptance, readiness, and the support structures provided by institutions. Understanding the psychological and organizational factors that influence lecturers’ decisions to adopt AI is therefore essential for effective policy implementation and institutional management.

Methodology: In this study, a quantitative research approach was employed using a cross-sectional survey design. The target population consisted of full-time lecturers employed at public and private universities across Thailand. A total of 400 respondents were selected through stratified random sampling to ensure representation across institution types and academic disciplines. Data were collected using a structured online questionnaire developed from established UTAUT2 measurement scales and adapted to the context of AI usage in higher education. The instrument underwent expert validation and reliability testing prior to data collection. Descriptive statistics were used to summarize respondent characteristics and variable levels. In contrast, multiple regression analysis was used to examine the relationships between the independent variables—Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Pprice Value, and Habit—and the dependent variables—Behavioral Intention and Use Behavior.

Findings: The results indicated that university lecturers in Thailand generally reported high levels of intention to use AI technologies and moderate to high levels of actual usage. Regression analysis revealed that Performance Expectancy, Hedonic Motivation, Social Influence, and Habit had a statistically significant positive influence on Behavioral Intention to adopt AI. Together, these factors explained 61.2% of the variance in behavioral intention. In contrast, Effort Expectancy and Price Value did not demonstrate a significant effect on intention in this context. Regarding actual use behavior, Behavioral Intention emerged as the strongest predictor, followed by Facilitating Conditions and Habit. These variables collectively explained 48.7% of the variance in AI use behavior. The findings suggest that lecturers are more likely to adopt AI when they perceive clear professional benefits, derive enjoyment from using AI tools, experience supportive social norms, and have prior experience with digital technologies. At the same time, institutional support plays a crucial role in enabling sustained application.

Contribution/Impact on Society: This study contributes to the academic literature by empirically validating the UTAUT2 model within the context of Thai higher education and AI adoption. It highlights the importance of both utilitarian and intrinsic motivational factors, demonstrating that lecturers value enjoyment and perceived usefulness more strongly than ease of use. The findings have practical implications for policymakers and university administrators by emphasizing that AI adoption strategies should move beyond basic technology provision to foster engaging, supportive, and habit-forming environments that encourage long-term use.

Recommendations: Higher education institutions should prioritize communication strategies that clearly demonstrate the practical benefits of AI for teaching, research, and administrative tasks in order to enhance performance expectancy. In addition, universities should design engaging professional development activities—such as hands-on workshops, innovation labs, and collaborative experimentation—to stimulate hedonic motivation. Adequate technical infrastructure, training opportunities, and ongoing support services should also be ensured to strengthen facilitating conditions and enable lecturers to translate intention into actual use.

Research Limitations: This study was subject to several limitations. The cross-sectional design restricted the ability to capture changes in lecturers’ attitudes and behaviors over time. Furthermore, reliance on an online survey may have limited participation by lecturers with lower levels of digital proficiency, potentially affecting the generalizability of the findings.

Future Research: Future studies should adopt longitudinal research designs to examine the evolution of AI adoption behavior over time. Incorporating qualitative methods, such as interviews or focus groups, would also provide deeper insights into lecturers’ concerns, expectations, and contextual challenges related to AI usage. Comparative studies across academic disciplines or institutional types are recommended to refine the understanding of AI adoption dynamics in higher education.