Abstract
With the rapid evolution of educational technology and the resulting changes in the academic environment, the application of artificial intelligence in higher education not only involves technological change but also represents the generational transformation of educational concepts and culture in Asia. This study thus sheds light on how cross-national and generational factors influence instructors’ beliefs, classroom practices, and the implementation of artificial intelligence in Chinese language education at the university level. Based on the Technology Acceptance Model, Cultural Context Theory, and Generational Theory, we adopted an explanatory sequential mixed-methods design (survey: n = 98; interviews: n = 12) to study Sino–Thai educators across four generations. The research results have revealed two intersecting gradients: the first is the “national gradient,” which is reflected in the teaching approach of Thai instructors that is deeply rooted in local culture; the second is the “generational gradient,” which is characterized by the younger generation of instructors having a greater advantage in technological adaptability, and this generational characteristic transcends national boundaries. The intersection of these two gradients forms a complementary relationship between the stability of teaching culture and technological agility, laying a strategic foundation for the innovative development of higher education in China and Thailand.
Keywords
Introduction
Within the contemporary landscape of higher education, institutions are undergoing transformative restructuring influenced by systemic shocks due to geopolitical tensions, pandemics, and regional conflicts (Ul Hassan et al., 2025). Simultaneously, the advancement of technologies such as artificial intelligence in higher education has not only intensified these transformations but also reshaped the modalities of collaboration, mobility, and cross-border research, which concurrently generate increasing pressures on how different generations of academics engage with digital tools and pedagogical innovation (Kovari, 2025). Coincidentally, these international transformations align with the historical criticisms of hegemonic internationalization strategies based on prevailing models that continue to remain excessively focused on mobility flows, ranks, and institutional partnerships but underestimate localized environments and human dimensions. Hans De Wit (2020) promotes more balanced and qualitative strategies with a focus on “internationalization at home” and public mobilization. Concurrently, Marginson (2022) outlines global higher education as a geo-cognitive landscape supported by material sets and cultural imaginings, characterized by Anglo-American hegemony yet susceptible to emergent patterns of epistemic diversity and multipolar alignment. While these criticisms are insightful macro-level overviews, they take little note of the way in which internationalization is felt and enacted by various cohorts of the academic workforce, especially in the non-West. Within the Asian higher education environment where the professoriate spans Boomers to Gen Z, variations in beliefs, pedagogical orientations, and acceptance of AI tools in language education across generations are the crucial but, to date, relatively under-analyzed mediating variables in determining how institutions adapt to internationalization challenges and opportunities.
Western-Centric Narratives and Asian Knowledge Realignment
Despite all of these criticisms, much of the scholarship related to these changes remains Western-centric, written mostly from Anglophone contexts (De Wit & Jones, 2022; Huang & Chen, 2024). This imbalance constricts our comprehension of how global change is felt and negotiated in Asian higher education systems and threatens to obscure other models of internationalization underway in Asia, where generational, linguistic, and cultural forces have equally profound effects. Significantly, as far back as 2016, China had already led the United States in scientific publication production with more than 426,000 studies (18.6% of global output), surpassing the U.S.’s 409,000, marking its rise as the world’s largest producer of research publications (Kulkarni, 2018). This quantitative realignment in global knowledge production further fueled the debate on internationalization and shifting global paradigms of knowledge production. While Asian universities grow increasingly globally networked, the age structure of their teaching personnel is a pivotal, although too often neglected, element in determining pedagogic adaptation and institutional policy.
Generational Theory and Its Underutilization in Higher Education
In the Asian context, this neglect is compounded by the paucity of research on generational dynamics among the academic professoriate, an understudied dimension of higher education reform (Ballová Mikušková, 2023; Yaakob et al., 2020). Generational theory predicts that cohorts defined by distinctive historical and cultural contexts develop enduring attitudes, values, and orientations toward work and technology (Costanza & Finkelstein, 2015; Lyons & Kuron, 2014). In the teaching profession, these differences manifest in contradictory instructional philosophies, classroom management styles, and levels of openness to pedagogical creativity and technological infusion. However, in the majority of Asian universities, curricula policies and faculty development plans remain too generic, with insufficient sensitivity to the diverse needs of different generational groups. Such a lack of differentiation risks undermining institutional strategies for improving the quality of teaching. For instance, despite rapid technological advances, faculty development policies in Asian higher education still adopt one-size-fits-all approaches that neglect generational diversity and cultural context. This misalignment undermines institutional innovation capacity, particularly in the integration of AI into teaching. The co-presence of different teacher generations within the same institutional environment creates opportunities for pedagogical imagination but also generates tensions in coordinating beliefs, practices, and technology adoption across generations. Although such generational dynamics are increasingly valued, empirical research to date remains patchy and limited in scope, with basic questions still unanswered about how such dynamics evolve in different cultural and institutional environments. Understanding these generational and cultural intersections is therefore not only theoretically relevant but also practically urgent for achieving sustainable educational transformation (Wang & Duan, 2025).
Research Gaps
The rationale for the present study arises from the convergence of three systemic challenges: (1) the lack of empirical evidence on how intergenerational diversity shapes digital pedagogy in non-Western higher education; (2) the dominance of Western-centric models that overlook Asian cultural and institutional specificities; and (3) the absence of differentiated faculty development strategies informed by generational realities. In light of these challenges, this study is significant for three reasons. First, it offers the first empirical comparison of generational differences in Chinese language education between nations. Secondly, it brings cultural and technological aspects together along with the generational factor, in this way extending the explanatory power of the theory beyond Western higher education. Thirdly, it offers practical insights into faculty development policies for Asian universities navigating digital transformation. Addressing these challenges is crucial for advancing both theory and practice in educational technology and for guiding policy reforms in teacher education across Asia. Earlier research has mostly treated these variables in isolation, examining beliefs, pedagogy, or technology adoption independently, rather than examining these variables comprehensively (Dignath et al., 2022; Tondeur, 2020; Yang et al., 2025; Yang & Yanchinda, 2024; Zhang et al., 2025). Most of the existing research is also grounded in Western higher education systems, leading to an incomplete understanding of intergenerational dynamics in Asian higher education systems (De Wit & Jones, 2022; Huang & Chen, 2024). Moreover, generational studies in education remain largely domestic comparisons, hence limiting their capacity to explain how cultural, institutional, and linguistic factors crosscut in shaping faculty attitudes and practices (Mullick et al., 2025; Yaakob et al., 2020; Zhou & Deolalikar, 2022). Such a mismatch represents a significant gap between the timely need for faculty development policies addressed to multigenerational realities and the modest empirical evidence base to inform such reforms. The present study thus provides a timely contribution and a specific research angle based on Sino-Thai comparison, which is particularly appropriate in this regard: China, as the source and primary exporter of Chinese language teaching personnel and education, provides the supply-side perspective, whereas Thailand, being the largest host country for Chinese language students in Southeast Asia, is the demand-side environment in which teaching is dependent on both locally trained personnel and imported personnel from China. By conducting a Sino–Thai comparative study, the research extends generational theory beyond a single-country context and provides empirical evidence on the dynamics at the intersection of cultural and institutional pressures and age-based variations. The results provide a theory-grounded and context-specific basis for formulating differentiated faculty development strategies for multigenerational classroom environments across Asia. Building on this, the present study pursues three objectives: (1) To compare generational differences in beliefs, pedagogical practices, and AI acceptance in language education among Chinese language instructors between China and Thailand. (2) To generate nuanced insights into intergenerational patterns that can inform differentiated faculty development strategies in Sino-Thai higher education. (3) To propose evidence-based and context-sensitive strategies for enhancing teaching quality and supporting multigenerational faculty development in Asian higher education.
Methodology
Research Design
The study adopted an explanatory sequential mixed-methods research design to investigate the generational dynamics in teacher beliefs, classroom pedagogical practices, and perceptions of AI among Sino-Thai instructors. The questionnaire survey during the quantitative phase was conducted online to enable between-group comparisons. The qualitative phase included semi-structured interviews to explore how teacher beliefs, classroom practices, and attitudes towards the integration of AI varied among different generations.
Research Context
Mae Fah Luang University (MFU) in Thailand provides a unique context to examine intergenerational variations in Asia higher education. The School of Sinology is recognized nationally as a leading and comprehensive center for Chinese language education and research (Yang & Jirawit, 2025). MFU employs a hybrid instructional workforce, comprising both locally trained Sino-Thai instructors and Chinese volunteer or visiting faculty dispatched by Beijing Normal University and Xiamen University.
Population and Participants
The research sample consisted of Chinese language educators from the School of Sinology at Mae Fah Luang University (MFU). In 2024, MFU employed a total of 614 academic staff members, including 519 full-time and 95 part-time instructors. A stratified purposive sampling strategy was used to recruit 98 full-time instructors for the quantitative phase of the study, representing four generational cohorts: Boomers, Generation X, Generation Y, and Generation Z.
Inclusion criteria required that participants: (1) were full-time academic staff employed at MFU for at least two consecutive academic years; (2) had primary responsibility for teaching Chinese language or related courses, with a minimum of two courses per semester; (3) possessed at least two years of teaching experience in Chinese language education; (4) belonged to one of the four target generational cohorts defined by standardized birth-year ranges; (5) demonstrated sufficient language proficiency to participate in surveys and interviews; and (6) provided informed consent.
From the 98 participants, 12 instructors (three from each generational cohort) were purposively selected for semi-structured interviews, with balanced representation in teaching experience, nationality, and generational group.
Research Instruments
The present study employed a structured questionnaire and a semi-structured interview protocol for data collection. The questionnaire was developed around four domains of Chinese language education: (1) demographic and generational background, (2) teaching beliefs based on the Teacher Belief Model (Fives & Buehl, 2012; Pajares, 1992), (3) pedagogical practices grounded in the Theory of Pedagogical Practice (Vygotsky, 1978), and (4) AI acceptance based on the Technology Acceptance Model (Davis, 1989). It consisted of 60 closed-ended items rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree).
The content validity was established using the Item Objective Congruence (IOC) index with three specialists in Chinese language pedagogy and educational technology. All items were initially reviewed, and those with IOC values below the 0.80 criterion were revised or removed based on expert feedback. A pilot test was conducted with 15 instructors to assess item clarity, response comprehension, and overall structure. These pilot respondents did not participate in the main study to avoid potential test effects or response bias. Internal reliability was calculated using Cronbach’s alpha. The coefficients indicated strong internal consistency across the subscales: Teaching Beliefs (α = 0.86), Classroom Practice (α = 0.88), and AI Acceptance (α = 0.91). After data collection, an item refinement procedure was conducted through exploratory factor analysis (EFA) to remove poorly performing items with low communalities or cross-loadings, thereby ensuring a more robust factor structure.
The semi-structured interview protocol aligned with the questionnaire domains and consisted of 10 guiding questions organized into four thematic areas: teaching beliefs, classroom practice, AI use and acceptance, and generational and cultural perspectives. To ensure content validity, the interview guide was reviewed by two field experts. A pilot interview with two teachers was conducted to evaluate question clarity, sequencing, and cultural appropriateness, and minor refinements were made accordingly.
Data Analysis
Quantitative data were analyzed using IBM SPSS Statistics (version 29). Descriptive statistics (means, standard deviations, and distribution frequencies) were generated to report participant characteristics and variable distributions. Independent samples t-tests were employed to examine inter-group differences between Chinese and Thai educators on the dimensions of teaching beliefs, classroom teaching behaviors, and acceptance of AI. To examine generational differences among Boomers, Generation X, Generation Y, and Generation Z, Welch’s ANOVA was employed to account for unequal group sizes and potential violations of homogeneity of variances. Post-hoc pairwise comparisons were conducted using the Games–Howell procedure, which is robust to unequal variances and does not require the assumption of homogeneity. Effect size measures on all the inferential tests provided an indication of the magnitude of the intergroup differences (Cohen’s d on the t-tests and η2 on the ANOVA tests). Exploratory factor analysis (EFA) was also employed to confirm the instrument’s multidimensional structure and to identify the final scale items to be retained on the instrument. Cronbach’s alpha measures were also recalculated after the last data collection to confirm the internal consistency reliability of the measure.
Transcripts of interviews were imported verbatim into NVivo 14 and coded and analyzed. Initial codes were inductively derived from the data and then were organized into higher-order categories and overarching themes. For analytical rigor, two researchers coded the subsample of the transcripts independently, and the resultant coding demonstrated high inter-coder agreement (Cohen’s κ = 0.82). Differences between the researchers were resolved through discussion between peers with mutual agreement. Collection of data stopped once data saturation took place, such that new concepts and themes did not arise out of successive interviews. This ensured the thematic structure was also strong and representative of the group of participants. By repeated comparison and constant feedback between the coders, the coded framework became complete and unambiguous.
Combination of quantitative and qualitative data: Triangulation in the methodological direction guided the combination of the two strands of data. Quantitative outcomes provided macro-level patterns by nationality and by generation, and the qualitative outcomes provided the underlying explanations for these patterns embedded within the contextual settings. The integrated analytical approach strengthened the research results’ validity, trustworthiness, and richness of interpretation.
Ethical Considerations
The present study focused on non-sensitive educational attitudes and involved minimal risk to participants; therefore, formal approval from an ethics committee was not required. The research project was officially approved by the Research and Academic Service Division of Mae Fah Luang University. Informed consent was obtained from all participating instructors, and permission to conduct the study was granted by the dean of the School of Sinology. Participation was voluntary, and all responses were treated confidentially.
Results
A principal axis factoring with Promax rotation was used to reduce the 60-item survey. The value of KMO was 0.927 and Bartlett’s test was significant (χ2 (1770) = 6542.18, p < .001), indicating there was sampling adequacy for factor analysis. The 13 low-loading or cross-loading items were then eliminated, leaving the remaining 47 items loading onto 13 factors, which explained 72.6% of the variance. The factor loadings ranged 0.57 to 0.86. Cronbach’s α coefficients ranged 0.80 to 0.91 across subscales, with overall reliability being 0.94. These results define a stable and theoretically coherent factor structure indicative of strong internal consistency, establishing construct validity and reliability of the instrument for further analyses.
Independent-samples t-tests showed significant group differences in the responses of Thai and Chinese teachers in several pedagogical and AI-related factors. Higher agreement levels in Instructional Planning (t (238) = 3.12, p = 0.002, Cohen’s d = 0.52), Classroom Management (t (238) = 3.50, p = 0.001, Cohen’s d = 0.58), and Assessment and Feedback (t (238) = 2.96, p = 0.004, Cohen’s d = 0.50) indicated that the Thailand-based teachers exhibited a more locally embedded pedagogical routine. On the other hand, Chinese teachers exhibited more endorsement in the areas of perceived usefulness (t (238) = −3.12, p = 0.002, Cohen’s d = 0.52), perceived ease of use (t (238) = −3.50, p = 0.001, Cohen’s d = 0.59), behavioral intention (t (238) = −2.08, p = 0.040, Cohen’s d = 0.38), and somewhat higher language pedagogy beliefs (t (238) = −2.01, p = 0.047, Cohen’s d = 0.36), which showed a higher inclination toward accepting AI and higher pedagogical self-efficacy.
Welch’s analysis of variance (ANOVA) identified distinct generational differences in both the acceptance of artificial intelligence and pedagogic fields. Significant effects were found in Perceived Usefulness (F (3, 38.4) = 8.72, p < 0.001, η2 = 0.19), Perceived Ease of Use (F (3, 36.9) = 9.14, p < 0.001, η2 = 0.21), Attitude Toward Artificial Intelligence (F (3, 40.9) = 5.78, p = 0.001, η2 = 0.12), and Behavioral Intention (F (3, 39.6) = 7.56, p < 0.001, η2 = 0.17). There was a tendency for Generation Y and Generation Z to agree more strongly in these AI-related constructs than there was among Boomers and Generation X, indicating higher digital familiarity. There were lower levels of perceived usefulness and perceived ease of use, as expected, among Boomers, yet they differed non-significantly in behavioral intention (p = 0.115) and seemed to reflect a “willing but less able” mode of adopting technology. There were also significant differences in AI-enhanced Teaching (F (3, 41.5) = 5.48, p = 0.002, η2 = 0.12), Classroom Management (F (3, 40.7) = 5.12, p = 0.003, η2 = 0.11), and in Beliefs toward Language Pedagogy (F (3, 39.9) = 4.26, p = 0.009, η2 = 0.10). There was a heightened sense of technology integration among Generation Y and Generation Z, while the Boomers indicated a heightened emphasis on traditional pedagogical beliefs and classroom experience.
Post-hoc Games–Howell tests indicated that there were significant generational differences in several pedagogy- and AI-related areas. There was significantly higher agreement among Generation Y (Mean Difference = 0.42, p = 0.002) and Generation Z (Mean Difference = 0.38, p = 0.004) than among Boomers in relation to AI-Enhanced Teaching, but there was no difference between Generation Y and Generation Z. Both Perceived Usefulness and Perceived Ease of Use, for both Generation Y and Generation Z, showed significantly higher levels than for Boomers, while Generation X occupied an intermediate position, showing no statistically significant differences in relation to either Boomers or Generation Y. There were also significant differences in Behavioral Intention (Generation Y and Generation Z > Boomers), as the younger generations indicated greater openness to AI integration in learning environments. Compared to Boomers, the levels in Language Pedagogy Beliefs and Classroom Management among Generation Z were significantly lower, indicating lower alignment with traditional learning formats and pedagogical models.
Discussion
Our results confirm the existence of a clear national difference in pedagogic orientation and technology adoption. The Thai instructors showed much higher agreement levels in Instructional Planning (t = 3.12, p = 0.002, d = 0.52), in Classroom Management (t = 3.50, p = 0.001, d = 0.58), and in Assessment and Feedback (t = 2.96, p = 0.004, d = 0.50). The same pattern was also exhibited in interview discourses, as the Boomer and Generation X participants frequently highlighted the values of “teaching as structure,” “chalk over cloud,” and “silence as learning,” indicating their tendency toward order in pedagogic rituals and interactional order. In sharp contrast, Chinese teachers showed much higher agreement levels in Perceived Usefulness (t = −3.12, p = 0.002, d = 0.52), in Perceived Ease of Use (t = −3.50, p = 0.001, d = 0.59), in Behavioral Intention (t = −2.08, p = 0.040, d = 0.38), and marginally in Language Pedagogy Beliefs (t = −2.01, p = 0.047, d = 0.36). Interviews resonated with this orientation: most Chinese teachers framed AI as a “co-worker, not master” and felt confident in gradually incorporating digital tools into their classroom practice. A plausible mechanism behind such a difference is contextual familiarity and infrastructural asymmetry. The high-context and hierarchical classroom culture of the pedagogical routines of the Thai group is consistent with an orientation toward stability and role definition in the instructional order (Edward T. Hall, 1976). The Chinese group’s higher technology acceptance, meanwhile, is consistent with China’s accelerated development of AI infrastructure, which reduces both psychological and usage-related barriers (Zhao & Watterston, 2021). These trends imply complementary orientations: Thai teachers provide culturally entrenched pedagogical structure, while Chinese teachers demonstrate higher degrees of technological readiness. Combining these orientations can offer an equalized path to innovation in Sino–Thai language pedagogy.
Post-hoc analysis reveals that there exists a multilevel generational logic to pedagogy- and AI-associated orientations. Generation Y (Mean Difference = 0.42, p = 0.002) and Generation Z (Mean Difference = 0.38, p = 0.004) exhibited significantly greater levels in AI-augmented teaching than Boomers, with no difference between Y and Z. Both Y and Z also exhibited greater levels in Perceived Usefulness and Perceived Ease of Use, while Generation X filled an in-between role without showing distinguishable contrasts with the other cohorts. Conversely, Boomers registered higher levels in Language Pedagogy Beliefs and Classroom Management, indicating stronger agreement with formalized and traditional pedagogy modes. The pattern is consistent with accepted technology acceptance models stressing the pivotal predicting role of usefulness and ease of use for behavioral intention (Davis, 1989; Venkatesh & Davis, 2000) as well as generational theory, whereby technological orientation constitutes cohort-based habitus as opposed to age effects (Costanza & Finkelstein, 2015; Lyons & Kuron, 2014). Qualitative interviews validated the pattern: Boomers often referenced “teaching as structure” and “chalk over cloud,” while the younger generations mentioned AI as a “co-worker, not master” and emphasized careful yet active integration. Rather than a simplistic generational gap, the gap reflects two mutually complementary habitus: older generations underpin teaching in institutional order and orderly pedagogic rituals, while the younger generations integrate it into dynamic digital ecologies with heightened receptiveness to AI. The gap provides a strategic basis for faculty development, cross-cohort cooperation can take advantage of the pedagogic capital of the Boomers and the techno agility of the Gen Y/Z generations in order to build adaptive and resilient teaching ecologies (Zhao & Watterston, 2021).
The triangulated outcomes imply that multigenerational faculty development in Asian higher education needs to value strategic complementarity over homogeneous intervention. Quantitative findings reveal differentiated strengths among cohorts, while qualitative narratives shed light on how these orientations are practiced. Boomers and Generation X value stable instructional structures and culture-embedded classroom practices, while Generation Y and Generation Z show their heightened receptiveness to AI and adaptive pedagogical practices. These findings confirm the conclusions of Hyden and Cherrstrom (2025) that the older generations (Gen X and the Baby Boomers) are favorable toward sequential, structured, discussion-based learning styles and having lower technology confidence, and the younger generations (Generation Z and the Millennials) show higher digital competency and disposition toward interactive, gamification learning environments, show a distinct generational digital divide. The divergent value functions as complementary habitus, pedagogical stability vs. technological flexibility, providing the basis for capacity-building plans that leverage, as opposed to diluting, generational variation. In place of generic top-down training, an evidence-based strategy needs to incorporate cross-generational mentorship, shared co-teaching laboratory settings, as well as hybrid pedagogical design centers to create structured yet adaptable teaching environments. Through deliberate design of intergenerational knowledge bridges, institutions can create mutual learning across cohorts, balancing cultural-pedagogical capital with digital literacy, toward sustainable innovation in Sino–Thai higher education.
Theoretically, this research contributes to the existing body of work based on the Technology Acceptance Model by placing the adoption of technology within the frameworks of generation and culture, illustrating that technology readiness is more than just an individual concept but rather a cohort concept that can be applied in a pedagogically directed manner based upon shared historical experience. In terms of implications, this research provides evidence-driven insights grounded in the realities of multigenerational technology adoption, contributing toward the creation of informed pedagogical strategies for developing generational support programs based upon the intersection of existing pedagogical capital with necessary digital support. In terms of policy, the implications of this research include the establishment of a positive model toward the transformation of Asia’s rapidly digitalizing higher educational systems, based upon the reframing of generational difference as positive assets rather than as obstacles. Taken together, these findings collectively highlight how intergenerational and cross-national dynamics intersect to shape pedagogical and technological orientations in Asian higher education, laying the groundwork for the following conclusion.
Research Limitations
Although the current study has made certain contributions, there are still some limitations associated with this research, which need to be acknowledged. First, the empirical data were collected from a single higher education institution, Mae Fah Luang University (MFU) in Thailand, which may constrain the generalizability of the findings. Although such a context offers a distinctive and data-rich “hybrid” environment where Thai and Chinese pedagogical cultures converge, it is essential to treat the results obtained in the current study as context-specific rather than universal. Following the principles of mixed methods research approaches, the study aims at achieving analytical generalization that allows for theoretical transferability rather than statistical generalization. However, it is important to note that differences in institutional goals, investments in AI infrastructure, and faculty development programs can generate distinct interactions of generation and culture.
Second, the organizational structure of MFU, which involves a mixed staff of local Thai lecturers and visiting Chinese lecturers, may not fully reflect the complexity of higher education settings across Asia. In that respect, the “national” and “generational” gradients found here should be interpreted as context-bound patterns rather than universally applicable models.
Third, despite incorporating data from both quantitative and qualitative sources, the sample size remains relatively modest, especially in the qualitative phase. Although data saturation was achieved, a larger and more diverse sample pool could have strengthened the robustness of the findings.
Finally, the current study primarily focuses on educators’ perceptions regarding the adoption of artificial intelligence rather than direct observation of AI implementation in the classroom. Future research may involve integrating classroom-based data into the analysis, as well as employing longitudinal and comparative research designs.
All in all, these limitations call for cautious interpretation of the findings while also highlighting important directions for future research on multigenerational and cross-cultural dynamics in higher education.
Conclusion
This research presents a combined explanation of how intergenerational and cross-national forces shape pedagogical orientations and technology integration in Sino–Thai higher education. Two overlapping gradients are identified as outcomes: a national gradient, where Thai teachers demonstrate pedagogically embedded culture, and a generational gradient, where younger generations (Generation Y and Z) show heightened adaptability to technology. Importantly, generational trends transcend national borders, where Boomers and Generation X center instruction in rigid classroom routines, and Generation Y acts as a connecting group bridging cultural stability and technological innovation. These findings add to current models by integrating the Technology Acceptance Model, cultural context theory, and generational theory, providing a nuanced account of transitional educational faculty behavior. In particular, the present research makes a theoretical contribution as it reconceptualizes the phenomenon of technology acceptance as being both cohort-informed and culture-mediated, as opposed to solely an individualistic cognitive response. The proposed dual-gradient perspective expands on current frameworks as it explicates the interaction between pedagogical and technological orientations in non-Western higher education settings.
Demographic Profile of Chinese Language Instructor Participants
Exploratory Factor Analysis (EFA) Results
Group Differences by Nationality (Independent Samples t-Test Results)
Generational Differences (Welch’s ANOVA Results)
Post-hoc Games–Howell Test Results for Generational Differences
Thematic Analysis Results From NVivo 14 (n = 12)
Note. Frequency reflects relative salience: High = ≥ 4 participants; Mid = 2–3; Low = 1. Multiple references per participant were possible.
Footnotes
Acknowledgements
The authors acknowledge the use of AI-assisted language tools, such as ChatGPT and Grammarly, to improve grammar accuracy and refine minor aspects of academic expression during the manuscript editing process. All research design, theoretical development, data collection, data analysis, interpretations, and final decisions were solely made by the authors. No AI tools were used for generating research ideas, analyzing data, or drawing conclusions.
Ethical Considerations
This study followed the ethical research guidelines of Thailand, including the Thailand National Guidelines for Ethics in Research Involving Humans, and was conducted in accordance with ethical research practices. The study did not require formal approval from an Institutional Review Board (IRB) as it involved non-invasive questionnaire surveys and did not collect sensitive personal information. All participants provided informed consent prior to participation.
Consent to Participate
Written informed consent was obtained from all participants prior to their involvement in the study. Participants signed informed consent regarding the publication of their anonymized data.
Author contribution
Y.Y. developed the theoretical and methodological framework; conceived and designed the study; developed the research instruments; conducted data analysis and interpretation; prepared the draft manuscript; reviewed the results; and approved the final version of the manuscript.
Q.L. recruited participants; collected data; proofread the manuscript; provided constructive feedback; reviewed the results; and approved the final version of the manuscript.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Due to privacy concerns and institutional restrictions, the raw data cannot be publicly shared.