Building a Model of AI Adoption among University Students in the Eastern Region of Thailand

Building a Model of AI Adoption among University Students in the Eastern Region of Thailand | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Building a Model of AI Adoption among University Students in the Eastern Region of Thailand Siwaporn Kunnapapdeelert, Passarin Phalitnonkiat, Tuangporn Pinudom, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8590201/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Based on the remarkable use of Artificial Intelligence (AI) among university students in their educational settings, this study investigated student AI adoption by focusing on students in the eastern region of Thailand. The methodology utilized a quantitative approach and Structural Equation Modeling (SEM) on data obtained from 435 survey respondents. The study examined both psychological and behavioral variables affecting AI usage by employing the Unified Theory of Acceptance and Use of Technology (UTAUT). The results of the model suggested three AI-specific psychological factors (intention, satisfaction, and anxiety) that significantly predict AI user behavior. AI user intention and AI user satisfaction have significant impacts on AI user behavior. Additionally, this paper highlights the need for collaborative effort between AI developers, educators, and policy makers to further develop an AI ecosystem that is both technologically sound and culturally aligned with educational goals and societal values. This research contributes to the literature addressing AI in educational environments by offering an empirical study and by giving a refined theoretical model to understand AI adoption among university students. Business and commerce/Business and management Social science/Business and management Social science/Education Business and commerce/Information systems and information technology Biological sciences/Psychology Social science/Psychology Social science/Science technology and society AI Adoption Artificial Intelligence (AI) Higher Education Structural Equation Modeling (SEM) Technology Acceptance Figures Figure 1 Introduction Recently, Artificial Intelligence (AI) has rapidly developed from theoretical concept to transformative technology, which has impacted several sectors including healthcare, finance, and transportation. Within education, AI tools are being used to enhance personal learning, automate personal tasks, and promote interactive environments. Consequently, teaching practice and student experiences are being reshaped. Despite AI being widely used, there remains some critical gaps in the study of AI adoption within an education setting. Psychological and behavioral factors influencing students’ engagement with such technologies have not been addressed by the existing literature. Several researchers have explored the technical aspects and broad societal impacts of AI. However, the empirical insight into the specific study of AI adoption among university students, especially in culturally diverse contexts such as Thailand, is limited. Most of the existing research emphasizes the key opportunities and challenges in AI’s integration and AI’s potential to enhance learning experiences and support academic workflows (Ng et al., 2025 ; Barus., 2025). The Unified Theory of Acceptance and Use of Technology (UTAUT) has been applied to investigate technology adoption. Additionally, some studies extended the models to incorporate AI-specific constructs. The practical approaches for adapting AI technologies to meet the needs of non-Western educational contexts have still not been fully investigated. This study addresses these limitations by investigating the interplay of AI user intention, satisfaction, and anxiety in driving AI adoption behaviors among university students in the eastern part of Thailand. Structural Equation Modeling (SEM) is employed to analyze data from 435 students providing three contributions: 1) Theoretical: The study integrates AI-specific psychological constructs into technology acceptance models, which enhance their applicability to modern educational contexts. 2) Practical: The study provides actionable recommendations for AI developers to improve user-centric designs. 3) Contextual: The study clarifies AI-adoption by students in the eastern region of Thailand. This study includes both academic discussion and practical strategies for increasing AI’s transformation potential in Thai higher education. The findings highlight the need for collaborative efforts between developers, educators, and policy makers to develop AI systems that are not only technologically robust, but also culturally aligned. Literature Review Recently, Artificial Intelligence (AI) has dramatically developed from theoretical concept to practical technology very quickly. The use of AI significantly impacts modern life. It influences numerous sectors including healthcare, manufacturing, finance, energy, transportation, and education by incorporating technology into our daily life activities. The application of AI for education has been adopted to enhance educational experiences. Numerous researchers have studied the use of generative AI tools for various educational purposes such as curriculum integration, personalized learning, research, academic support,, plagiarism detection, academic integrity, ethical education governance, and administrative automation. Generative AI tools have been applied for creating interactive learning tools, quizzes, simulations, and educational games. AI has been utilized to create flexible content that is customized for individual learning preferences. Further, AI can be used to search journals, review literature, analyze data, write code, generate new ideas, and learn new topics. It promotes flipped classrooms and adaptive learning systems to boost student engagement and critical thinking. Educators, researchers, and publishers employ AI-driven tools for detecting both AI-generated content and plagiarism based on pattern analysis and similarity checks. Moreover, some administrative tasks and resource distributions are easily completed by AI. Various research studies have been conducted to find insight into the use of Generative AI in the education sector. Acquah et al. ( 2024 ) determined factors that influence preservice educators’ intentions to include AI into their lesson planning. The combination of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was applied to investigate the impact of several factors on behavioral intention. Key predictors of preservice teachers’ intentions to use AI in lesson planning are social influence, habit, performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation. The results explained that educational institutions can better prepare preservice teachers by addressing the identified factors. Ng et al. ( 2025 ) explored the application of Generative Artificial Intelligence (GenAI) to education. The study focused on potential opportunities, challenges, and strategies for promoting GenAI adoption. Several opportunities for the use of GenAI for educators include teaching and learning, administration, and student assessment. Numerous institutions might face some challenges when attempting to integrate GenAI such as institutional readiness, educator AI competencies, and students’ AI ethics. The study suggested that training programs for educators should be provided to enhance educators’ AI competencies. Clear guidelines of policies and frameworks related to the ethical and effective use of AI in schools should be provided. Lastly, AI software and technical support for both teachers and students should be available. Amin et al. ( 2025 ) examined the transformative potential of AI in education, especially within the Open and Distance Learning (ODL) environment. This research is well aligned with the previous research in that AI can tailor educational experiences to each learner's needs. AI enhances both learner engagement and learners’ outcomes. AI automates administration tasks (e.g. grading and feedback) which can promote institutions to efficiently manage larger numbers of students. An additional opportunity is that AI technologies (e.g. chatbots and speech-to-technologies) can assist diverse learners, including those with disabilities and language barriers. Barus et al. ( 2025 ) studied the governance of GenAI in higher education. The study focused on student perceptions. The results reported that undergraduate students most commonly use GenAI for generating ideas for assignments, follow by learning new topics, writing essays or papers, writing programs or query code, and translating language. The influence of GenAI tools and the need for structural frameworks that address ethical and operational challenges in higher education were studied. Based on the insight of undergraduate students, the key factors that were identified included the potential of AI to enhance learning experiences, academic integrity, and data privacy. The authors recommended that higher education institutions should 1) establish an AI ethics committee that includes people from various faculties, 2) include AI competency training modules in the school’s curriculum, and 3) regularly hold open discussions on AI ethics for students and faculty. Almogren et al. ( 2024 ) investigated the influencing factors of ChatGPT acceptance in higher education. The study explored how perceived usefulness (PU), ease of use (EU), social influence (SI), and facilitating conditions (FC) affect students’ and educators’ willingness to use ChatGPT. This study employed structural equation modeling (SEM), and the results revealed that PU, EU, SI, and FC play important roles for ChatGPT adoption. Saha et al. ( 2025 ) studied how the use of AI, including students’ intentions, could drive learning success in Bangladesh, an emerging economy. The unified theory of acceptance and use of technology (UTAUT) model was examined for predicting educational AI users’ acceptance. The relationships between performance expectancy (PE), effort expectancy (EE), social influence (SI), subjective norms (SN), facilitation condition (FC), Attitude (ATT), intent to use (IU), and actual use (AU) of AI tools in education were studied. Structural equation modeling (SEM) was then applied to analyze these relationships. The study found PE, EE, SI, FC, and SN have a positive impact on AU, but mediated by IU. The authors recommended that AI adoption in education could be enhanced by encouraging developers to incorporate features such as local language interaction, adaptive learning, and gamification methods. From a managerial standpoint, educational institutions could invest in reliable IT infrastructure, high-speed internet, and technical support for the types of AI learning tools that students use. Such investments would help promote the adoption of educational AI technologies. Despite the number of studies that explore AI technology for education found in the literature, some limitations still exist. Specifically, there is limited research related to undergraduate students’ AI perception and the effect of their perceptions on usage behavior. The existing studies mostly emphasize the technical aspects of AI tools or their broad social impact. Research related to the specific factors that affect students’ interactions with AI tools has not been addressed. Although some researchers have explored variables affecting technology adoption, the relationships among such variables, especially in the context of AI usage for higher education students, remain unexplored. Additionally, there is a lack of actionable insight for AI developers in the academic sector to tailor AI technologies that align with the needs and behaviors of university students. With such alignment, educational outcomes might be improved, which could positively impact the overall institutional system. By addressing these gaps, this study aims to provide more insight into AI adoption within higher education settings. Such insight could contribute to academic literature as well as provide practical applications for AI driven education. Research Objectives To investigate student factors regarding AI technology that influence AI user behavior. To explore the relationships among different variables affecting AI user behavior.. To suggest how AI technology developers or related institutions could enhance AI technology performance so that it aligns with university students’ usage. Methodology The motivation for this study was to explore factors influencing the AI user behavior of university students in the eastern region of Thailand. Research Design and Questionnaire Development This study employed a quantitative research methodology with a descriptive design to explore students’ perceptions in using AI technology. The study included 25 items, and individual items were carefully rephrased for the context of student AI user experiences. Students were asked to complete a survey consisting of four parts: personal information, AI user behavior, perceptions toward using AI technology, and suggestions. Part three (students’ perceptions toward using AI technology) used a 5-point Likert scale to assess all items based on the significance level of each item when deciding to use AI technology. The Likert scale ranged from 1 to 5 where 1 = “Totally Disagree” and 5 = “Totally Agree”. All questions and items in the survey were translated in English and Thai, keeping the same meanings in both versions. Research Setting and Data Collection Data collection for this study was self-reported using a Google Forms online survey. Due to accessibility and ease of data collection, convenience sampling was used to select online survey participants. The reasons for using this tool were based on the students’ familiarity with Google Forms and the ease of accessibility using various devices such as smart phones, tablets, or laptops. All respondents were informed of the purpose of the survey, which was clearly stated in the survey introduction page. Students saw this page before starting the survey. Time required to complete the survey was estimated to be 20–30 minutes. The investigation was carefully designed to ensure samples were selected from university students in the eastern region of Thailand. Questionnaires were distributed to 500 students, and 435 complete and valid responses were returned. Of the 435 samples, 23% were males, 65.3% were females, and 11.7% did not specify their gender. Moreover, 1.8% were younger than 18 years old, 86.9% were 18–22, 10.6% were 23–27, and 0.7% were older than 27 years old. Table 1 presents the respondents’ behavior when using AI technology. Table 1 The respondents’ AI user behavior. Variable N = 435 Percent How long using AI technology per day Shorter than 1 hour 2 0.4 1–2 hours 13 3.1 2–4 hours 61 14.0 Longer than 4 hours 359 82.5 The most frequent activity on social media Communicating with others 77 17.7 Posting message/photo/VDO 20 4.6 Updating news/celebrity/pages 20 4.6 Shopping online 9 2.1 Selling a product/service or running a business 11 2.5 Entertaining yourself 272 62.5 Finding new friends 8 1.8 Searching for information 16 3.7 Other 2 0.5 The main benefit of using AI technology for your studies Increasing the level of study interest 73 16.8 Reducing time doing repetitive work 178 40.9 Designing class reports/assignment 165 37.9 Guiding the prospect examination 9 2.1 Other 10 2.3 What activities have you done by using AI technology? (You can answer more than 1) Suggesting how your work should be done 193 44.4 Finding the desired information for your work 309 71.0 Checking the information for your work 214 49.2 Answering questions in your work 221 50.8 Solving basic problems for your work 224 51.5 Other 5 1.1 Measurements After collecting data from the respondents, the students’ perceptions were coded. This study applied descriptive statistical analyses which summarize and organized the respondents’ personal information. Moreover, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) were applied to test the model’s hypotheses. Cronbach’s alpha was performed as a pretest of proposed variables that were based on respondent data. The values for 25 items were 0.908, and this value exceeded the threshold of 0.70 that is considered acceptable (Hair et al., 2006 ). Results Frequency analysis was conducted on the data to obtain the respondents’ personal information and their behaviors regarding the use of AI technology. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were also applied in this study. Two items were removed from the original list of 25 items, and the 23 remaining items were assessed and grouped into four constructs: AI user intentions, AI user satisfaction, AI user anxiety, and AI user behavior, as shown in Table 2 . Factor extraction was then performed on the findings in Table 2 . Data inspection techniques for EFA were classified as Kaiser-Meyer-Olkin (KMO) (Dziuban & Shirkey, 1974 ; Kaiser, 1970) and Bartlett’s test of sphericity (Bartlett, 1950 ; Dziuban & Shirkey, 1974 ). KMO values illustrate sample adequacy, and correlation values of more than 0.7 are considered adequate for analyzing the EFA. For this study KMO and Bartlett’s test of sphericity were conducted on the data. The KMO value was 0.895 while the Bartlett test value was p < 0.001. These results are evidence of the sample’s appropriateness (Hutcheson & Sofroniou, 1999 ). Exploratory Factor Analysis and Confirmatory Factor Analysis This paper used EFA and CFA to obtain, merge, and confirm variables. Table 2 presents the rotated component matrix and the four extracted constructs along with the mean, standard deviation, factor loading, and construct reliability values for the 23 remaining items. All factor-loading values were relatively high at over 0.5. These values are shown in Table 3 . Table 2 EFA and CFA results and descriptive statistics. Construct (AVE) Code Mean S.D. Factor Loading CR AI user intention Cog2 4.29 0.660 0.607 0.913 Cog3 0.784 Cog4 0.762 Cog5 0.749 Cog6 0.738 Cog7 0.768 Cog8 0.740 Cog9 0.536 AI user satisfaction Aff1 4.36 0.642 0.631 0.890 Aff2 0.780 Aff3 0.831 Aff4 0.755 AI user anxiety Aff5 3.65 0.918 0.774 0.719 Aff7 0.684 Aff8 0.524 AI user behavior Beh1 4.11 0.661 0.517 0.905 Beh2 0.595 Beh3 0.642 Beh4 0.779 Beh5 0.773 Beh6 0.791 Beh7 0.801 Beh8 0.760 Note: AVE denotes average variance extracted; CR denotes construct reliability, and S.D. denotes standard deviation. The variables were extracted into four dimensions, and the factor matrix loadings in each construct are considered acceptable as shown in Table 3 and Table 4 . Table 3 Factor loading acceptance levels with required sample sizes. Minimum Acceptable Factor Loading Sample Size Needed for Significance 0.30 350 0.40 200 0.55 100 0.70 60 0.75 50 Source: Hair et al. (2010) Confirmatory Factor Analysis (CFA) was conducted in AMOS. The results show that the assessment of university students’ AI user behavior confirmed their behavior to use AI technology as revealed by the model of fit summary in Table 4 . Table 4 Structural model fit indices. Measurement Model Findings Cut-Off Value Conclusion References CMIN/DF 3.545 ≤ 5.00 Good Bollen ( 1989 ) RMSEA 0.076 ≤ 0.08 Good Kline ( 2005 ) CFI 0.914 ≥ 0.90 Good Hu & Bentler ( 1999 ) TLI 0.896 ≥ 0.80 Good Bentler ( 1990 ) NFI 0.884 0–1 Good Hu & Bentler ( 1999 ) The results of the CFA suggest some variables should be combined, while some variables should be removed. Table 5 presents the correlation matrix of the observed variables from the model, which was tested using a Pearson correlation measuring the relationships among each construct. Table 5 The observed variables’ correlation coefficients. Variables AI User Intention AI User Satisfaction AI User Intention 1 0.582** AI User Satisfaction 0.582** 1 AI User Anxiety 0.135* 0.252** Note: **Correlation is significant at a level of 0.01 (two-tailed). *Correlation is significant at a level of 0.05 (two-tailed). Hypotheses Testing For this paper, Structural Equation Modeling (SEM) was used to test the relationships between the observed variables. Regarding the findings, the modeling revealed four constructs: AI user intention, AI user satisfaction, AI user anxiety, and AI user behavior. From these four observed constructs, three hypotheses were created to explore how each factor affected AI user behavior: Hypothesis 1 AI user intention will positively influence AI user behavior for university students in the eastern region of Thailand. Hypothesis 2 AI user satisfaction will positively influence AI user behavior for university students in the eastern region of Thailand. Hypothesis 3 AI user anxiety will positively influence AI user behavior for university students in the eastern region of Thailand. Table 6 and Table 7 display the results of this testing on the four observed constructs. All three hypotheses were supported and accepted by the model shown in Fig. 1 . Table 6 The effect of predicting AI user behavior. Predictor AI User Behavior \(\:\varvec{\beta\:}\) SE \(\:\varvec{\beta\:}\) Standardized Coefficient AI User Intention 0.218 0.064 0.227** AI User Satisfaction 0.235 0.078 0.286** AI User Anxiety 0.161 0.062 0.162* Note: **Correlation is significant at the 0.01 level (two-tailed). *Correlation is significant at the 0.05 level (two-tailed). Table 7 Results of the structural model (hypotheses testing). No. Hypothesis Coefficients N = 435 Results H1 AI User Intention → AI User Behaviour 0.227** Supported H2 AI User Satisfaction → AI User Behaviour 0.286** Supported H3 AI User Anxiety → AI User Behaviour 0.162* Supported Note: **Significant (p < 0.01). *Significant (p < 0.05). Discussion This paper aimed to investigate how the factors AI user intention, AI user satisfaction, and AI user anxiety affect AI user behavior among university students in the eastern region of Thailand. The study utilized a quantitative research design with a Structural Equation Modeling (SEM) approach. The findings show different key insights into university students’ perceptions and behaviors toward using AI technology. Interpretation of the Findings The findings confirmed that AI user intention positively and significantly influenced AI user behavior (β = 0.227, p < 0.01). The results align with the studies designed by Davis ( 1989 ), and Venkatesh et al. ( 2012 ) in that the people having a strong intention to use AI technology tended to engage with it for their personal and academic work. The findings extended The Unified Theory of Acceptance and Use of Technology (UTAUT) framework (Venkatesh et al., 2003 ) by incorporating two AI-specific constructs: AI user satisfaction and AI anxiety. Those two constructs were used to predict the role of behavioral intention in technology adoption. The results suggest that the high levels of user intention can cause an increase in AI technology usage among university students. The Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003 ) provides a strong foundation for understanding technology acceptance and usage behaviors. Core constructs such as Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions have been validated across multiple contexts. UTAUT effectively captures general factors that explain technology adoption. However, AI adoption – particularly among university students – may involve other factors such as user satisfaction and user anxiety that is specific to AI systems. AI user satisfaction was shown to have a significantly positive effect on AI user behavior (β = 0.286, p < 0.01). This result suggests that students who derive efficiency and fulfilment from the use of AI technology tend to continue using AI tools for their classwork. Moreover, satisfaction mediates long-term technology acceptance and engagement (Wang & Lin, 2021 ; Alalwan et al., 2018 ). The literature on educational technology adoption consistently highlights the importance of ease of use and perceived usefulness as determinants of user satisfaction (Al-Emran et al., 2020 ; Venkatesh et al., 2003 ). High levels of user satisfaction can enhance learning experiences with AI tools that were designed for university students. These tools can reduce repetitive work, assist with student engagement, and increase student study efficiency (Zawacki-Richter et al., 2019 ). Satisfaction reflects the users' overall affective response to interacting with AI tools, and includes perceptions of usefulness, ease of use, and trust (Bhattacherjee, 2001 ). In AI contexts, satisfaction can influence continued usage intention and perceived value (Lee & Kozar, 2012 ). When integrated with UTAUT, satisfaction serves as a variable that amplifies the effects of core constructs such as “Performance Expectancy” on behavioral intentions. AI user anxiety had an important impact on AI user behavior, but with a lower coefficient value (β = 0.162, p < 0.05). The results suggest that anxiety might pose challenges or act as a detriment to AI adoption. However, usage anxiety may also motivate university students to use more AI technology. AI usage anxiety can lead to user concerns regarding ethical issues, privacy, and technology complexity (Li et al., 2020 ). Although students experienced some anxiety, they continued to engage with AI tools because of the benefits the students received when using those tools. Moreover, Kumar et al. ( 2021 ) suggested that users who develop experience with familiar AI technology can minimize their anxiety levels. Anxiety towards AI includes apprehension related to AI reliability, job security, privacy concerns, and ethical issues (Kim et al., 2019 ). The original UTAUT emphasizes behavioral and performance-related factors. By adding user anxiety, emotional and psychological barriers to AI adoption can be addressed. Anxiety can negatively influence Behavioral Intention directly or reduce the positive effects of UTAUT factors (Venkatesh & Bala, 2008 ). Recent research emphasizes the importance of emotional and attitudinal factors in technology acceptance (Luo et al., 2018 ; Kim et al., 2020). For instance, Lee and Kozar ( 2012 ) explain that satisfaction is a vital determinant of continued system use, while Kim et al. ( 2019 ) emphasize that anxiety can significantly slow AI adoption. Incorporating these constructs improves the power of the UTAUT model and provides a more complete understanding of AI adoption behaviors among students. This study advances the UTAUT framework by integrating two AI-specific psychological factors: satisfaction and anxiety. As a result, the framework can better reflect the decision-making processes among university students in adopting AI technologies. This extension recognizes that emotional responses and satisfaction levels are critical in AI contexts, especially when users may feel uncertain about new AI systems. Practical Implications One of this study’s purposes is to provide suggestions to AI developers and other related institutions to improve AI-based technology based on users’ insights and feedback. AI-tool developers should place high priority on designing user-friendly and intuitive interfaces that will raise the level of user satisfaction and improve usage behavior, particularly in academic settings. To increase the level of user intention and satisfaction for academic usage, AI development around user-centered designs is recommended. For example, AI developers could offer students tutorial classes, personalized learning recommendations, writing assistance, language translation, and smart summary functions. Although concerns about AI user anxiety, which influences user behavior, were noticeably addressed, AI developers should implement some features to further reduce users’ fears related to security and privacy. For example, improvements to data-usage transparency and AI literacy programs could be implemented. AI developers can enhance AI literacy features for university students. These enhancements should be the first and most critical process in fostering students’ AI adoption. The key focuses of AI tool usage are how students adapt their own learning experience with different styles by using adaptive algorithms. With AI technology, however, users are still concerned with aspects of data privacy, digital responsibility, and ethical issues (Zawacki-Richter et al., 2019 ). AI developers could create features of AI to align with students’ individual responses by emphasizing user-centered design, which is beneficial because it decreases students’ stress and anxiety and provides more confidence to users (Kumar et al., 2021 ). Furthermore, developers can improve AI interfaces by conducting adoptive, interactive processes, AI-usage testing, and incorporating users’ feedback into designs (Norman, 2013 ). The critical role of AI literacy in academic institutions is to proactively integrate AI-related context with an educational curriculum. Adaptive AI-based tools could foster different learning styles and learning cultures that help students and instructors to achieve academic outcomes (Dede et al., 2016 ). For example, in the Thai culture, AI developers might design platforms in the Thai language to fit with the known culture and norms of Thai people when designing interfaces. AI technology is not only about its technical use, but it is also the alignment of ethical implications (Holmes et al., 2019 ). For example, AI tools can help students to think critically while also improving those students’ understanding of ethics. Therefore, educational institutions could apply a holistic approach using both technical and ethical dimensions in the classrooms. Technological tools that can provide personalized advice on students’ performance and engagement levels are better able to improve users’ satisfaction and learning efficacy (Roll & Wylie, 2016 ). In addition to technical skills, AI literacy should include digital ethics, critical thinking, and the ability to interpret and evaluate AI outputs (Long & Magerko, 2020 ). Integrating these competencies into a curriculum prepares students not only to use AI tools but also to critically think about AI outputs in both academic and professional contexts. Educational institutions and AI developers should collaborate to create new academic pedagogical standards by aligning suitable AI technology with learning curriculum and materials. AI integration in educational settings should promote human-AI collaboration rather than simply replacing people with technology. This hybrid model allows instructors to maintain pedagogical integrity while AI handles repetitive tasks, offering personalized insights and freeing educators to focus on higher-order instruction (Luckin, 2017 ). The implementation of educational policies regarding the use of AI-driven platforms will improve student performance, reduce their administrative and cognitive workloads, and improve their study experience (Chang & Hwang, 2020 ). The data governance and building trust are concerned and responsible by the AI developers and academic institutions to be sure in using AI tools. Developers should also be sure that AI tools are accessible to students with diverse learning needs and disabilities. Features like screen-reader compatibility, customizable text sizes, and multilingual support can improve the usability of AI platforms for all students (Al-Azawei et al., 2017 ). Continuous feedback loops between users and developers are important for the continuous improvement of AI tools. This design approach ensures that tools evolve to meet actual user needs, which should improve adoption rates in academic settings (Schuler & Namioka, 1993 ). Applying these strategies may ensure that AI technology can be effectively leveraged to support successful student learning by giving students valuable insights and preferences. Limitations First, since this study focused on university students who study in the eastern region of Thailand, the generalization of the study’s results on the entire population is lacking. For future research, a study can be designed to collect data from a larger scope of people. For example, a study could focus on university students in different regions of Thailand to gain more complete geographical coverage. Second, further study could explore the context of additional psychological impacts of using AI technology in educational settings to better understand users’ insights and perceptions. Conclusion This study highlights AI user intentions, satisfaction, and anxiety that shaped AI user behavior among university students in the eastern region of Thailand. The results revealed the significance of user experiences that enhance AI user engagement. In addition, this study revealed some noticeable concerns about user anxiety. These findings offer dual contributions. On the theoretical side, they support and extend technology acceptance models. On the practical side, they indicate that AI developers should tailor tools to student preferences and work with universities to create standards for AI use, facilitating more effective integration of AI into higher education curricula. These findings offered dual contributions. On the theoretical side, they supported and extended technology acceptance models. On the practical side, they indicated that AI developers should tailor tools to student preferences and work with universities to create standards for AI use, facilitating more effective integration of AI into higher education curricula. Declarations Acknowledgements The corresponding author would like to thank all authors and their families. Author Contributions SK: Conceptualization, methodology, writing-review, and editing. PP: Conceptualization, data collection, methodology, analysis, project administration. TP: Writing-review, material preparation. JVJ: Writing-review and editing. HH: Reviewing and editing. Funding There was no funding for this study. Data Availability Statement The datasets generated and analyzed during the current study, including anonymized survey data, the questionnaire, and variable definitions, are provided as supplementary materials for peer review. Public access to the data is restricted due to ethical and confidentiality considerations. After publication, the data may be made available from the corresponding author upon reasonable request. Ethics approval statement This study was reviewed and approved by the Research Ethics Committee of Burapha University (Approval No. HU094/2568) on 25 September 2025. All research procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Ethical approval was obtained prior to the commencement of data collection. Informed consent statement Informed consent was obtained from all individual participants prior to their participation in the study. Written informed consent was collected by the researchers on 25 September 2025, before data collection commenced, after participants were provided with full information regarding the purpose of the study, research procedures, confidentiality, and their right to withdraw at any time without penalty. Consent for publication was also obtained from all individual participants. For cases where a participant was under the age of 18, consent was obtained from the participant’s parent or legal guardian. Competing interests The authors declare that there are no conflicts of interest related to this study. References Acquah BYS, Arthur F, Salifu I et al (2024) Preservice teachers’ behavioural intention to use artificial intelligence in lesson planning: A dual-staged PLS-SEM-ANN approach. 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Int J Learn Futures 33(1):126–144 Schuler D, Namioka A (1993) Participatory Design: Principles and Practices. CRC Venkatesh V, Bala H (2008) Technology acceptance model 3 and a research agenda on interventions. Decis Sci 39(2):273–315 Venkatesh V, Morris MG, Davis GB et al (2003) User acceptance of information technology: Toward a unified view. MIS Q 27(3):425–478 Venkatesh V, Thong JYL, Xu X (2012) Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q 36(1):157–178 Wang YS, Lin HH (2021) Understanding AI adoption in higher education: The moderating role of academic self-efficacy. Comput Educ 167:104186 Zawacki-Richter O, Marín VI, Bond M et al (2019) Systematic review of research on artificial intelligence applications in higher education. Int J Educational Technol High Educ 16(1):1–27 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviews received at journal 11 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 08 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Editor invited by journal 28 Jan, 2026 Submission checks completed at journal 24 Jan, 2026 First submitted to journal 24 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8590201","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":589438822,"identity":"70e97762-928e-4e1c-a935-e76bffcaee1e","order_by":0,"name":"Siwaporn Kunnapapdeelert","email":"","orcid":"","institution":"Thammasat University","correspondingAuthor":false,"prefix":"","firstName":"Siwaporn","middleName":"","lastName":"Kunnapapdeelert","suffix":""},{"id":589438823,"identity":"aff919cb-f456-4334-bd21-c46585d74eb5","order_by":1,"name":"Passarin Phalitnonkiat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYPACCQZ+IMkM4zLjUYrQItmAUMnYTJQ9BgeI1WLOfvzxh597LPKMzy8+wFzYdpiBv/0A++MCPFose3LMJHueSRSb3XiWwDwTqEXiTAJj8wy87slhY+A5IJG47cYZA2ZeoBaGG0CH8eDTcv75449/gFo2z4BqkSeo5UaCgTTIlg38PRAtBoS0WM54YyYtA9Qy4wZbwuEZ59J5DM8kNs7Gp8WcP/3xxzcH6hL7+w8ffFxQZi0nd/zwgc94HQZnSSQwHABSQMWMDXg0IGvhP4BX4SgYBaNgFIxgAACvQE3qnYzfcQAAAABJRU5ErkJggg==","orcid":"","institution":"Burapha University","correspondingAuthor":true,"prefix":"","firstName":"Passarin","middleName":"","lastName":"Phalitnonkiat","suffix":""},{"id":589438824,"identity":"df89f0f2-c4dd-4ae1-81f5-9975f979a605","order_by":2,"name":"Tuangporn Pinudom","email":"","orcid":"","institution":"Burapha University","correspondingAuthor":false,"prefix":"","firstName":"Tuangporn","middleName":"","lastName":"Pinudom","suffix":""},{"id":589438828,"identity":"e61654af-d749-4b6e-a8ad-7f8496567b7b","order_by":3,"name":"James Vincent Johnson","email":"","orcid":"","institution":"Burapha University","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"Vincent","lastName":"Johnson","suffix":""},{"id":589438836,"identity":"66f0a109-a2f8-4a2c-b1a3-3577f722fefc","order_by":4,"name":"Hanafi Bin Hamzah","email":"","orcid":"","institution":"UCSI University","correspondingAuthor":false,"prefix":"","firstName":"Hanafi","middleName":"Bin","lastName":"Hamzah","suffix":""}],"badges":[],"createdAt":"2026-01-13 09:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8590201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8590201/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102550388,"identity":"80ce693e-8a35-4181-8788-05fcf337fabc","added_by":"auto","created_at":"2026-02-12 23:07:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78379,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized total effects for the reconstructed model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8590201/v1/786e7d4587a48a9c16a7636b.png"},{"id":102746961,"identity":"14103eb5-6f41-4886-beb4-8ad95ca30739","added_by":"auto","created_at":"2026-02-16 09:03:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":943521,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8590201/v1/5534f090-6dd7-4102-a986-4f868fdbfee4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Building a Model of AI Adoption among University Students in the Eastern Region of Thailand","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecently, Artificial Intelligence (AI) has rapidly developed from theoretical concept to transformative technology, which has impacted several sectors including healthcare, finance, and transportation. Within education, AI tools are being used to enhance personal learning, automate personal tasks, and promote interactive environments. Consequently, teaching practice and student experiences are being reshaped. Despite AI being widely used, there remains some critical gaps in the study of AI adoption within an education setting. Psychological and behavioral factors influencing students\u0026rsquo; engagement with such technologies have not been addressed by the existing literature.\u003c/p\u003e \u003cp\u003eSeveral researchers have explored the technical aspects and broad societal impacts of AI. However, the empirical insight into the specific study of AI adoption among university students, especially in culturally diverse contexts such as Thailand, is limited. Most of the existing research emphasizes the key opportunities and challenges in AI\u0026rsquo;s integration and AI\u0026rsquo;s potential to enhance learning experiences and support academic workflows (Ng et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Barus., 2025). The Unified Theory of Acceptance and Use of Technology (UTAUT) has been applied to investigate technology adoption. Additionally, some studies extended the models to incorporate AI-specific constructs. The practical approaches for adapting AI technologies to meet the needs of non-Western educational contexts have still not been fully investigated.\u003c/p\u003e \u003cp\u003eThis study addresses these limitations by investigating the interplay of AI user intention, satisfaction, and anxiety in driving AI adoption behaviors among university students in the eastern part of Thailand. Structural Equation Modeling (SEM) is employed to analyze data from 435 students providing three contributions: 1) Theoretical: The study integrates AI-specific psychological constructs into technology acceptance models, which enhance their applicability to modern educational contexts. 2) Practical: The study provides actionable recommendations for AI developers to improve user-centric designs. 3) Contextual: The study clarifies AI-adoption by students in the eastern region of Thailand.\u003c/p\u003e \u003cp\u003eThis study includes both academic discussion and practical strategies for increasing AI\u0026rsquo;s transformation potential in Thai higher education. The findings highlight the need for collaborative efforts between developers, educators, and policy makers to develop AI systems that are not only technologically robust, but also culturally aligned.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eRecently, Artificial Intelligence (AI) has dramatically developed from theoretical concept to practical technology very quickly. The use of AI significantly impacts modern life. It influences numerous sectors including healthcare, manufacturing, finance, energy, transportation, and education by incorporating technology into our daily life activities. The application of AI for education has been adopted to enhance educational experiences. Numerous researchers have studied the use of generative AI tools for various educational purposes such as curriculum integration, personalized learning, research, academic support,, plagiarism detection, academic integrity, ethical education governance, and administrative automation. Generative AI tools have been applied for creating interactive learning tools, quizzes, simulations, and educational games. AI has been utilized to create flexible content that is customized for individual learning preferences. Further, AI can be used to search journals, review literature, analyze data, write code, generate new ideas, and learn new topics. It promotes flipped classrooms and adaptive learning systems to boost student engagement and critical thinking. Educators, researchers, and publishers employ AI-driven tools for detecting both AI-generated content and plagiarism based on pattern analysis and similarity checks. Moreover, some administrative tasks and resource distributions are easily completed by AI.\u003c/p\u003e \u003cp\u003eVarious research studies have been conducted to find insight into the use of Generative AI in the education sector. Acquah et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) determined factors that influence preservice educators\u0026rsquo; intentions to include AI into their lesson planning. The combination of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was applied to investigate the impact of several factors on behavioral intention. Key predictors of preservice teachers\u0026rsquo; intentions to use AI in lesson planning are social influence, habit, performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation. The results explained that educational institutions can better prepare preservice teachers by addressing the identified factors.\u003c/p\u003e \u003cp\u003eNg et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) explored the application of Generative Artificial Intelligence (GenAI) to education. The study focused on potential opportunities, challenges, and strategies for promoting GenAI adoption. Several opportunities for the use of GenAI for educators include teaching and learning, administration, and student assessment. Numerous institutions might face some challenges when attempting to integrate GenAI such as institutional readiness, educator AI competencies, and students\u0026rsquo; AI ethics. The study suggested that training programs for educators should be provided to enhance educators\u0026rsquo; AI competencies. Clear guidelines of policies and frameworks related to the ethical and effective use of AI in schools should be provided. Lastly, AI software and technical support for both teachers and students should be available.\u003c/p\u003e \u003cp\u003eAmin et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) examined the transformative potential of AI in education, especially within the Open and Distance Learning (ODL) environment. This research is well aligned with the previous research in that AI can tailor educational experiences to each learner's needs. AI enhances both learner engagement and learners\u0026rsquo; outcomes. AI automates administration tasks (e.g. grading and feedback) which can promote institutions to efficiently manage larger numbers of students. An additional opportunity is that AI technologies (e.g. chatbots and speech-to-technologies) can assist diverse learners, including those with disabilities and language barriers.\u003c/p\u003e \u003cp\u003eBarus et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) studied the governance of GenAI in higher education. The study focused on student perceptions. The results reported that undergraduate students most commonly use GenAI for generating ideas for assignments, follow by learning new topics, writing essays or papers, writing programs or query code, and translating language. The influence of GenAI tools and the need for structural frameworks that address ethical and operational challenges in higher education were studied. Based on the insight of undergraduate students, the key factors that were identified included the potential of AI to enhance learning experiences, academic integrity, and data privacy. The authors recommended that higher education institutions should 1) establish an AI ethics committee that includes people from various faculties, 2) include AI competency training modules in the school\u0026rsquo;s curriculum, and 3) regularly hold open discussions on AI ethics for students and faculty.\u003c/p\u003e \u003cp\u003eAlmogren et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated the influencing factors of ChatGPT acceptance in higher education. The study explored how perceived usefulness (PU), ease of use (EU), social influence (SI), and facilitating conditions (FC) affect students\u0026rsquo; and educators\u0026rsquo; willingness to use ChatGPT. This study employed structural equation modeling (SEM), and the results revealed that PU, EU, SI, and FC play important roles for ChatGPT adoption.\u003c/p\u003e \u003cp\u003eSaha et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) studied how the use of AI, including students\u0026rsquo; intentions, could drive learning success in Bangladesh, an emerging economy. The unified theory of acceptance and use of technology (UTAUT) model was examined for predicting educational AI users\u0026rsquo; acceptance. The relationships between performance expectancy (PE), effort expectancy (EE), social influence (SI), subjective norms (SN), facilitation condition (FC), Attitude (ATT), intent to use (IU), and actual use (AU) of AI tools in education were studied. Structural equation modeling (SEM) was then applied to analyze these relationships. The study found PE, EE, SI, FC, and SN have a positive impact on AU, but mediated by IU. The authors recommended that AI adoption in education could be enhanced by encouraging developers to incorporate features such as local language interaction, adaptive learning, and gamification methods. From a managerial standpoint, educational institutions could invest in reliable IT infrastructure, high-speed internet, and technical support for the types of AI learning tools that students use. Such investments would help promote the adoption of educational AI technologies.\u003c/p\u003e \u003cp\u003eDespite the number of studies that explore AI technology for education found in the literature, some limitations still exist. Specifically, there is limited research related to undergraduate students\u0026rsquo; AI perception and the effect of their perceptions on usage behavior. The existing studies mostly emphasize the technical aspects of AI tools or their broad social impact. Research related to the specific factors that affect students\u0026rsquo; interactions with AI tools has not been addressed. Although some researchers have explored variables affecting technology adoption, the relationships among such variables, especially in the context of AI usage for higher education students, remain unexplored. Additionally, there is a lack of actionable insight for AI developers in the academic sector to tailor AI technologies that align with the needs and behaviors of university students. With such alignment, educational outcomes might be improved, which could positively impact the overall institutional system. By addressing these gaps, this study aims to provide more insight into AI adoption within higher education settings. Such insight could contribute to academic literature as well as provide practical applications for AI driven education.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch Objectives\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo investigate student factors regarding AI technology that influence AI user behavior.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo explore the relationships among different variables affecting AI user behavior..\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo suggest how AI technology developers or related institutions could enhance AI technology performance so that it aligns with university students\u0026rsquo; usage.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e "},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003eThe motivation for this study was to explore factors influencing the AI user behavior of university students in the eastern region of Thailand.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Design and Questionnaire Development\u003c/h3\u003e\n\u003cp\u003eThis study employed a quantitative research methodology with a descriptive design to explore students\u0026rsquo; perceptions in using AI technology. The study included 25 items, and individual items were carefully rephrased for the context of student AI user experiences. Students were asked to complete a survey consisting of four parts: personal information, AI user behavior, perceptions toward using AI technology, and suggestions. Part three (students\u0026rsquo; perceptions toward using AI technology) used a 5-point Likert scale to assess all items based on the significance level of each item when deciding to use AI technology. The Likert scale ranged from 1 to 5 where 1 = \u0026ldquo;Totally Disagree\u0026rdquo; and 5 = \u0026ldquo;Totally Agree\u0026rdquo;. All questions and items in the survey were translated in English and Thai, keeping the same meanings in both versions.\u003c/p\u003e\n\u003ch3\u003eResearch Setting and Data Collection\u003c/h3\u003e\n\u003cp\u003eData collection for this study was self-reported using a Google Forms online survey. Due to accessibility and ease of data collection, convenience sampling was used to select online survey participants. The reasons for using this tool were based on the students\u0026rsquo; familiarity with Google Forms and the ease of accessibility using various devices such as smart phones, tablets, or laptops. All respondents were informed of the purpose of the survey, which was clearly stated in the survey introduction page. Students saw this page before starting the survey. Time required to complete the survey was estimated to be 20\u0026ndash;30 minutes. The investigation was carefully designed to ensure samples were selected from university students in the eastern region of Thailand. Questionnaires were distributed to 500 students, and 435 complete and valid responses were returned. Of the 435 samples, 23% were males, 65.3% were females, and 11.7% did not specify their gender. Moreover, 1.8% were younger than 18 years old, 86.9% were 18\u0026ndash;22, 10.6% were 23\u0026ndash;27, and 0.7% were older than 27 years old. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the respondents\u0026rsquo; behavior when using AI technology.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe respondents\u0026rsquo; AI user behavior.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;435\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHow long using AI technology per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShorter than 1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;4 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLonger than 4 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eThe most frequent activity on social media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunicating with others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosting message/photo/VDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpdating news/celebrity/pages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShopping online\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelling a product/service or running a business\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntertaining yourself\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinding new friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSearching for information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eThe main benefit of using AI technology for your studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreasing the level of study interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReducing time doing repetitive work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDesigning class reports/assignment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuiding the prospect examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eWhat activities have you done by using AI technology? \u003cem\u003e(You can answer more than 1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuggesting how your work should be done\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinding the desired information for your work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChecking the information for your work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnswering questions in your work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolving basic problems for your work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003eAfter collecting data from the respondents, the students\u0026rsquo; perceptions were coded. This study applied descriptive statistical analyses which summarize and organized the respondents\u0026rsquo; personal information. Moreover, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) were applied to test the model\u0026rsquo;s hypotheses. Cronbach\u0026rsquo;s alpha was performed as a pretest of proposed variables that were based on respondent data. The values for 25 items were 0.908, and this value exceeded the threshold of 0.70 that is considered acceptable (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFrequency analysis was conducted on the data to obtain the respondents\u0026rsquo; personal information and their behaviors regarding the use of AI technology. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were also applied in this study. Two items were removed from the original list of 25 items, and the 23 remaining items were assessed and grouped into four constructs: AI user intentions, AI user satisfaction, AI user anxiety, and AI user behavior, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Factor extraction was then performed on the findings in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Data inspection techniques for EFA were classified as Kaiser-Meyer-Olkin (KMO) (Dziuban \u0026amp; Shirkey, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Kaiser, 1970) and Bartlett\u0026rsquo;s test of sphericity (Bartlett, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Dziuban \u0026amp; Shirkey, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). KMO values illustrate sample adequacy, and correlation values of more than 0.7 are considered adequate for analyzing the EFA. For this study KMO and Bartlett\u0026rsquo;s test of sphericity were conducted on the data. The KMO value was 0.895 while the Bartlett test value was p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. These results are evidence of the sample\u0026rsquo;s appropriateness (Hutcheson \u0026amp; Sofroniou, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExploratory Factor Analysis and Confirmatory Factor Analysis\u003c/h2\u003e \u003cp\u003eThis paper used EFA and CFA to obtain, merge, and confirm variables. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the rotated component matrix and the four extracted constructs along with the mean, standard deviation, factor loading, and construct reliability values for the 23 remaining items. All factor-loading values were relatively high at over 0.5. These values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEFA and CFA results and descriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct (AVE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.D.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor Loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eAI user intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCog2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCog3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCog4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCog5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCog6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCog7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCog8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCog9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAI user satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAff1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAff2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAff3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAff4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAI user anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAff5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAff7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAff8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eAI user behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeh1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeh2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeh3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeh4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeh5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeh6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeh7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeh8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: AVE denotes average variance extracted; CR denotes construct reliability, and S.D. denotes standard deviation.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe variables were extracted into four dimensions, and the factor matrix loadings in each construct are considered acceptable as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor loading acceptance levels with required sample sizes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum Acceptable Factor Loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample Size Needed for Significance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003eSource: Hair et al. (2010)\u003c/p\u003e\n\u003cp\u003eConfirmatory Factor Analysis (CFA) was conducted in AMOS. The results show that the assessment of university students\u0026rsquo; AI user behavior confirmed their behavior to use AI technology as revealed by the model of fit summary in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural model fit indices.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel Findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-Off Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMIN/DF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBollen (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1989\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKline (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHu \u0026amp; Bentler (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBentler (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1990\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHu \u0026amp; Bentler (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the CFA suggest some variables should be combined, while some variables should be removed. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the correlation matrix of the observed variables from the model, which was tested using a Pearson correlation measuring the relationships among each construct.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe observed variables\u0026rsquo; correlation coefficients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI User Intention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI User Satisfaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI User Intention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.582**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI User Satisfaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.582**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI User Anxiety\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote: **Correlation is significant at a level of 0.01 (two-tailed).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Correlation is significant at a level of 0.05 (two-tailed).\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003eHypotheses Testing\u003c/h3\u003e\n\u003cp\u003eFor this paper, Structural Equation Modeling (SEM) was used to test the relationships between the observed variables. Regarding the findings, the modeling revealed four constructs: AI user intention, AI user satisfaction, AI user anxiety, and AI user behavior. From these four observed constructs, three hypotheses were created to explore how each factor affected AI user behavior:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eAI user intention will positively influence AI user behavior for university students in the eastern region of Thailand.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eAI user satisfaction will positively influence AI user behavior for university students in the eastern region of Thailand.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eAI user anxiety will positively influence AI user behavior for university students in the eastern region of Thailand.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e display the results of this testing on the four observed constructs. All three hypotheses were supported and accepted by the model shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effect of predicting AI user behavior.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAI User Behavior\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eStandardized Coefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI User Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.227**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI User Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.286**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI User Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: **Correlation is significant at the 0.01 level (two-tailed).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Correlation is significant at the 0.05 level (two-tailed).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e Results of the structural model (hypotheses testing).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 435\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eAI User Intention \u003cspan style='text-align: start;color: rgb(71, 71, 71);background-color: rgb(255, 255, 255);font-size: 16px;font-family: \"'\u003e\u0026rarr;\u003c/span\u003e AI User Behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.227**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eAI User Satisfaction \u003cspan style='text-align: start;color: rgb(71, 71, 71);background-color: rgb(255, 255, 255);font-size: 16px;font-family: \";'\u003e\u0026rarr;\u003c/span\u003e AI User Behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.286**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eAI User Anxiety \u003cspan style='text-align: start;color: rgb(71, 71, 71);background-color: rgb(255, 255, 255);font-size: 16px;font-family: \";'\u003e\u0026rarr;\u003c/span\u003e AI User Behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.162*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: **Significant (p \u0026lt; 0.01).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;*Significant (p \u0026lt; 0.05).\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper aimed to investigate how the factors AI user intention, AI user satisfaction, and AI user anxiety affect AI user behavior among university students in the eastern region of Thailand. The study utilized a quantitative research design with a Structural Equation Modeling (SEM) approach. The findings show different key insights into university students\u0026rsquo; perceptions and behaviors toward using AI technology.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of the Findings\u003c/h2\u003e \u003cp\u003eThe findings confirmed that AI user intention positively and significantly influenced AI user behavior (β\u0026thinsp;=\u0026thinsp;0.227, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The results align with the studies designed by Davis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), and Venkatesh et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in that the people having a strong intention to use AI technology tended to engage with it for their personal and academic work. The findings extended The Unified Theory of Acceptance and Use of Technology (UTAUT) framework (Venkatesh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) by incorporating two AI-specific constructs: AI user satisfaction and AI anxiety. Those two constructs were used to predict the role of behavioral intention in technology adoption. The results suggest that the high levels of user intention can cause an increase in AI technology usage among university students.\u003c/p\u003e \u003cp\u003eThe Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) provides a strong foundation for understanding technology acceptance and usage behaviors. Core constructs such as Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions have been validated across multiple contexts. UTAUT effectively captures general factors that explain technology adoption. However, AI adoption \u0026ndash; particularly among university students \u0026ndash; may involve other factors such as user satisfaction and user anxiety that is specific to AI systems.\u003c/p\u003e \u003cp\u003eAI user satisfaction was shown to have a significantly positive effect on AI user behavior (β\u0026thinsp;=\u0026thinsp;0.286, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This result suggests that students who derive efficiency and fulfilment from the use of AI technology tend to continue using AI tools for their classwork. Moreover, satisfaction mediates long-term technology acceptance and engagement (Wang \u0026amp; Lin, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Alalwan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The literature on educational technology adoption consistently highlights the importance of ease of use and perceived usefulness as determinants of user satisfaction (Al-Emran et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). High levels of user satisfaction can enhance learning experiences with AI tools that were designed for university students. These tools can reduce repetitive work, assist with student engagement, and increase student study efficiency (Zawacki-Richter et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSatisfaction reflects the users' overall affective response to interacting with AI tools, and includes perceptions of usefulness, ease of use, and trust (Bhattacherjee, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In AI contexts, satisfaction can influence continued usage intention and perceived value (Lee \u0026amp; Kozar, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). When integrated with UTAUT, satisfaction serves as a variable that amplifies the effects of core constructs such as \u0026ldquo;Performance Expectancy\u0026rdquo; on behavioral intentions.\u003c/p\u003e \u003cp\u003eAI user anxiety had an important impact on AI user behavior, but with a lower coefficient value (β\u0026thinsp;=\u0026thinsp;0.162, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results suggest that anxiety might pose challenges or act as a detriment to AI adoption. However, usage anxiety may also motivate university students to use more AI technology. AI usage anxiety can lead to user concerns regarding ethical issues, privacy, and technology complexity (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although students experienced some anxiety, they continued to engage with AI tools because of the benefits the students received when using those tools. Moreover, Kumar et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) suggested that users who develop experience with familiar AI technology can minimize their anxiety levels.\u003c/p\u003e \u003cp\u003eAnxiety towards AI includes apprehension related to AI reliability, job security, privacy concerns, and ethical issues (Kim et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The original UTAUT emphasizes behavioral and performance-related factors. By adding user anxiety, emotional and psychological barriers to AI adoption can be addressed. Anxiety can negatively influence Behavioral Intention directly or reduce the positive effects of UTAUT factors (Venkatesh \u0026amp; Bala, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent research emphasizes the importance of emotional and attitudinal factors in technology acceptance (Luo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kim et al., 2020). For instance, Lee and Kozar (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) explain that satisfaction is a vital determinant of continued system use, while Kim et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) emphasize that anxiety can significantly slow AI adoption. Incorporating these constructs improves the power of the UTAUT model and provides a more complete understanding of AI adoption behaviors among students.\u003c/p\u003e \u003cp\u003eThis study advances the UTAUT framework by integrating two AI-specific psychological factors: satisfaction and anxiety. As a result, the framework can better reflect the decision-making processes among university students in adopting AI technologies. This extension recognizes that emotional responses and satisfaction levels are critical in AI contexts, especially when users may feel uncertain about new AI systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePractical Implications\u003c/h2\u003e \u003cp\u003eOne of this study\u0026rsquo;s purposes is to provide suggestions to AI developers and other related institutions to improve AI-based technology based on users\u0026rsquo; insights and feedback. AI-tool developers should place high priority on designing user-friendly and intuitive interfaces that will raise the level of user satisfaction and improve usage behavior, particularly in academic settings. To increase the level of user intention and satisfaction for academic usage, AI development around user-centered designs is recommended. For example, AI developers could offer students tutorial classes, personalized learning recommendations, writing assistance, language translation, and smart summary functions. Although concerns about AI user anxiety, which influences user behavior, were noticeably addressed, AI developers should implement some features to further reduce users\u0026rsquo; fears related to security and privacy. For example, improvements to data-usage transparency and AI literacy programs could be implemented.\u003c/p\u003e \u003cp\u003eAI developers can enhance AI literacy features for university students. These enhancements should be the first and most critical process in fostering students\u0026rsquo; AI adoption. The key focuses of AI tool usage are how students adapt their own learning experience with different styles by using adaptive algorithms. With AI technology, however, users are still concerned with aspects of data privacy, digital responsibility, and ethical issues (Zawacki-Richter et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). AI developers could create features of AI to align with students\u0026rsquo; individual responses by emphasizing user-centered design, which is beneficial because it decreases students\u0026rsquo; stress and anxiety and provides more confidence to users (Kumar et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, developers can improve AI interfaces by conducting adoptive, interactive processes, AI-usage testing, and incorporating users\u0026rsquo; feedback into designs (Norman, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe critical role of AI literacy in academic institutions is to proactively integrate AI-related context with an educational curriculum. Adaptive AI-based tools could foster different learning styles and learning cultures that help students and instructors to achieve academic outcomes (Dede et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For example, in the Thai culture, AI developers might design platforms in the Thai language to fit with the known culture and norms of Thai people when designing interfaces. AI technology is not only about its technical use, but it is also the alignment of ethical implications (Holmes et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, AI tools can help students to think critically while also improving those students\u0026rsquo; understanding of ethics. Therefore, educational institutions could apply a holistic approach using both technical and ethical dimensions in the classrooms. Technological tools that can provide personalized advice on students\u0026rsquo; performance and engagement levels are better able to improve users\u0026rsquo; satisfaction and learning efficacy (Roll \u0026amp; Wylie, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to technical skills, AI literacy should include digital ethics, critical thinking, and the ability to interpret and evaluate AI outputs (Long \u0026amp; Magerko, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Integrating these competencies into a curriculum prepares students not only to use AI tools but also to critically think about AI outputs in both academic and professional contexts.\u003c/p\u003e \u003cp\u003eEducational institutions and AI developers should collaborate to create new academic pedagogical standards by aligning suitable AI technology with learning curriculum and materials. AI integration in educational settings should promote human-AI collaboration rather than simply replacing people with technology. This hybrid model allows instructors to maintain pedagogical integrity while AI handles repetitive tasks, offering personalized insights and freeing educators to focus on higher-order instruction (Luckin, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The implementation of educational policies regarding the use of AI-driven platforms will improve student performance, reduce their administrative and cognitive workloads, and improve their study experience (Chang \u0026amp; Hwang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The data governance and building trust are concerned and responsible by the AI developers and academic institutions to be sure in using AI tools. Developers should also be sure that AI tools are accessible to students with diverse learning needs and disabilities. Features like screen-reader compatibility, customizable text sizes, and multilingual support can improve the usability of AI platforms for all students (Al-Azawei et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Continuous feedback loops between users and developers are important for the continuous improvement of AI tools. This design approach ensures that tools evolve to meet actual user needs, which should improve adoption rates in academic settings (Schuler \u0026amp; Namioka, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Applying these strategies may ensure that AI technology can be effectively leveraged to support successful student learning by giving students valuable insights and preferences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eFirst, since this study focused on university students who study in the eastern region of Thailand, the generalization of the study\u0026rsquo;s results on the entire population is lacking. For future research, a study can be designed to collect data from a larger scope of people. For example, a study could focus on university students in different regions of Thailand to gain more complete geographical coverage. Second, further study could explore the context of additional psychological impacts of using AI technology in educational settings to better understand users\u0026rsquo; insights and perceptions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights AI user intentions, satisfaction, and anxiety that shaped AI user behavior among university students in the eastern region of Thailand. The results revealed the significance of user experiences that enhance AI user engagement. In addition, this study revealed some noticeable concerns about user anxiety. These findings offer dual contributions. On the theoretical side, they support and extend technology acceptance models. On the practical side, they indicate that AI developers should tailor tools to student preferences and work with universities to create standards for AI use, facilitating more effective integration of AI into higher education curricula. These findings offered dual contributions. On the theoretical side, they supported and extended technology acceptance models. On the practical side, they indicated that AI developers should tailor tools to student preferences and work with universities to create standards for AI use, facilitating more effective integration of AI into higher education curricula.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author would like to thank all authors and their families.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSK: Conceptualization, methodology, writing-review, and editing.\u003c/p\u003e\n\u003cp\u003ePP: Conceptualization, data collection, methodology, analysis, project administration.\u003c/p\u003e\n\u003cp\u003eTP: Writing-review, material preparation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJVJ: Writing-review and editing.\u003c/p\u003e\n\u003cp\u003eHH: Reviewing and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study, including anonymized survey data, the questionnaire, and variable definitions, are provided as supplementary materials for peer review. Public access to the data is restricted due to ethical and confidentiality considerations. After publication, the data may be made available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Research Ethics Committee of Burapha University (Approval No. HU094/2568) on 25 September 2025. All research procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Ethical approval was obtained prior to the commencement of data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants prior to their participation in the study. Written informed consent was collected by the researchers on 25 September 2025, before data collection commenced, after participants were provided with full information regarding the purpose of the study, research procedures, confidentiality, and their right to withdraw at any time without penalty. Consent for publication was also obtained from all individual participants. For cases where a participant was under the age of 18, consent was obtained from the participant\u0026rsquo;s parent or legal guardian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest related to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcquah BYS, Arthur F, Salifu I et al (2024) Preservice teachers\u0026rsquo; behavioural intention to use artificial intelligence in lesson planning: A dual-staged PLS-SEM-ANN approach. Computers Education: Artif Intell 7:100307\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlalwan AA, Dwivedi YK, Rana NP et al (2018) Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk, and self-efficacy. 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MIS Q 36(1):157\u0026ndash;178\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang YS, Lin HH (2021) Understanding AI adoption in higher education: The moderating role of academic self-efficacy. Comput Educ 167:104186\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZawacki-Richter O, Mar\u0026iacute;n VI, Bond M et al (2019) Systematic review of research on artificial intelligence applications in higher education. Int J Educational Technol High Educ 16(1):1\u0026ndash;27\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"AI Adoption, Artificial Intelligence (AI), Higher Education, Structural Equation Modeling (SEM), Technology Acceptance","lastPublishedDoi":"10.21203/rs.3.rs-8590201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8590201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on the remarkable use of Artificial Intelligence (AI) among university students in their educational settings, this study investigated student AI adoption by focusing on students in the eastern region of Thailand. The methodology utilized a quantitative approach and Structural Equation Modeling (SEM) on data obtained from 435 survey respondents. The study examined both psychological and behavioral variables affecting AI usage by employing the Unified Theory of Acceptance and Use of Technology (UTAUT). The results of the model suggested three AI-specific psychological factors (intention, satisfaction, and anxiety) that significantly predict AI user behavior. AI user intention and AI user satisfaction have significant impacts on AI user behavior. Additionally, this paper highlights the need for collaborative effort between AI developers, educators, and policy makers to further develop an AI ecosystem that is both technologically sound and culturally aligned with educational goals and societal values. This research contributes to the literature addressing AI in educational environments by offering an empirical study and by giving a refined theoretical model to understand AI adoption among university students.\u003c/p\u003e","manuscriptTitle":"Building a Model of AI Adoption among University Students in the Eastern Region of Thailand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 23:06:58","doi":"10.21203/rs.3.rs-8590201/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-21T19:43:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T14:55:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271780984702806803183743517193055338996","date":"2026-02-18T14:30:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147966574902578440488456079152104859390","date":"2026-02-13T02:06:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T07:47:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301311784844525294923475253098920726165","date":"2026-02-09T02:18:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-08T23:54:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T22:40:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T07:30:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-24T08:27:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-01-24T06:20:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"01f22c4b-ad84-4243-b6aa-9fcf8f5b74e0","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":62714249,"name":"Business and commerce/Business and management"},{"id":62714250,"name":"Social science/Business and management"},{"id":62714251,"name":"Social science/Education"},{"id":62714252,"name":"Business and commerce/Information systems and information technology"},{"id":62714253,"name":"Biological sciences/Psychology"},{"id":62714254,"name":"Social science/Psychology"},{"id":62714255,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-04-21T19:53:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 23:06:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8590201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8590201","identity":"rs-8590201","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}