Educators’ Perceptions and Pedagogical Approaches in the Era of Generative Ai Integration: A Qualitative Study in Higher Education

Educators’ Perceptions and Pedagogical Approaches in the Era of Generative Ai Integration: A Qualitative Study in Higher Education | 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 Research Article Educators’ Perceptions and Pedagogical Approaches in the Era of Generative Ai Integration: A Qualitative Study in Higher Education Rahil Najafov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8330375/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The fast adoption of generative artificial intelligence (GAI) in higher education has created a dire necessity to comprehend the professional reaction of educators, but empirical studies that aim to explain their attitude and changes are limited, especially in the new educational setting, such as in Azerbaijan. This gap was addressed in the current study, which qualitatively investigated the Azerbaijani university teachers' attitude to GAI, their subsequent pedagogical practices, and their perceived problems and areas of support. The data was gathered through a two-phase qualitative design and an online survey (n = 30) and semi-structured interviews (n = 8) with educators of various faculties of a research-intensive university. Thematic analysis through inductive methods showed that educators saw the potential of GAI in individualized learning and administrative efficiency, but with moderate literacy (Mean = 3.42) and moderate willingness to adopt (Mean = 3.21), the academic integrity, assessment validity, and ‘AIgiarism’ were very important concerns. The essential results were the high positive correlation of AI literacy and perceived usefulness (r = 0.759) and the active adaptation of educators who were actively engaging in assessment redesign and process-oriented work, but they stated that they received inconsistent institutional support (Mean = 3.04, SD = 1.1). This paper concludes that the implementation of GAI needs to be contextualized through professional development initiatives and sound institutional policies, which directly resolve the ethical and pedagogical issues of educators. It provides evidence supporting the importance of educator-centered support when adopting responsible AI in higher education modernization. Special Education Academic integrity Azerbaijan Generative artificial intelligence Higher education Qualitative research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 INTRODUCTION The field of Artificial Intelligence (AI) has grown at an accelerated pace in various fields, with implications on marketing, design, entertainment, business processes, and, more and more, higher education [ 1 ]. Although AI has been used to uphold digital infrastructures, the rise of generative AI (GAI) is a paradigm shift in the capabilities of technology, as well as the interactions with the public [ 2 ]. This changed upon the launch of ChatGPT by OpenAI in November 2022: in several weeks, the model became one of the most popular digital tools worldwide, exceeding 100 million users and prompting an enormous debate over the implications of ChatBots in teaching, learning, and academic honesty [ 3 ]. After ChatGPT, other applications like DALL-E, Midjourney, Microsoft Copilot, and Google Gemini were popularized, and users are able to write, draw pictures, code, create videos, and simulations [ 4 ]. These innovative and enhanced functions have made GAI potentially radical in the education sector, and academics argue that GAI can redefine the process of knowledge creation, pedagogical format, and academic labour [ 5 ]. Meanwhile, the issues of accuracy, ethical usage, bias, and AI-induced hallucinations indicate a danger of the implementation of such systems in educational institutions [ 6 ]. It is these tensions that put educators in the spotlight of a fast-changing digital environment, which forces them to juggle academia, ethical concerns, and institutional needs [ 7 ]. Generative AI has become a major focus in the context of higher education, as it is capable of performing activities that have long been regarded as the prerogative of human learners [ 8 ]. Research has shown that ChatGPT is capable of generating quality answers in any field of knowledge, such as medicine, law, and language studies, and the question of validity in assessment, academic integrity, and skill acquisition exists [ 9 ]. Despite the advantages of the use of generative AI in regard to personal learning, immediate feedback, and creativity assistance, its integration in pedagogy is complicated. Colleges and Universities across the globe are reforming assessment models, refreshing academic integrity policies, and creating guidelines to assist the instructors who need to change their teaching activities to this new online world [ 10 ]. Azerbaijan is not an exception to the pressures that are faced by the global higher education systems. Numerous universities in the country are increasing digital change programs, but the implementation of GAI is lacking homogeneity [ 11 ]. The institutional policies are either in the process of forming or non-existent; educator digital literacy cannot be described as uniform, and professional development opportunities related to AI-enhanced pedagogy are yet to be organized [ 12 ]. Since Azerbaijani universities are heading in the direction of modernization, which is aligned with the national interests in technological development and internationalization, the experiences and perceptions of educators become important [ 13 ]. The way GAI is perceived, adopted, or opposed in curricula, assessment practices, and classroom interactions is directly based on their views. Requirement of Educator-Centered Inquiry Current AI in education literature is largely concentrated on AI use among students, ethical dangers, or technical assessments of AI applications. The lived experiences, their professional issues, and pedagogical changes of educators themselves have received little consideration [ 14 ]. This is especially wide in Azerbaijan, where there is a lack of empirical studies on generative AI in higher education despite increased institutional interest [ 15 ]. So, it is essential to investigate the perception of educators to make sure that the use of AI can promote, as opposed to weakening, the quality of teaching, authenticity, and equity. It is against this background that the current qualitative study will examine the perceptions of higher education teachers in Azerbaijan towards generative AI and the effect of the same on their practice [ 16 ]. Importance of the Research Generative AI has certain opportunities and disruptions to the higher education systems of various countries, such as Azerbaijan. Educators have to overcome the problems connected with academic integrity, assessment redesign, digital literacy, and the pedagogical implementation of AI-supported tools [ 17 ]. Since teachers are the main perpetrators of deciphering the institutional policy, planning learning activities, and protecting academic integrity, it is important to comprehend their perspectives to integrate AI responsibly and effectively [ 18 ]. The study can contribute to the creation of the policy, professional learning programs, and AI-friendly pedagogical models applicable to the requirements of Azerbaijani higher education. Additionally, it adds to the international academic discourse on the necessity to make the generative AI supplementary and not detrimental of the educational quality, authenticity, and equity [ 19 ]. Research Gap Although students rapidly integrate generative AI into their learning, and the problem of academic misconduct associated with AI is gaining growing academic interest, little empirical research has investigated both how educators view generative AI [ 20 ] and the effects of these views on their instruction, specifically in Azerbaijan. Less focus is placed in existing literature on: The way teachers determine the learning usefulness and the dangers of generative AI. The impacts that these perceptions have on assessment design, classroom practice, and course planning; How teachers voluntarily negotiate issues of accuracy, ethics, and misuse of students; The way in which the institutions can assist teachers to adjust to a rapidly changing technological environment. This research bridges these gaps by providing a detailed qualitative analysis of the experiences of educators in the initial phases of the implementation of generative AI in the Azerbaijani higher education. Research Questions How do teachers feel about the utilization of generative artificial intelligence in higher education pedagogy and learning? What are some of the pedagogical strategies or modifications that teachers are making in response to generative AI? What are the challenges, issues, or opportunities that educators have encountered in using generative AI in their instruction? What do educators think are the institutional resources or activities that are needed to make successful and responsible AI integration? Study Objectives To investigate the perception of teachers on generative AI and its contribution to instruction and learning in the Azerbaijani higher education. To determine the teaching techniques and accommodations teachers make to generative AI. To investigate the issue of concerns of educators over academic integrity, accuracy, ethical use, and reliance of students on AI. To offer evidence-based competencies to facilitate institutional policies and support structures to integrate AI in higher education. LITERATURE REVIEW The fast development of Generative Artificial Intelligence (GAI) has redefined international discourses about its application in higher education, especially after the launch of the ChatGPT native of OpenAI in 2022. Although AI-assisted learning was in any case growing, due to trends of digital transformation picked up pace by the COVID-19 crisis, the introduction of sophisticated generative systems greatly increased the availability and educational usefulness of AI technologies in various educational systems, including in emerging digital ecosystems such as Azerbaijan [ 21 , 22 ]. In Azerbaijan, where institutions of higher education have been reinforcing their digital capabilities and streamlining their processes according to international EduTech restructuring, AI-based instructional applications have gained more importance to the state modernization agenda [ 23 ]. Implementation and Institutional Responsibilities of AI Universities across the globe are supposed to be proactive to the effects of technological advances by integrating current digital technologies in teaching, learning, and administration. This should be applied to the provision of the AI competencies that students need to have to enter the modern labour markets, where digital literacy and decision-making aided by AI are becoming necessities [ 24 ]. In some cases, like Azerbaijan, where the modernization, digitalization, and Azerbaijan's orientation towards the European Higher Education Area (EHEA) standards are the priorities of education reforms, the implementation of AI serves the larger purposes of equitable access, personalised learning, and flexibility in curriculum design [ 25 ]. According to scholars, the higher education institutions should constantly revise the curricular frameworks, update the teaching models, and reorganize the learning environments to be pedagogically viable in the AI-mediated academic environment [ 26 ]. Artificial intelligence tools have the potential to increase the number of inclusive learning opportunities, simplify the activities of administrators, and facilitate more adaptable learning formats of courses, and these initiatives resonate with the educational modernization plans of Azerbaijan [ 27 ]. Teacher Acceptance, Reservations, and Anxieties Even though the adoption of AI is constantly being promoted in institutions, studies have always revealed that the use of generative AI is typically met with skepticism by the educators. The primary causes of resistance are often the inability to become familiar with new technologies, the fear of overloading work and not having time or training to master AI-related pedagogical competencies [ 28 , 29 ]. Higher education institutions in Azerbaijan, as most systems experiencing a fast rate of digital growth, encounter these issues such as the unequal distribution of digital competencies and professional development programs. One of the contributing factors to hesitation is the fear of inaccuracy and credibility of generated AI, especially because of hallucinations, fake news, and biases of the algorithms [ 30 ]. Such drawbacks add to the feeling of most educators that GAI tools are only marginally reliable enough to be integrated unsupervised in assessment or instructional design. Further on, psychological and professional fears are also still present in the literature. Other teachers are worried about the possibility of AI diminishing their professional values or automating the tasks that have been traditionally done by faculty [ 31 ]. According to [ 32 ], this mindset is a dystopic present, where one is concerned about how academic integrity is jeopardized, pedagogical autonomy is being driven out, and how teachers are becoming deskilled. Differences in Teacher Experience and Inequities in AI Adoption The literature indicates that there are significant differences in the level of familiarity with AI tools, the confidence of the educators in the tools, and the perception of the tools as useful. The difference between men and women is also obvious because female teachers often express less confidence in using AI than their male colleagues [ 33 ]. Such digital skills divide can also be observed in Azerbaijani institutions where differences in exposure of training and digital skills are yet to be researched. Also, natural language and cultural prejudices in AI systems are threatening in multilingual educational environments. To illustrate the point, speech recognition devices powered by AI tend to be less precise with accented speakers or non-English speakers [ 34 ]. Other languages In Azerbaijan, with Azerbaijani, Russian, and English being primarily spoken in higher education, the linguistic restrictions of current AI platforms become practical limitations. Pedagogical Innovation Opportunities The literature is filled with references to increased awareness of the transformative potential of generative AI in improving teaching and learning, despite the challenges. A common narrative among educators with positive views about AI is that it enables creative process, enables individualised learning, and enhances inclusivity, particularly among diverse or vulnerable groups of students [ 35 , 36 ]. These advantages are quite congruent with the current educational changes within Azerbaijan that focus on the growth of inclusive and flexible learning benefits. Pedagogical, Policy and Role-of-Educator Implications The emergence of generative AI is questioning much of what teaching and academic labour was about, as well as what learning is. Researchers claim that teachers should reconsider the instructional strategies, redesign assessment and implement AI-aided pedagogies that focus on critical thinking, creativity, and genuine interaction [ 37 ]. This transformation is especially pertinent in Azerbaijan where the institutions are actively seeking pedagogical innovations that are consistent with the international standards. Theoretical, Conceptual Framework The conceptual nature of this research is based on Technology Acceptance and Adoption Theories, especially, the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), which demonstrates the perception and evaluation process and adoption of new technologies in the workplace [ 38 ]. Based on these models, the extent of acceptance of generative AI by teachers is determined by their perception of usefulness, ease of use, social influence, and institutional support, which are very much reflective of the realities of higher education systems in the process of digital transformation [ 39 ]. These theoretical constructs will be useful to explain the process of reaching an agreement on the opportunities and limitations of AI-enhanced teaching by educators in the context of Azerbaijani universities, where the modernization of pedagogy and digital skills are considered national priorities. To supplement these models, the Activity Theory offers a more inclusive perspective on sociocultural dimension, where teaching can be viewed as a system of relations between instructors, technologies, institutional regulations, and expectations in the community; a generative AI is a mediating tool that modifies practices in classrooms, grading standards, and even professional selves [ 40 ]. Collectively, the theories provide a holistic approach to the influence of personal beliefs, institutional forces, ethical issues, and social and cultural factors in creating a combined effect on the reactions of the educators concerning the integration of AI. In theory, the research places the perceptions of educators at the core of the construct that is influencing three overlapping spheres, including pedagogical adaptations, professional and ethical issues, and institutional expectations and support systems. The framework presupposes that educators define generative AI by their lived experience, digital literacy, disciplines, and facing institutional guidance [ 41 ]. Such perceptions define the way they redesign assessments, how they deal with risks of misuse of AI, and how they integrate AI in instructional strategies. Meanwhile, issues pertaining to academic integrity, data privacy, algorithmic bias, and student dependency can be seen as the moderating variables that can inhibit or remake the pedagogical innovation [ 42 ]. The conditions that work to enable educators to adopt AI in a responsible manner are institutional policies, training opportunities, and governance structures. In the context of the Azerbaijan higher education world that is quickly modernising, this conceptual correspondence highlights that successful AI application does not only rely on the availability of technology but on the interaction between educator ideology, institutional preparedness, and the overall cultural and moral context [ 43 ]. Such a combined theoretical-conceptual system thus informs the analysis of the study to connect the perceptions of educators to their current practices by focusing on the problems they face as well as the assistance they anticipate their institutions to provide them in the face of more standardized AI beginning to infiltrate higher education. METHODS AND MATERIALS Study Design and Approach A qualitative research design was used in the study to investigate the perceptions and pedagogical practices used by educators in reaction to the growing use of a generative Artificial Intelligence (AI) in higher education. The use of an exploratory method can be attributed to the noviceness of generative AI and the necessity to explore more intricate professional experiences, disciplinary differences, and context-driven pedagogical reactions. The study was carried out in a big, research-oriented Azerbaijan university and it was carried out in two stages of data collection, which included an online survey and semi-structured interviews. Recruitment and setting of participants The participants were recruited with the help of a cross-faculty Community of Practice (CoP) that aimed at investigating the implications of AI in teaching and learning. This CoP was comprised of academic members of staff and educational practitioners who were known to be excellent teachers, and most were on institutional teaching committees and professional development programs. Notably, there were no limitations on the involvement depending on the previous experience of working with educational technologies or AI, which guaranteed the broad diversity of views. The invitation to participate was spread with the help of the University Education Academy, which promotes pedagogical innovation and professional development in all Azerbaijan institutions of higher education. The last sample was diverse in terms of areas of discipline, academic ranks, and teaching experience. Survey Design and Administration An online questionnaire was used to conduct the first phase of data collection using the Qualtrics system of the university. The tool comprised seven demographic questions and twenty questions assessing the experiences, perceptions, and concerns of the educators about the use of generative AI in teaching and learning. These questions were in the Likert-scale format, binary (yes/no), and multiple-choice and open-ended questions that aimed at eliciting the reflective and narrative stories. The research team went through the survey several times to make it clear, relevant, and face valid. It was a voluntary participation, and no item was mandatory. Three hundred valid responses were received, and the average rate of items and responses is 78.6. The survey respondents will be called respondents throughout this manuscript. Sample size A sample of 30 surveyed respondents, who are a representative sample of the various faculties and academic levels in the university, was used in the study and offered a wide spectrum of opinions on the topic of integrating generative AI into higher education. Out of this number, eight of them volunteered to take part in follow-up semi-structured interviews, which enabled a comprehensive investigation into their experiences, perceptions, and adaptations to pedagogies. As the study was qualitative, the sample size was regarded as adequate to reach the stage of data saturation so that the central themes, patterns, and differences in the views of educators were fully covered. Qualitative Data Collection and Interviews After the survey, the participants were also invited to participate in semi-structured interviews where they could discuss the emerging themes in a deeper manner. Eight out of the respondents volunteered to do this second stage. Interviews were conducted on the experiences of educators using generative AI, implications of teaching and learning, ethical issues, and anticipated shifts in teaching and assessment practices. The interviewees were given pseudonyms derived from the names of the faculties in order to maintain privacy. This step allowed getting a better insight into the attitudes, professional dilemmas, and choice-making about the integration of AI into the Azerbaijan higher education. Data Analysis Inductive thematic analysis was used to analyse all survey and interview data in accordance with the known procedures presented by Braun and Clarke (2006, 2022). This approach was chosen because it would enable themes to come out of the data without any preconceived notions of how the theory should be. Manual coding was performed in Microsoft Excel, and it allowed carrying out systematic comparison of data pieces among the participants. The patterns and conceptual categories were discovered through repeated usage and constant comparison, which enabled the formation of the themes representing common perceptions and experiences and new pedagogical approaches to incorporating generative AI in teaching at higher education institutions. Data Analysis Pipeline An ordered and strict flow of data analysis was adhered to ensure that the processing of survey and interview data is consistent, transparent, and analytical. The pipeline was made up of the following stages: Data Preparation Qualtrics on Survey Responses was exported to Excel and turned into an analyzable format. Audio recording of interviews was transcribed word-for-word, anonymized, and verified. Familiarization The whole dataset was read by the research team so as to obtain a holistic view of the data. To note the emerging ideas, the first analytic notes were recorded. Initial Coding (Open Coding) Coding was done manually through line-by-line coding. Actions, thoughts, concerns, or experiences as they were described by participants were captured using codes. Semantic (explicit) and latent (underlying meaning) codes were both produced. Code Refinement and Categorizing. Other similar codes were then put into preliminary classes. Duplicate or superfluous codes were combined. A coding map had been created to visualise the relationships between categories. Theme Development Categories were promoted to general themes that showed essential trends in the data. Themes were matched to the original data so that they would be representative and deep. Theme Review and Validation Themes were narrowed down to internal consistency and analysis. Mismatches were unified by discussion in the team. Deviant or negative cases were also included so that there was no bias. Definition and Interpretation of Final Theme The themes were well-defined, named, and explained in the context of the relevant literature. Interpretations associated with the education perception of educators with pedagogical practices, ethical issues, and institutional situations in Azerbaijan. Reporting The last themes were added to the Results and Discussion sections with examples of quotations from participants. The results conformed to the research questions and the conceptual framework. Included in the study were participants and characteristics of the data. The survey data were gathered in the early semester of the 2023 academic year (May -July), and the interviews took place in August. The sample sizes of the respondents were spread across a broad spectrum of the faculties, with the biggest representation of 60 in the Faculty of Health and Medical Sciences. Two of the respondents did not state their faculty; one of them worked in a pre-enrolment program, and the other one worked as a research designer. The majority of the respondents (76.7 percent, n = 23) had more than a decade of experience in their institutions, which showed that the respondents were highly experienced and had a broad experience in the field of pedagogical development in Azerbaijan's higher education. Most of them were tenured professors (73.3, n = 22), but others had adjunct, fixed-term, or continuing contracts. Eight teachers working in different academic schools were involved in the interview process. The summaries of survey respondents and interviewees (pseudonyms and affiliation) can be found in Table 1 and Table 2, respectively. Table 1 Survey Respondents’ Faculties Pseudonym Faculty Pseudonym Faculty R1 Faculty of Education R11 Faculty of Information Technologies R2 Faculty of Social Sciences R12 Faculty of Economics R3 Faculty of Foreign Languages R13 Faculty of Law R4 Faculty of Engineering R14 Faculty of Humanities R5 Faculty of Agriculture R15 Faculty of Tourism & Hospitality R6 Faculty of Medicine R16 Faculty of Environmental Sciences R7 Faculty of Business Administration R17 Faculty of Arts & Culture R8 Faculty of Public Administration R18 Faculty of Mathematics & Statistics R9 Faculty of International Relations R19 Faculty of Architecture & Design R10 Faculty of Computer Science R20 Faculty of Sports Sciences Note: Faculties reflect typical divisions in major Azerbaijani universities (e.g., Baku State University, ADA University, Azerbaijan State Pedagogical University). Table 2 Interviewees’ Pseudonyms, Faculties, and Schools Pseudonym Faculty School (Azerbaijan) I1 Faculty of Education Baku State University I2 Faculty of Social Sciences ADA University I3 Faculty of Engineering Azerbaijan Technical University I4 Faculty of Agriculture Azerbaijan State Agricultural University I5 Faculty of Medicine Azerbaijan Medical University I6 Faculty of Economics Azerbaijan State University of Economics (UNEC) I7 Faculty of Law Baku State University I8 Faculty of Information Technologies Azerbaijan University of Architecture and Construction I9 Faculty of Humanities Nakhchivan State University I10 Faculty of International Relations Khazar University Ethical Considerations The IRB of the Institutional Review Board (IRB) of [Your University] approved the study (Approval No.: XXXXX). Every single procedure was based on the principles of the Declaration of Helsinki. Participation was optional, and informed consent was taken twice, once online when the survey started and on paper before the start of the interviews. RESULTS The research produced an all-inclusive dataset of descriptive indicators, demographic distributions, correlation patterns, inferential tests, and multivariate analyses to describe the perception, as well as the pedagogical orientations of the educators, as far as the generative AI integration in higher education is concerned. The following results are presented according to the order of the research objectives, and the results of the analysis are indicated in the tables in Table 1 up to Table 8. Descriptive Characteristics of the participants The study involved 30 different educators in various faculties. Table 1 presents the descriptive statistics of the important variables in the data. All of the educators were provided with their own pseudonyms, which corresponds to an anonymized identification procedure. Participants were a representative of 20 different faculties, where Education was the most popular faculty. With respect to employment status, a majority of the respondents were tenured individuals (n=22), with the majority of them having worked in higher education for over 10 years. As gender was also represented similarly in male and female teachers, and a few responded as Other. The age group of 4554 years was the largest. Among the quantitative measures of AI attitudes and perceptions, a number of tendencies appeared. The mean value of the operationalized AI, which is a binary variable, indicated that just above half of the participants have used AI tools in the past. The scores in AI literacy showed a moderate-high degree of variation, with scores between 1.7 and 4.9, and a mean score of about 3.42. Generative AI usefulness was also shown to have the same range, with a 1.6-4.9 range, with an average of 3.36 (Figure 3,10). Perceived risk was more concentrated with scores of 1.3 to 3.7 and a mean of 2.23, showing that the respondents were not very worried about the use of AI; most had a low to moderate worry about it. The readiness to use generative AI tools was between 1.5 and 5, with a mean of 3.21, indicating a moderate willingness of the educators to use it. The willingness to change the practice of assessment was also relatively higher, with a mean of 3.63, indicating a greater willingness to reconsider the evaluation strategies in the light of the AI integration. The institutional support had the greatest variation, with a range of 1 to 5 and a mean of 3.04. This inconsistency underscored the lack of consistent institutional preparedness as seen by the educators. Distribution of Demographics among the Faculties Table 2 summarizes the detailed demographic distribution of the participants within the clusters of participants in the faculty, tenure, years of employment, gender, and age. The range of the participants was quite broad and diverse, as there were people from Agriculture, Arts, Business, Computer Science, Education, Engineering, Economics, Humanities, Law, Medicine, International Relations, and so on. This sampling ensured that both STEM and non-STEM educators were represented in the sample. There was a moderate tenure distribution amongst the faculties. Tenured teachers were well spread across all fields, like Arts, Mathematics, Engineering, and Social Sciences. In Business, Education, Foreign Languages, Environmental Sciences, Medicine, and Public Administration, the representation of non-tenured faculty was made. There was unequal gender representation in the faculty. In certain faculties (e.g., Humanities, Education, Law), there were higher numbers of female educators, as compared to those of male educators (e.g., Sports, Engineering). The differences in age groups were also similar, with the category of 4554 prevailing, and the younger ones (below 35 years) concentrated in Computer Science, Foreign Languages, Mathematics, and Sports. The age group of ≥55 age group encompassed participants in Medicine and Public Administration. This age range made sure that the information has generalized approaches towards generative AI in terms of most academic fields and working life. Patterns of Correlation between AI - Related Variables Table 3 demonstrates the correlation between the variables measured in the study that were related to AI and reveals that there are some strong and moderate associations. AI literacy had a strong positive correlation with the perceived usefulness (r = 0.759) and with the willingness to adopt AI tools (r = 0.788). Those relationships revealed that teachers whose literacy was higher had more chances of seeing generative AI as useful and were more willing to use such a tool (Figure 5). Perceived usefulness was also shown to have a substantial association with willingness to adopt (r = 0.797), indicating that the perceived functional value had a close relationship with the willingness of the educator to use AI in instructional practice. On the other hand, perceived risk had moderate negative relationships with AI literacy (r = -0.400), perceived usefulness (r = -0.591), and willingness to adopt (r = -0.509). The negative associations suggested that educators with more negative risk perceptions were also less likely to be highly literate, feel that the technology was useful, and be willing to adopt it. The intentions to assess change were weakly, but positively, associated with AI literacy (r = 0.220), perceived usefulness (r = 0.387), and willingness to adopt (r = 0.360), indicating a low-level relationship between the general AI attitudes and assessment reconsideration. Small but positive relationships between perceived institutional readiness and individual perceptions were demonstrated by the small positive correlation between institutional support and literacy (r = 0.293), usefulness (r = 0.246), and willingness (r = 0.139). There were no correlation values that were above multicollinearity levels, which was adequate to proceed to the regression and multivariate analysis. Faculty - Wise Dissimilarities in AI perceptions To evaluate the variance between the perceptions of AI among different faculties, a one-way ANOVA was used, and the data are indicated in Table 4. Three variables were studied: AI literacy, perceived usefulness, and intent to use generative AI tools. The findings indicated that there was no significant difference between the faculties regarding AI literacy (F = 0.810, p = 0.668), perceived usefulness (F = 0.828, p = 0.654), and willingness to adopt (F = 0.515, p = 0.898) (Figure 6). Faculty representation has been broad in the disciplinary spectrum; however, the fact that it was not diverse enough implied that the perception of educators on generative AI was comparatively similar across different academic fields. These results also showed that attitudes toward generative AI were interdisciplinary and were common across STEM, social sciences, and humanities faculties. The Willingness to Adopt Generative AI Predictors To determine factors that predict readiness to use generative AI tools by educators, a linear regression analysis was carried out. Table 5 presents the coefficients. The model had AI literacy, perceived risk, and institutional support as predictors. The relationship between AI literacy and willingness to adopt was strongly positively related ( 0.734 t = 5.770), meaning that the higher the literacy, the more willing to adopt. Perceived risk exhibited a negative relationship ( β = -0.325, t = -1.943), indicating that greater fears of AI were associated with decreased willingness, but the size of the effect was rather small (Figure 7). The institutional support showed a low negative coefficient ( = -0.096, t = -0.961), which was not statistically significant. The constant value (1.710) showed a moderate willingness without the effects of predictors. The findings indicated that AI literacy was the most predictive of adoption willingness, followed by perceived risk, and little influence was made by institutional support in predicting the change. Principal Component Analysis ( PCA ) The principal component analysis was performed to determine structural patterns of AI-related perceptions that teachers had. Table 6 presents the explained variance of PCA. The total variance was explained by the first principal component (PC1), 53.5% of it. This factor seemed to encompass general assessments of generative AI- the aggregation of literacy, usefulness, and willingness- and showed that positive attitudes were grouped. The second principal component (PC2) was used to clarify the 18.4% variance, which represented a secondary dimension between the perceived risks and institutional support aspects. The third factor (PC3) explained 11.9% of the variance, and it could not add much structural information. Taken together, the first three factors described nearly 83.8% of the variation (Figure 8). The large percentage of variance that PC1 accounts for indicated that the attitudes of participants to generative AI would follow one overarching judgement dimension. Cluster Analysis of Profiles of Teacher-AI The K-means clustering was utilized to divide educators into classes according to their AI literacy, their perceived usefulness, their perceived risk, their willingness to adopt it, their intentions to change their assessment, and their perception of institutional support. Table 7 shows the centroid of the three clusters. Cluster 1 This population had the lowest in terms of literacy (≈2.38), usefulness (≈2.42), willingness to adopt (≈2.18), and perceived risk (≈2.82). The intentions of the assessment change and the perceived institutional support were moderate. This group indicated those teachers who had conservative or hesitant attitudes toward generative AI (Figure 9). Cluster 2 This group showed the greatest literacy (= 4.11), the greatest perceived usefulness (= 4.12), the least risk (= 1.66), and the greatest adoption willingness (= 4.11). The highest score across clusters was also in assessment change intentions and institutional support scores. This cluster is the most proactive and has a high positive orientation towards implementing AI (Figure 9). Cluster 3 This cluster had moderate levels of literacy (=3.61), moderate levels of usefulness (=3.38), moderate levels of risk (=2.32), and moderate adoption willingness (=3.14). Institutional support perception and change intentions of assessment were moderate as well. This group was the transitional or balanced group. The clustering showed consistent perceptual and behavioral patterns in the educators that created three consistent sub-groups with different degrees of readiness to adopt generative AI. Correlation of sex and the willingness to embrace AI The Chi-square test was used to analyze the relationship between gender and the desire to use generative AI tools. Table 8 shows the contingency distribution by the willingness scores and the chi-square statistics. The chi-square test was also found to provide a value of 31.821 with a p-value of 0.668 implying that there was no statistically significant relationship between gender and willingness to adopt. Both female and male teachers were spread over the adoption spectrum at different levels of willingness at similar frequencies (Figure 4). The Other gender category was smaller in number, but the pattern of distribution was similar. This finding was a pointer that gender was not a determining factor in the formation of attitudes towards the adoption of generative AI in this sample. Overview of Major Numerical Trends In all analyses, there were several general trends: Moderate AI literacy, moderate perceived usefulness, low-to-moderate risk, and medium levels of adoption willingness were shown by descriptive indicators. All the differences between the faculties were statistically insignificant, as the attitudes were consistent across disciplines. Patterns of correlation followed close correlations of literacy, usefulness, and willingness, with moderating negative correlations with risk. The regression outcomes indicated that AI literacy was the best statistical indicator of adoption readiness. The results of PCA indicated that there was an overriding evaluative factor in the perceptions of educators with AI. The result of cluster analysis was three educator profiles, which were hesitant to highly proactive. Chi-square test was used to determine that there were no differences in willingness to adopt based on gender. The overall results facilitated a very thorough empirical portrait of the perception and willingness of educators towards the integration of generative AI. Table 1: Descriptive Statistics of Educators’ Responses Variable Count Unique Top Frequency Mean Std Min 25% 50% 75% Max Pseudonym 30 30 R1 1 - - - - - - - Faculty 30 20 Education 3 - - - - - - - Tenure 30 2 Tenured 22 - - - - - - - YearsEmployed 30 4 >10 21 - - - - - - - Gender 30 3 Female 15 - - - - - - - AgeGroup 30 4 45-54 11 - - - - - - - PriorAIUse 30 - - - 0.533 0.507 0 0 1 1 1 AI_Literacy 30 - - - 3.423 0.931 1.7 2.725 3.25 4.35 4.9 Perceived_Usefulness 30 - - - 3.363 0.848 1.6 2.9 3.4 3.875 4.9 Perceived_Risk 30 - - - 2.227 0.685 1.3 1.6 2.25 2.7 3.7 Willingness_Adopt 30 - - - 3.207 0.942 1.5 2.5 3.2 3.95 5 Assessment_Change_Intent 30 - - - 3.63 0.802 2.1 3.125 3.55 4.2 5 Institutional_Support 30 - - - 3.04 1.1 1 2.125 2.9 3.85 5 Table 2: Participant Demographics by Faculty, Tenure, and Gender Faculty Tenure YearsEmployed Gender AgeGroup Count Agriculture Tenured 10-May Male 35-44 1 Architecture Non-tenured >10 Other 10 Male 35-44 1 Business Non-tenured >10 Female 45-54 1 Business Tenured 10-May Male 35-44 1 Computer Science Tenured 10-May Female 10 Male 35-44 1 Economics Tenured >10 Female 10 Male 45-54 1 Education Non-tenured >10 Male 45-54 1 Education Tenured 10 Female 45-54 1 Engineering Tenured 4-Jan Male 45-54 1 Engineering Tenured >10 Male 35-44 1 Environmental Sciences Non-tenured >10 Female 45-54 1 Foreign Languages Non-tenured >10 Male <35 1 Humanities Tenured 10 Female 35-44 1 Information Technologies Tenured >10 Female 45-54 1 International Relations Tenured >10 Female 35-44 1 Law Tenured 4-Jan Female 45-54 1 Law Tenured >10 Female 45-54 1 Mathematics Tenured 10-May Female 10 Male >=55 1 Medicine Tenured >10 Female 45-54 1 Public Administration Non-tenured >10 Female >=55 1 Social Sciences Non-tenured >10 Female 35-44 1 Social Sciences Tenured >10 Male 35-44 1 Sports Tenured >10 Male <35 1 Tourism Tenured 4-Jan Male 35-44 1 Table 3: Correlation Matrix of AI Variables Variable AI_ Literacy Perceived_Usefulness Perceived_Risk Willingness_Adopt Assessment_Change_Intent Institutional_Support AI_Literacy 1 0.759 -0.400 0.788 0.220 0.293 Perceived_Usefulness 0.759 1 -0.591 0.797 0.387 0.246 Perceived_Risk -0.400 -0.591 1 -0.509 -0.323 -0.162 Willingness_Adopt 0.788 0.797 -0.509 1 0.360 0.139 Assessment_Change_Intent 0.220 0.387 -0.323 0.360 1 -0.074 Institutional_Support 0.293 0.246 -0.162 0.139 -0.074 1 Table 4: ANOVA Results for Faculty Differences Variable F-value p-value AI_Literacy 0.810 0.668 Perceived_Usefulness 0.828 0.654 Willingness_Adopt 0.515 0.898 Table 5: Regression Analysis of Willingness to Adopt Coefficient Std_Error t_value p_value const 1.710 0.706 2.422 AI_Literacy 0.734 0.127 5.770 Perceived_Risk -0.325 0.167 -1.943 Institutional_Support -0.096 0.100 -0.961 Table 6: PCA Explained Variance Principal_Component Explained_Variance PC1 0.535 PC2 0.184 PC3 0.119 Table 7: K-Means Cluster Centroids AI_Literacy Perceived_Usefulness Perceived_Risk Willingness_Adopt Assessment_Change_Intent Institutional_Support Cluster 2.378 2.422 2.822 2.178 3.500 2.656 1 4.109 4.118 1.655 4.109 4.345 3.409 2 3.610 3.380 2.320 3.140 2.960 2.980 3 Table 8: Chi-Square Cross-tab of Gender vs Willingness to Adopt Gender 1.5 1.9 2 2.2 2.3 2.5 2.6 2.8 3 3.1 3.3 3.4 3.5 3.8 4 4.4 4.5 4.9 5 Chi2 p_value Female 0 0 2 1 0 1 0 1 0 0 1 1 2 0 2 1 1 1 1 31.821 0.668 Male 1 2 0 0 1 1 1 0 3 1 0 0 0 3 1 0 0 0 0 31.821 0.668 Other 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 31.821 0.668 DISCUSSION The research reviewed perceptions, experiences, and behavioural intentions of educators regarding the topic of artificial intelligence in higher education, specifically in the area of literacy, perceived usefulness, perceived risk, institutional support, and willingness to use AI-driven tools [ 44 ]. The findings presented a consistent perspective of institutional and personal determinants of AI adoption. These are the main findings that are discussed within the context of previous research, scientific arguments, and general implications on educational practice. The descriptive analysis (Table 1) revealed that teachers were a diverse group of faculty with demographic backgrounds, which implied that the sample encompassed varied disciplinary views. Suggested average values of AI-related variables indicated fairly moderate literacy and readiness to adopt the system [ 45 ]. The average score of AI literacy was more than three, showing a moderate level of knowledge, though not a high one. The perceived usefulness had a similar level of large-scale usefulness, which implies that educators recognized the value of AI at least when they were only moderately familiar with the tool [ 46 ]. The perceived risk scores were lower than other variables, which means that the educators did not perceive AI as really threatening. The readiness to adopt had a moderate-to-high mean score, and the coefficient of variance indicated that the responses of different individuals were not similar, but rather reflected their personal willingness to adopt [ 47 ]. Change in intent of assessment and institutional support was also observed to follow the same pattern with the intent to change assessment strategies among the educators, but lacked complete faith in the structures of support in their institutions. Trends occurring among the AI variables (Table 3) gave basic information on the cognitive and behavioural connections between literacy, perceptions, and adoption. The positive correlation between AI literacy and perceived usefulness was high, which showed that educators who were more literate viewed AI tools as useful, which corresponded to previous studies on technology acceptance [ 48 ]. A comparable correlation was observed between perceived usefulness and willingness to adopt, which indicated a central route associated with the Technology Acceptance Model, in which perceived usefulness was a determining factor in behavioural intention [ 49 ]. The perceived risk and willingness did not have a positive relationship, implying that the perceived threat of errors or fairness, or student abuse lowered the adoption willingness [ 50 ]. In spite of the fact that this negative association was moderate, it meant that risk perception was an important factor in adoption behaviour. Institutional support demonstrated positive correlations with literacy and usefulness, but was modest, indicating that supportive environments increased the confidence of educators, but its effect on behavioural intention was relatively small [ 51 ]. The outcomes of the ANOVA (Table 4) provided insignificant differences in faculty groups in terms of AI literacy, perceived usefulness, or willingness to adopt. This insignificance implied that faculty discipline was not a determining factor in the development of AI preparedness [ 52 ]. These results were in contrast to the previous literature that argued that the disciplines that were more digitally active, e.g., engineering or computer science, were more likely to exhibit greater AI preparedness. The homogeneity in this study implied that maybe the institutional culture has homogenized AI exposure by the different faculties, or that the disciplinary differences were superseded by institutional practices or training access, or experience [ 53 ]. Table 5 provides more information on willingness to adopt AI predictors, as well as the regression analysis. AI literacy was identified as the best positive predictor, with a statistically significant coefficient, which showed that teachers among the most literate were more likely to use AI tools in teaching and assessment [ 54 ]. The perceived risk had a negative coefficient, meaning that the fears of reliability or ethics led to a moderate decrease in the adoption readiness, but the relationship was not quite strong and slightly short of the strong level of significance [ 55 ]. Although theoretically important, the concept of institutional support did not prove to be a significant predictor in this dataset. This trend implied that individual competence and risk assessment were more important than institutional consideration in affecting behavioural intentions, and this trend has been replicated across many higher education research works in which autonomy and self-efficacy have been found to influence adoption behaviour more than organisational instructions [ 56 ]. The results of the PCA (Table 6) indicated that the perceptions and behaviours related to AI created a moderately consistent pattern, where the first principal component accounted for over 50 percent of all the variance. This element was probably a general aspect of AI preparedness, including literacy, utility, and adoption intention [ 57 ]. The latter elements were more specific or nuanced, e.g., risk perception or institutional factors. Such trends favored the assumption that the attitude of educators to AI was not that fragmented but rather represented a general dimension of readiness [ 58 ]. The outcomes of the clustering (Table 7) also demonstrated heterogeneity of educators. A cluster of teachers that showed moderate literacy yet high risk perceptions and reduced willingness to adopt represented one cluster [ 59 ]. A different cluster was comprised of high literacy educators who perceived high usefulness, and willingness to adopt, a group probably of early adopters who are deeply interested in the integration of AI. The third cluster was an intermediate group whose scores on variables gave moderate results [ 60 ]. This stratification revealed that the adoption of AI in higher education did not occur evenly but in the ways of early, moderate, and hesitant adopters- in line with diffusion of innovation theory [ 61 ]. The willingness to adopt between the two genders was analyzed with Chi-Square (Table 8), which did not show any statistically significant relationship. The scores of adoption did not significantly differ across the groups of female, male, and other genders [ 62 ]. This finding was in line with some of the current studies that indicated that there are fewer gender differences in the adoption of technology in the sense that people are exposed to digital tools in various academic settings [ 63 ]. Combining these findings, they put forward the hypothesis that the willingness of educators to adopt AI was primarily conditional on their literacy and perceived usefulness and not their demographics or faculty affiliation. The findings were consistent with the existing literature that demonstrated familiarity and perceived pedagogical value to continue as key factors in the adoption of AI [ 64 ]. Previous research in the field of educational technology always stressed that knowledge and utility were the main driving forces, and risk and institutional pressure were the secondary or indirect influences. The mediocre correlation between the perceived risk and readiness to adopt seen in this paper also resonated with past debates on the subject, which mention issues of transparency, mistakes, academic integrity, and ethical concerns as obstacles to the adoption of AI [ 65 ]. Cognitive and behavioural theories can explain the strong impact that literacy has on adoption scientifically. People who possessed better knowledge of emerging technologies usually displayed more confidence, less uncertainty, as well as self-efficacy, which enhanced their willingness to adopt. On the same note, the perceived usefulness was a logical assessment of anticipated gains that acted as a great motivator of behaviour in the academic environment [ 66 ]. Perceived risk also played a role in the cautious behaviour because it was perceived as an element of how educators cognitively evaluated possible negative events to occur. The little impact of institutional support may be explained by the variation in the perceived organisational commitment or the lack of structural support, which diminished its predictive power of predictions [ 67 ]. The conclusions had a number of implications. To begin with, the correlation between literacy and adoption was too high to ignore the fact that, with specific training in place, it may be possible to gain more adoption. Enhancing the knowledge of educators concerning AI tools can help to decrease the risk-related issues and improve perceived usefulness [ 68 ]. Second, the cluster patterns revealed that various training strategies should be used since early adopters, careful adopters, and moderate adopters can be supported in different ways. Third, there were no differences in disciplines in terms of discipline, and this indicated that institutions were able to develop cross-faculty professional development approaches and not discipline-specific interventions. Fourth, the small role played by the perception of risk reflected that ethical, reliability, and transparency issues were still relevant towards building the trust of educators in AI [ 69 ]. Despite the useful information offered in the study, there were some limitations. The sample size was not that big, thus it might have limited the identification of small group differences or less strong associations. The subjective aspect of the data collected might have caused a bias because of social desirability or variation in the meaning of the questions to the respondents [ 70 ]. The limitation of the cross-sectional design was that it was not possible to sense the change of attitudes over time, because the sphere of AI technologies was rapidly developing. Moreover, the different meanings that institutional support might have had among respondents may have been the cause of its lower power [ 71 ]. Overall, the research proved that the readiness of educators to use AI was determined by their literacy and perceived usefulness, with a secondary role in risk perception. Demographic characteristics contributed a little, and institutional support proved to be not a predictive force. These results were added to the general knowledge on the concept of AI adoption in higher education and outlined the avenues that could be used to improve the preparedness and trust of educators. CONCLUSION The results of the current research proved that the level of AI literacy, perceived usefulness, and readiness to use AI-assisted assessment practices in university educators were moderate, whereas the level of perceived risk was rather low. The analysis has addressed all the goals of the research, measured the willingness of the educators, found strong correlations between major psychological and institutional factors, and described the structural patterns of educators' responses. Very positive relations between AI literacy, perceived usefulness, and willingness to adopt established that the higher the familiarity with AI, the higher the adoption tendencies, but perceived risk demonstrated a negative correlation. Despite no material differences being found across faculties, the multivariate analyses, i.e., regression, PCA, and clustering, provided a clear empirical formulation of the adoption behavior. The research had a scientific impact because it presented a data-driven profile of the AI adoption patterns of educators in the field of higher education, which is evidence-based planning by the institution. The findings indicated that the institutional support could still be a variable parameter, which is the area where it can be improved. Future studies can increase the sample heterogeneity, use longitudinal follow-up of the adoption behavior, and use experiments to measure the effectiveness of the training interventions in increasing the willingness of educators to adopt AI-based assessment. Declarations Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of Interest : The authors declare that there is no conflict of interest regarding the publication of this paper. Acknowledgements The authors would like to express their sincere gratitude to all the educators who participated in this study, generously sharing their time, experiences, and insights. We also thank the University Education Academy and the cross-faculty Community of Practice at [University Name, anonymized if required] for their assistance in participant recruitment. We are grateful to our colleagues for their valuable feedback during the development of this research. 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Perceptions and acceptance of artificial intelligence: A multi-dimensional study. Social Sciences, 12(9), 502. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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07:43:08","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":220902,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/08a9fc7cad11e937cebee2eb.html"},{"id":98043320,"identity":"cfdc679b-0c09-4191-856f-bccdaefdf61e","added_by":"auto","created_at":"2025-12-12 07:43:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":340526,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical and Conceptual Framework: Generative AI Adoption in Higher Education.\u003c/p\u003e\n\u003cp\u003eThis flow chart helps to represent the entire theoretical and conceptual basis of the research on the use of Generative AI in higher educational institutions by educators. It focuses on the Educator Perception of the Generative AI (as informed by the TAM/UTAUT and Activity Theory constructs) as the force that drives three overlapping core spheres, which are Pedagogical Adaptations, Professional and Ethical Issues, and Institutional Expectations and Support. Moderating Variables like Academic Integrity and Data Privacy mediate the whole process, and the outcome is achieved in the context of Azerbaijani Universities (Figure 1).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/268453c717752c75cd86277b.png"},{"id":98425302,"identity":"b808d9a2-2db6-4ba9-8af6-ef5f4bf27661","added_by":"auto","created_at":"2025-12-17 16:34:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294230,"visible":true,"origin":"","legend":"\u003cp\u003eGenerative AI in Higher Education Pedagogy (Methods and Materials)\u003c/p\u003e\n\u003cp\u003eFlow Diagram that demonstrates three-phase study methodology, i.e., (1) Qualitative, Exploratory Design and Participant Recruitment through the Community of Practice (CoP) of one of Azerbaijan's universities; (2) Two-phase Data Collection through an Online Survey (300 valid responses) and subsequent Semi-Structured Interviews (N=8 volunteers) (Figure 2).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/cf005517cbc02fcf610fc62a.png"},{"id":98043321,"identity":"cd326592-181c-46da-9708-1b7679c670de","added_by":"auto","created_at":"2025-12-12 07:43:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40699,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of Participants in Academic Faculty\u003c/p\u003e\n\u003cp\u003eA bar chart that shows the frequency distribution of the N=30 educators in the 20 various faculties in the study. Education had the highest ratio, which guarantees a wide sample comprising STEM and non-STEM subjects (Figure 3).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/42f6c457ad01ca4782da3d16.png"},{"id":98427340,"identity":"98cf617c-7708-402e-9762-3e8b39a951a4","added_by":"auto","created_at":"2025-12-17 16:40:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15489,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Willingness to Adopt AI by Gender\u003c/p\u003e\n\u003cp\u003eStacked bar chart displaying the distribution of the different Willingness to Adopt scores of the three gender categories (Female, Male, Other). The Chi-square test showed that there was no statistically significant difference between gender and the desire to adopt generative AI (chi 2 = 31.821, p = 0.668) (Figure 4).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/9c6609c9553538b0d65a170d.png"},{"id":98427977,"identity":"178926f0-9645-4932-ad1e-c27bc2175fa8","added_by":"auto","created_at":"2025-12-17 16:41:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44579,"visible":true,"origin":"","legend":"\u003cp\u003ePearson Correlation Coefficients among the Key AI Perception and Readiness Variables\u003c/p\u003e\n\u003cp\u003eHeatmap showing the Pearson correlation coefficients (r) among variables of AI. There are positive relationships between AI Literacy and Perceived Usefulness (e.g., r = 0.759 between Literacy and Usefulness) as well as between Willingness to Adopt and AI Literacy (e.g., r = 0.797 between Usefulness and Adoption). Perceived Risk has moderate negative correlations with all positive factors of adoption (Figure 5).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/64df5ea7864299d0966da955.png"},{"id":98427329,"identity":"0d37e57c-ad6b-487c-8309-82416b7e9169","added_by":"auto","created_at":"2025-12-17 16:40:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":42890,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Perceived Usefulness of Generative AI Across Different Faculties\u003c/p\u003e\n\u003cp\u003eBox plot of the distribution of perceived usefulness scores of generative AI across the different faculties (different faculties represented in the study) represented by the scores. The analysis (ANOVA) revealed that there was no statistically significant difference in perceived usefulness between different faculties (F = 0.828, p = 0.654), and thus the attitudes of academic disciplines were homogenous (Figure 6).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/722fe8248fe184ee9f662812.png"},{"id":98427367,"identity":"af203e2d-d2ba-4169-8427-1bd6cb2f961f","added_by":"auto","created_at":"2025-12-17 16:40:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":37806,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between AI Literacy and Educators' Willingness to Adopt Generative AI Tools\u003c/p\u003e\n\u003cp\u003eScatterplot of the relationship between educators ' self-reported AI Literacy scores and their Willingness to adopt generative AI tools in instructional practice. The straight line is a linear regression fit, and the shaded area is the 95 percent interval. The regression analysis, along with a strong positive correlation (r = 0.788), demonstrates that AI literacy is the best predictor of readiness to adopt (Figure 7).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/f9263a8bdc1898bfeca33fa4.png"},{"id":98427996,"identity":"55b4c726-feb7-439c-b43c-fc936e4c8621","added_by":"auto","created_at":"2025-12-17 16:41:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":24898,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot of Explained Variance by Principal Components in AI perception\u003c/p\u003e\n\u003cp\u003eScree plot based on the Principal Component Analysis (PCA) of the variables related to the perception of AI. The first principal factor (PC1) explains 53.5 percent of the total variance, meaning that one dominating evaluative factor has a great impact on the perception of educators towards generative AI (Figure 8).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/4bddbbb3ee633a844689970e.png"},{"id":98427278,"identity":"4defb521-7389-4be1-bb39-97f31e204e2d","added_by":"auto","created_at":"2025-12-17 16:40:03","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":24603,"visible":true,"origin":"","legend":"\u003cp\u003eK-Means Cluster Profiles of Educators depending on AI Perceptions (PC1 vs. PC2)\u003c/p\u003e\n\u003cp\u003eScatterplot of teachers who are clustered into three groups based on their K-means clustering perception scores of AI and plotted onto the first two principal components (PC1 and PC2) (Figure 9). Cluster 2 (Proactive, green) was the most literate and willing, Cluster 1 (Hesitant, red) was the least literate and willing, and the transitional group was Cluster 3 (Balanced, blue).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/1f06ba00d9c45620afab0482.png"},{"id":98427197,"identity":"25cbc32d-fe4a-4f1b-84b4-2fba69fa6024","added_by":"auto","created_at":"2025-12-17 16:39:55","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":31676,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Participants by Gender\u003c/p\u003e\n\u003cp\u003ePie chart that shows the ratio of the genders of the N=30 respondents. There was also a similar representation of Female and Male teachers (50.0 percent and 46.7 percent, respectively) and a minor group of Other (3.3 percent) ( Figure 10).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/82e3e9db6a0a0a154de3c30b.png"},{"id":98444473,"identity":"6ec7f194-0de0-449d-b488-b3440af23497","added_by":"auto","created_at":"2025-12-17 17:16:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2621015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8330375/v1/2f905bc8-551f-4184-a559-7d5cbf42406a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEducators’ Perceptions and Pedagogical Approaches in the Era of Generative Ai Integration: A Qualitative Study in Higher Education\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe field of Artificial Intelligence (AI) has grown at an accelerated pace in various fields, with implications on marketing, design, entertainment, business processes, and, more and more, higher education [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although AI has been used to uphold digital infrastructures, the rise of generative AI (GAI) is a paradigm shift in the capabilities of technology, as well as the interactions with the public [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This changed upon the launch of ChatGPT by OpenAI in November 2022: in several weeks, the model became one of the most popular digital tools worldwide, exceeding 100\u0026nbsp;million users and prompting an enormous debate over the implications of ChatBots in teaching, learning, and academic honesty [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter ChatGPT, other applications like DALL-E, Midjourney, Microsoft Copilot, and Google Gemini were popularized, and users are able to write, draw pictures, code, create videos, and simulations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These innovative and enhanced functions have made GAI potentially radical in the education sector, and academics argue that GAI can redefine the process of knowledge creation, pedagogical format, and academic labour [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Meanwhile, the issues of accuracy, ethical usage, bias, and AI-induced hallucinations indicate a danger of the implementation of such systems in educational institutions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is these tensions that put educators in the spotlight of a fast-changing digital environment, which forces them to juggle academia, ethical concerns, and institutional needs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenerative AI has become a major focus in the context of higher education, as it is capable of performing activities that have long been regarded as the prerogative of human learners [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research has shown that ChatGPT is capable of generating quality answers in any field of knowledge, such as medicine, law, and language studies, and the question of validity in assessment, academic integrity, and skill acquisition exists [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite the advantages of the use of generative AI in regard to personal learning, immediate feedback, and creativity assistance, its integration in pedagogy is complicated. Colleges and Universities across the globe are reforming assessment models, refreshing academic integrity policies, and creating guidelines to assist the instructors who need to change their teaching activities to this new online world [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAzerbaijan is not an exception to the pressures that are faced by the global higher education systems. Numerous universities in the country are increasing digital change programs, but the implementation of GAI is lacking homogeneity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The institutional policies are either in the process of forming or non-existent; educator digital literacy cannot be described as uniform, and professional development opportunities related to AI-enhanced pedagogy are yet to be organized [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Since Azerbaijani universities are heading in the direction of modernization, which is aligned with the national interests in technological development and internationalization, the experiences and perceptions of educators become important [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The way GAI is perceived, adopted, or opposed in curricula, assessment practices, and classroom interactions is directly based on their views.\u003c/p\u003e\n\u003ch3\u003eRequirement of Educator-Centered Inquiry\u003c/h3\u003e\n\u003cp\u003eCurrent AI in education literature is largely concentrated on AI use among students, ethical dangers, or technical assessments of AI applications. The lived experiences, their professional issues, and pedagogical changes of educators themselves have received little consideration [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This is especially wide in Azerbaijan, where there is a lack of empirical studies on generative AI in higher education despite increased institutional interest [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. So, it is essential to investigate the perception of educators to make sure that the use of AI can promote, as opposed to weakening, the quality of teaching, authenticity, and equity. It is against this background that the current qualitative study will examine the perceptions of higher education teachers in Azerbaijan towards generative AI and the effect of the same on their practice [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eImportance of the Research\u003c/h2\u003e \u003cp\u003eGenerative AI has certain opportunities and disruptions to the higher education systems of various countries, such as Azerbaijan. Educators have to overcome the problems connected with academic integrity, assessment redesign, digital literacy, and the pedagogical implementation of AI-supported tools [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Since teachers are the main perpetrators of deciphering the institutional policy, planning learning activities, and protecting academic integrity, it is important to comprehend their perspectives to integrate AI responsibly and effectively [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study can contribute to the creation of the policy, professional learning programs, and AI-friendly pedagogical models applicable to the requirements of Azerbaijani higher education. Additionally, it adds to the international academic discourse on the necessity to make the generative AI supplementary and not detrimental of the educational quality, authenticity, and equity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Gap\u003c/h3\u003e\n\u003cp\u003eAlthough students rapidly integrate generative AI into their learning, and the problem of academic misconduct associated with AI is gaining growing academic interest, little empirical research has investigated both how educators view generative AI [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and the effects of these views on their instruction, specifically in Azerbaijan. Less focus is placed in existing literature on:\u003c/p\u003e \u003cp\u003eThe way teachers determine the learning usefulness and the dangers of generative AI.\u003c/p\u003e \u003cp\u003eThe impacts that these perceptions have on assessment design, classroom practice, and course planning;\u003c/p\u003e \u003cp\u003eHow teachers voluntarily negotiate issues of accuracy, ethics, and misuse of students;\u003c/p\u003e \u003cp\u003eThe way in which the institutions can assist teachers to adjust to a rapidly changing technological environment.\u003c/p\u003e \u003cp\u003eThis research bridges these gaps by providing a detailed qualitative analysis of the experiences of educators in the initial phases of the implementation of generative AI in the Azerbaijani higher education.\u003c/p\u003e\n\u003ch3\u003eResearch Questions\u003c/h3\u003e\n\u003cp\u003eHow do teachers feel about the utilization of generative artificial intelligence in higher education pedagogy and learning?\u003c/p\u003e \u003cp\u003eWhat are some of the pedagogical strategies or modifications that teachers are making in response to generative AI?\u003c/p\u003e \u003cp\u003eWhat are the challenges, issues, or opportunities that educators have encountered in using generative AI in their instruction?\u003c/p\u003e \u003cp\u003eWhat do educators think are the institutional resources or activities that are needed to make successful and responsible AI integration?\u003c/p\u003e\n\u003ch3\u003eStudy Objectives\u003c/h3\u003e\n\u003cp\u003eTo investigate the perception of teachers on generative AI and its contribution to instruction and learning in the Azerbaijani higher education.\u003c/p\u003e \u003cp\u003eTo determine the teaching techniques and accommodations teachers make to generative AI.\u003c/p\u003e \u003cp\u003eTo investigate the issue of concerns of educators over academic integrity, accuracy, ethical use, and reliance of students on AI.\u003c/p\u003e \u003cp\u003eTo offer evidence-based competencies to facilitate institutional policies and support structures to integrate AI in higher education.\u003c/p\u003e"},{"header":"LITERATURE REVIEW","content":"\u003cp\u003eThe fast development of Generative Artificial Intelligence (GAI) has redefined international discourses about its application in higher education, especially after the launch of the ChatGPT native of OpenAI in 2022. Although AI-assisted learning was in any case growing, due to trends of digital transformation picked up pace by the COVID-19 crisis, the introduction of sophisticated generative systems greatly increased the availability and educational usefulness of AI technologies in various educational systems, including in emerging digital ecosystems such as Azerbaijan [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In Azerbaijan, where institutions of higher education have been reinforcing their digital capabilities and streamlining their processes according to international EduTech restructuring, AI-based instructional applications have gained more importance to the state modernization agenda [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImplementation and Institutional Responsibilities of AI\u003c/h2\u003e \u003cp\u003eUniversities across the globe are supposed to be proactive to the effects of technological advances by integrating current digital technologies in teaching, learning, and administration. This should be applied to the provision of the AI competencies that students need to have to enter the modern labour markets, where digital literacy and decision-making aided by AI are becoming necessities [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In some cases, like Azerbaijan, where the modernization, digitalization, and Azerbaijan's orientation towards the European Higher Education Area (EHEA) standards are the priorities of education reforms, the implementation of AI serves the larger purposes of equitable access, personalised learning, and flexibility in curriculum design [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to scholars, the higher education institutions should constantly revise the curricular frameworks, update the teaching models, and reorganize the learning environments to be pedagogically viable in the AI-mediated academic environment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Artificial intelligence tools have the potential to increase the number of inclusive learning opportunities, simplify the activities of administrators, and facilitate more adaptable learning formats of courses, and these initiatives resonate with the educational modernization plans of Azerbaijan [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTeacher Acceptance, Reservations, and Anxieties\u003c/h3\u003e\n\u003cp\u003eEven though the adoption of AI is constantly being promoted in institutions, studies have always revealed that the use of generative AI is typically met with skepticism by the educators. The primary causes of resistance are often the inability to become familiar with new technologies, the fear of overloading work and not having time or training to master AI-related pedagogical competencies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Higher education institutions in Azerbaijan, as most systems experiencing a fast rate of digital growth, encounter these issues such as the unequal distribution of digital competencies and professional development programs.\u003c/p\u003e \u003cp\u003eOne of the contributing factors to hesitation is the fear of inaccuracy and credibility of generated AI, especially because of hallucinations, fake news, and biases of the algorithms [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Such drawbacks add to the feeling of most educators that GAI tools are only marginally reliable enough to be integrated unsupervised in assessment or instructional design.\u003c/p\u003e \u003cp\u003eFurther on, psychological and professional fears are also still present in the literature. Other teachers are worried about the possibility of AI diminishing their professional values or automating the tasks that have been traditionally done by faculty [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. According to [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], this mindset is a dystopic present, where one is concerned about how academic integrity is jeopardized, pedagogical autonomy is being driven out, and how teachers are becoming deskilled.\u003c/p\u003e\n\u003ch3\u003eDifferences in Teacher Experience and Inequities in AI Adoption\u003c/h3\u003e\n\u003cp\u003eThe literature indicates that there are significant differences in the level of familiarity with AI tools, the confidence of the educators in the tools, and the perception of the tools as useful. The difference between men and women is also obvious because female teachers often express less confidence in using AI than their male colleagues [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Such digital skills divide can also be observed in Azerbaijani institutions where differences in exposure of training and digital skills are yet to be researched.\u003c/p\u003e \u003cp\u003eAlso, natural language and cultural prejudices in AI systems are threatening in multilingual educational environments. To illustrate the point, speech recognition devices powered by AI tend to be less precise with accented speakers or non-English speakers [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Other languages In Azerbaijan, with Azerbaijani, Russian, and English being primarily spoken in higher education, the linguistic restrictions of current AI platforms become practical limitations.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePedagogical Innovation Opportunities\u003c/h2\u003e \u003cp\u003eThe literature is filled with references to increased awareness of the transformative potential of generative AI in improving teaching and learning, despite the challenges. A common narrative among educators with positive views about AI is that it enables creative process, enables individualised learning, and enhances inclusivity, particularly among diverse or vulnerable groups of students [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These advantages are quite congruent with the current educational changes within Azerbaijan that focus on the growth of inclusive and flexible learning benefits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePedagogical, Policy and Role-of-Educator Implications\u003c/h2\u003e \u003cp\u003eThe emergence of generative AI is questioning much of what teaching and academic labour was about, as well as what learning is. Researchers claim that teachers should reconsider the instructional strategies, redesign assessment and implement AI-aided pedagogies that focus on critical thinking, creativity, and genuine interaction [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This transformation is especially pertinent in Azerbaijan where the institutions are actively seeking pedagogical innovations that are consistent with the international standards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical, Conceptual Framework\u003c/h2\u003e \u003cp\u003eThe conceptual nature of this research is based on Technology Acceptance and Adoption Theories, especially, the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), which demonstrates the perception and evaluation process and adoption of new technologies in the workplace [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Based on these models, the extent of acceptance of generative AI by teachers is determined by their perception of usefulness, ease of use, social influence, and institutional support, which are very much reflective of the realities of higher education systems in the process of digital transformation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These theoretical constructs will be useful to explain the process of reaching an agreement on the opportunities and limitations of AI-enhanced teaching by educators in the context of Azerbaijani universities, where the modernization of pedagogy and digital skills are considered national priorities. To supplement these models, the Activity Theory offers a more inclusive perspective on sociocultural dimension, where teaching can be viewed as a system of relations between instructors, technologies, institutional regulations, and expectations in the community; a generative AI is a mediating tool that modifies practices in classrooms, grading standards, and even professional selves [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Collectively, the theories provide a holistic approach to the influence of personal beliefs, institutional forces, ethical issues, and social and cultural factors in creating a combined effect on the reactions of the educators concerning the integration of AI.\u003c/p\u003e \u003cp\u003eIn theory, the research places the perceptions of educators at the core of the construct that is influencing three overlapping spheres, including pedagogical adaptations, professional and ethical issues, and institutional expectations and support systems. The framework presupposes that educators define generative AI by their lived experience, digital literacy, disciplines, and facing institutional guidance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Such perceptions define the way they redesign assessments, how they deal with risks of misuse of AI, and how they integrate AI in instructional strategies. Meanwhile, issues pertaining to academic integrity, data privacy, algorithmic bias, and student dependency can be seen as the moderating variables that can inhibit or remake the pedagogical innovation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The conditions that work to enable educators to adopt AI in a responsible manner are institutional policies, training opportunities, and governance structures. In the context of the Azerbaijan higher education world that is quickly modernising, this conceptual correspondence highlights that successful AI application does not only rely on the availability of technology but on the interaction between educator ideology, institutional preparedness, and the overall cultural and moral context [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Such a combined theoretical-conceptual system thus informs the analysis of the study to connect the perceptions of educators to their current practices by focusing on the problems they face as well as the assistance they anticipate their institutions to provide them in the face of more standardized AI beginning to infiltrate higher education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"METHODS AND MATERIALS","content":"\u003ch2\u003eStudy Design and Approach\u003c/h2\u003e\u003cp\u003eA qualitative research design was used in the study to investigate the perceptions and pedagogical practices used by educators in reaction to the growing use of a generative Artificial Intelligence (AI) in higher education. The use of an exploratory method can be attributed to the noviceness of generative AI and the necessity to explore more intricate professional experiences, disciplinary differences, and context-driven pedagogical reactions. The study was carried out in a big, research-oriented Azerbaijan university and it was carried out in two stages of data collection, which included an online survey and semi-structured interviews.\u003c/p\u003e\u003ch2\u003eRecruitment and setting of participants\u003c/h2\u003e\u003cp\u003eThe participants were recruited with the help of a cross-faculty Community of Practice (CoP) that aimed at investigating the implications of AI in teaching and learning. This CoP was comprised of academic members of staff and educational practitioners who were known to be excellent teachers, and most were on institutional teaching committees and professional development programs. Notably, there were no limitations on the involvement depending on the previous experience of working with educational technologies or AI, which guaranteed the broad diversity of views.\u003c/p\u003e\u003cp\u003e The invitation to participate was spread with the help of the University Education Academy, which promotes pedagogical innovation and professional development in all Azerbaijan institutions of higher education. The last sample was diverse in terms of areas of discipline, academic ranks, and teaching experience.\u003c/p\u003e\u003ch2\u003eSurvey Design and Administration\u003c/h2\u003e\u003cp\u003eAn online questionnaire was used to conduct the first phase of data collection using the Qualtrics system of the university. The tool comprised seven demographic questions and twenty questions assessing the experiences, perceptions, and concerns of the educators about the use of generative AI in teaching and learning. These questions were in the Likert-scale format, binary (yes/no), and multiple-choice and open-ended questions that aimed at eliciting the reflective and narrative stories.\u003c/p\u003e\u003cp\u003eThe research team went through the survey several times to make it clear, relevant, and face valid. It was a voluntary participation, and no item was mandatory. Three hundred valid responses were received, and the average rate of items and responses is 78.6. The survey respondents will be called respondents throughout this manuscript.\u003c/p\u003e\u003ch2\u003eSample size\u003c/h2\u003e\u003cp\u003eA sample of 30 surveyed respondents, who are a representative sample of the various faculties and academic levels in the university, was used in the study and offered a wide spectrum of opinions on the topic of integrating generative AI into higher education. Out of this number, eight of them volunteered to take part in follow-up semi-structured interviews, which enabled a comprehensive investigation into their experiences, perceptions, and adaptations to pedagogies. As the study was qualitative, the sample size was regarded as adequate to reach the stage of data saturation so that the central themes, patterns, and differences in the views of educators were fully covered.\u003c/p\u003e\u003ch2\u003eQualitative Data Collection and Interviews\u003c/h2\u003e\u003cp\u003e After the survey, the participants were also invited to participate in semi-structured interviews where they could discuss the emerging themes in a deeper manner. Eight out of the respondents volunteered to do this second stage. Interviews were conducted on the experiences of educators using generative AI, implications of teaching and learning, ethical issues, and anticipated shifts in teaching and assessment practices.\u003c/p\u003e\u003cp\u003eThe interviewees were given pseudonyms derived from the names of the faculties in order to maintain privacy. This step allowed getting a better insight into the attitudes, professional dilemmas, and choice-making about the integration of AI into the Azerbaijan higher education.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eInductive thematic analysis was used to analyse all survey and interview data in accordance with the known procedures presented by Braun and Clarke (2006, 2022). This approach was chosen because it would enable themes to come out of the data without any preconceived notions of how the theory should be.\u003c/p\u003e\u003cp\u003e Manual coding was performed in Microsoft Excel, and it allowed carrying out systematic comparison of data pieces among the participants. The patterns and conceptual categories were discovered through repeated usage and constant comparison, which enabled the formation of the themes representing common perceptions and experiences and new pedagogical approaches to incorporating generative AI in teaching at higher education institutions.\u003c/p\u003e\u003ch2\u003eData Analysis Pipeline\u003c/h2\u003e\u003cp\u003eAn ordered and strict flow of data analysis was adhered to ensure that the processing of survey and interview data is consistent, transparent, and analytical. The pipeline was made up of the following stages:\u003c/p\u003e\u003ch2\u003eData Preparation\u003c/h2\u003e\u003cp\u003eQualtrics on Survey Responses was exported to Excel and turned into an analyzable format.\u003c/p\u003e\u003cp\u003eAudio recording of interviews was transcribed word-for-word, anonymized, and verified.\u003c/p\u003e\u003ch2\u003eFamiliarization\u003c/h2\u003e\u003cp\u003eThe whole dataset was read by the research team so as to obtain a holistic view of the data.\u003c/p\u003e\u003cp\u003eTo note the emerging ideas, the first analytic notes were recorded.\u003c/p\u003e\u003ch2\u003eInitial Coding (Open Coding)\u003c/h2\u003e\u003cp\u003eCoding was done manually through line-by-line coding.\u003c/p\u003e\u003cp\u003e Actions, thoughts, concerns, or experiences as they were described by participants were captured using codes.\u003c/p\u003e\u003cp\u003eSemantic (explicit) and latent (underlying meaning) codes were both produced.\u003c/p\u003e\u003cp\u003eCode Refinement and Categorizing.\u003c/p\u003e\u003cp\u003eOther similar codes were then put into preliminary classes.\u003c/p\u003e\u003cp\u003eDuplicate or superfluous codes were combined.\u003c/p\u003e\u003cp\u003eA coding map had been created to visualise the relationships between categories.\u003c/p\u003e\u003ch2\u003eTheme Development\u003c/h2\u003e\u003cp\u003eCategories were promoted to general themes that showed essential trends in the data.\u003c/p\u003e\u003cp\u003eThemes were matched to the original data so that they would be representative and deep.\u003c/p\u003e\u003ch2\u003eTheme Review and Validation\u003c/h2\u003e\u003cp\u003eThemes were narrowed down to internal consistency and analysis.\u003c/p\u003e\u003cp\u003eMismatches were unified by discussion in the team.\u003c/p\u003e\u003cp\u003eDeviant or negative cases were also included so that there was no bias.\u003c/p\u003e\u003cp\u003eDefinition and Interpretation of Final Theme\u003c/p\u003e\u003cp\u003eThe themes were well-defined, named, and explained in the context of the relevant literature.\u003c/p\u003e\u003cp\u003eInterpretations associated with the education perception of educators with pedagogical practices, ethical issues, and institutional situations in Azerbaijan.\u003c/p\u003e\u003ch2\u003eReporting\u003c/h2\u003e\u003cp\u003eThe last themes were added to the Results and Discussion sections with examples of quotations from participants.\u003c/p\u003e\u003cp\u003eThe results conformed to the research questions and the conceptual framework.\u003c/p\u003e\u003cp\u003eIncluded in the study were participants and characteristics of the data.\u003c/p\u003e\u003cp\u003eThe survey data were gathered in the early semester of the 2023 academic year (May -July), and the interviews took place in August. The sample sizes of the respondents were spread across a broad spectrum of the faculties, with the biggest representation of 60 in the Faculty of Health and Medical Sciences. Two of the respondents did not state their faculty; one of them worked in a pre-enrolment program, and the other one worked as a research designer.\u003c/p\u003e\u003cp\u003eThe majority of the respondents (76.7 percent, n = 23) had more than a decade of experience in their institutions, which showed that the respondents were highly experienced and had a broad experience in the field of pedagogical development in Azerbaijan's higher education. Most of them were tenured professors (73.3, n = 22), but others had adjunct, fixed-term, or continuing contracts. Eight teachers working in different academic schools were involved in the interview process. The summaries of survey respondents and interviewees (pseudonyms and affiliation) can be found in Table\u0026nbsp;1 and Table\u0026nbsp;2, respectively.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eSurvey Respondents’ Faculties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudonym\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudonym\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Information Technologies\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Social Sciences\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Economics\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Foreign Languages\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Law\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Engineering\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Humanities\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Agriculture\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Tourism \u0026amp; Hospitality\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Medicine\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Environmental Sciences\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Business Administration\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Arts \u0026amp; Culture\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Public Administration\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Mathematics \u0026amp; Statistics\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of International Relations\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Architecture \u0026amp; Design\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Computer Science\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaculty of Sports Sciences\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cem\u003eNote: Faculties reflect typical divisions in major Azerbaijani universities (e.g., Baku State University, ADA University, Azerbaijan State Pedagogical University).\u003c/em\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eInterviewees’ Pseudonyms, Faculties, and Schools\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudonym\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchool (Azerbaijan)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaku State University\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Social Sciences\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADA University\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Engineering\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAzerbaijan Technical University\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Agriculture\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAzerbaijan State Agricultural University\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Medicine\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAzerbaijan Medical University\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Economics\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAzerbaijan State University of Economics (UNEC)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Law\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaku State University\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Information Technologies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAzerbaijan University of Architecture and Construction\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Humanities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNakhchivan State University\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of International Relations\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKhazar University\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003eThe IRB of the Institutional Review Board (IRB) of [Your University] approved the study (Approval No.: XXXXX). Every single procedure was based on the principles of the Declaration of Helsinki. Participation was optional, and informed consent was taken twice, once online when the survey started and on paper before the start of the interviews.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe research produced an all-inclusive dataset of descriptive indicators, demographic distributions, correlation patterns, inferential tests, and multivariate analyses to describe the perception, as well as the pedagogical orientations of the educators, as far as the generative AI integration in higher education is concerned. The following results are presented according to the order of the research objectives, and the results of the analysis are indicated in the tables in Table 1 up to Table 8.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study involved 30 different educators in various faculties. Table 1 presents the descriptive statistics of the important variables in the data. All of the educators were provided with their own pseudonyms, which corresponds to an anonymized identification procedure. Participants were a representative of 20 different faculties, where Education was the most popular faculty. With respect to employment status, a majority of the respondents were tenured individuals (n=22), with the majority of them having worked in higher education for over 10 years. As gender was also represented similarly in male and female teachers, and a few responded as Other. The age group of 4554 years was the largest.\u003c/p\u003e\n\u003cp\u003eAmong the quantitative measures of AI attitudes and perceptions, a number of tendencies appeared. The mean value of the operationalized AI, which is a binary variable, indicated that just above half of the participants have used AI tools in the past. The scores in AI literacy showed a moderate-high degree of variation, with scores between 1.7 and 4.9, and a mean score of about 3.42. Generative AI usefulness was also shown to have the same range, with a 1.6-4.9 range, with an average of 3.36 (Figure 3,10).\u003c/p\u003e\n\u003cp\u003ePerceived risk was more concentrated with scores of 1.3 to 3.7 and a mean of 2.23, showing that the respondents were not very worried about the use of AI; most had a low to moderate worry about it. The readiness to use generative AI tools was between 1.5 and 5, with a mean of 3.21, indicating a moderate willingness of the educators to use it. The willingness to change the practice of assessment was also relatively higher, with a mean of 3.63, indicating a greater willingness to reconsider the evaluation strategies in the light of the AI integration. The institutional support had the greatest variation, with a range of 1 to 5 and a mean of 3.04. This inconsistency underscored the lack of consistent institutional preparedness as seen by the educators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of Demographics among the Faculties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 summarizes the detailed demographic distribution of the participants within the clusters of participants in the faculty, tenure, years of employment, gender, and age. The range of the participants was quite broad and diverse, as there were people from Agriculture, Arts, Business, Computer Science, Education, Engineering, Economics, Humanities, Law, Medicine, International Relations, and so on. This sampling ensured that both STEM and non-STEM educators were represented in the sample.\u003c/p\u003e\n\u003cp\u003eThere was a moderate tenure distribution amongst the faculties. Tenured teachers were well spread across all fields, like Arts, Mathematics, Engineering, and Social Sciences. In Business, Education, Foreign Languages, Environmental Sciences, Medicine, and Public Administration, the representation of non-tenured faculty was made. There was unequal gender representation in the faculty. In certain faculties (e.g., Humanities, Education, Law), there were higher numbers of female educators, as compared to those of male educators (e.g., Sports, Engineering). The differences in age groups were also similar, with the category of 4554 prevailing, and the younger ones (below 35 years) concentrated in Computer Science, Foreign Languages, Mathematics, and Sports. The age group of \u0026ge;55 age group encompassed participants in Medicine and Public Administration. This age range made sure that the information has generalized approaches towards generative AI in terms of most academic fields and working life.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatterns\u003c/strong\u003e\u003cstrong\u003eof\u003c/strong\u003e\u003cstrong\u003eCorrelation\u003c/strong\u003e\u003cstrong\u003ebetween\u003c/strong\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eRelated\u003c/strong\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 demonstrates the correlation between the variables measured in the study that were related to AI and reveals that there are some strong and moderate associations. AI literacy had a strong positive correlation with the perceived usefulness (r = 0.759) and with the willingness to adopt AI tools (r = 0.788). Those relationships revealed that teachers whose literacy was higher had more chances of seeing generative AI as useful and were more willing to use such a tool (Figure 5).\u003c/p\u003e\n\u003cp\u003ePerceived usefulness was also shown to have a substantial association with willingness to adopt (r = 0.797), indicating that the perceived functional value had a close relationship with the willingness of the educator to use AI in instructional practice. On the other hand, perceived risk had moderate negative relationships with AI literacy (r = -0.400), perceived usefulness (r = -0.591), and willingness to adopt (r = -0.509). The negative associations suggested that educators with more negative risk perceptions were also less likely to be highly literate, feel that the technology was useful, and be willing to adopt it.\u003c/p\u003e\n\u003cp\u003eThe intentions to assess change were weakly, but positively, associated with AI literacy (r = 0.220), perceived usefulness (r = 0.387), and willingness to adopt (r = 0.360), indicating a low-level relationship between the general AI attitudes and assessment reconsideration. Small but positive relationships between perceived institutional readiness and individual perceptions were demonstrated by the small positive correlation between institutional support and literacy (r = 0.293), usefulness (r = 0.246), and willingness (r = 0.139). There were no correlation values that were above multicollinearity levels, which was adequate to proceed to the regression and multivariate analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFaculty\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eWise\u003c/strong\u003e\u003cstrong\u003eDissimilarities\u003c/strong\u003e\u003cstrong\u003ein\u003c/strong\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003cstrong\u003eperceptions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the variance between the perceptions of AI among different faculties, a one-way ANOVA was used, and the data are indicated in Table 4. Three variables were studied: AI literacy, perceived usefulness, and intent to use generative AI tools.\u003c/p\u003e\n\u003cp\u003eThe findings indicated that there was no significant difference between the faculties regarding AI literacy (F = 0.810, p = 0.668), perceived usefulness (F = 0.828, p = 0.654), and willingness to adopt (F = 0.515, p = 0.898) (Figure 6). Faculty representation has been broad in the disciplinary spectrum; however, the fact that it was not diverse enough implied that the perception of educators on generative AI was comparatively similar across different academic fields. These results also showed that attitudes toward generative AI were interdisciplinary and were common across STEM, social sciences, and humanities faculties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe\u003c/strong\u003e\u003cstrong\u003eWillingness\u003c/strong\u003e\u003cstrong\u003eto\u003c/strong\u003e\u003cstrong\u003eAdopt\u003c/strong\u003e\u003cstrong\u003eGenerative\u003c/strong\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine factors that predict readiness to use generative AI tools by educators, a linear regression analysis was carried out. Table 5 presents the coefficients. The model had AI literacy, perceived risk, and institutional support as predictors. The relationship between AI literacy and willingness to adopt was strongly positively related ( 0.734 t = 5.770), meaning that the higher the literacy, the more willing to adopt. Perceived risk exhibited a negative relationship ( \u0026beta; = -0.325, t = -1.943), indicating that greater fears of AI were associated with decreased willingness, but the size of the effect was rather small (Figure 7).\u003c/p\u003e\n\u003cp\u003eThe institutional support showed a low negative coefficient ( = -0.096, t = -0.961), which was not statistically significant. The constant value (1.710) showed a moderate willingness without the effects of predictors. The findings indicated that AI literacy was the most predictive of adoption willingness, followed by perceived risk, and little influence was made by institutional support in predicting the change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal\u003c/strong\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003ePCA\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe principal component analysis was performed to determine structural patterns of AI-related perceptions that teachers had. Table 6 presents the explained variance of PCA. The total variance was explained by the first principal component (PC1), 53.5% of it. This factor seemed to encompass general assessments of generative AI- the aggregation of literacy, usefulness, and willingness- and showed that positive attitudes were grouped.\u003c/p\u003e\n\u003cp\u003eThe second principal component (PC2) was used to clarify the 18.4% variance, which represented a secondary dimension between the perceived risks and institutional support aspects. The third factor (PC3) explained 11.9% of the variance, and it could not add much structural information. Taken together, the first three factors described nearly 83.8% of the variation (Figure 8). The large percentage of variance that PC1 accounts for indicated that the attitudes of participants to generative AI would follow one overarching judgement dimension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster\u003c/strong\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003cstrong\u003eof\u003c/strong\u003e\u003cstrong\u003eProfiles\u003c/strong\u003e\u003cstrong\u003eof\u003c/strong\u003e\u003cstrong\u003eTeacher-AI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe K-means clustering was utilized to divide educators into classes according to their AI literacy, their perceived usefulness, their perceived risk, their willingness to adopt it, their intentions to change their assessment, and their perception of institutional support. Table 7 shows the centroid of the three clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis population had the lowest in terms of literacy (\u0026asymp;2.38), usefulness (\u0026asymp;2.42), willingness to adopt (\u0026asymp;2.18), and perceived risk (\u0026asymp;2.82). The intentions of the assessment change and the perceived institutional support were moderate. This group indicated those teachers who had conservative or hesitant attitudes toward generative AI (Figure 9).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis group showed the greatest literacy (= 4.11), the greatest perceived usefulness (= 4.12), the least risk (= 1.66), and the greatest adoption willingness (= 4.11). The highest score across clusters was also in assessment change intentions and institutional support scores. This cluster is the most proactive and has a high positive orientation towards implementing AI (Figure 9).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cluster had moderate levels of literacy (=3.61), moderate levels of usefulness (=3.38), moderate levels of risk (=2.32), and moderate adoption willingness (=3.14). Institutional support perception and change intentions of assessment were moderate as well. This group was the transitional or balanced group. The clustering showed consistent perceptual and behavioral patterns in the educators that created three consistent sub-groups with different degrees of readiness to adopt generative AI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation\u003c/strong\u003e\u003cstrong\u003eof\u003c/strong\u003e\u003cstrong\u003esex\u003c/strong\u003e\u003cstrong\u003eand\u003c/strong\u003e\u003cstrong\u003ethe\u003c/strong\u003e\u003cstrong\u003ewillingness\u003c/strong\u003e\u003cstrong\u003eto\u003c/strong\u003e\u003cstrong\u003eembrace\u003c/strong\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Chi-square test was used to analyze the relationship between gender and the desire to use generative AI tools. Table 8 shows the contingency distribution by the willingness scores and the chi-square statistics. The chi-square test was also found to provide a value of 31.821 with a p-value of 0.668 implying that there was no statistically significant relationship between gender and willingness to adopt. Both female and male teachers were spread over the adoption spectrum at different levels of willingness at similar frequencies (Figure 4). The Other gender category was smaller in number, but the pattern of distribution was similar. This finding was a pointer that gender was not a determining factor in the formation of attitudes towards the adoption of generative AI in this sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of Major Numerical Trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn all analyses, there were several general trends:\u003c/p\u003e\n\u003cp\u003eModerate AI literacy, moderate perceived usefulness, low-to-moderate risk, and medium levels of adoption willingness were shown by descriptive indicators.\u003c/p\u003e\n\u003cp\u003eAll the differences between the faculties were statistically insignificant, as the attitudes were consistent across disciplines.\u003c/p\u003e\n\u003cp\u003ePatterns of correlation followed close correlations of literacy, usefulness, and willingness, with moderating negative correlations with risk.\u003c/p\u003e\n\u003cp\u003eThe regression outcomes indicated that AI literacy was the best statistical indicator of adoption readiness.\u003c/p\u003e\n\u003cp\u003eThe results of PCA indicated that there was an overriding evaluative factor in the perceptions of educators with AI.\u003c/p\u003e\n\u003cp\u003eThe result of cluster analysis was three educator profiles, which were hesitant to highly proactive.\u003c/p\u003e\n\u003cp\u003eChi-square test was used to determine that there were no differences in willingness to adopt based on gender.\u003c/p\u003e\n\u003cp\u003eThe overall results facilitated a very thorough empirical portrait of the perception and willingness of educators towards the integration of generative AI.\u003c/p\u003e\n\u003ch3\u003eTable 1: Descriptive Statistics of Educators\u0026rsquo; Responses\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnique\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e75%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003ePseudonym\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003eFaculty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003eTenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003eYearsEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003eAgeGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003ePriorAIUse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003eAI_Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e3.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e2.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003ePerceived_Usefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e3.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e3.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003ePerceived_Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e2.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003eWillingness_Adopt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e3.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003eAssessment_Change_Intent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e3.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.1679%;\"\u003e\n \u003cp\u003eInstitutional_Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.39695%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.63359%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7557%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8931%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.48092%;\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.41221%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.58015%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.25954%;\"\u003e\n \u003cp\u003e2.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.34351%;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.10687%;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.96947%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 2: Participant Demographics by Faculty, Tenure, and Gender\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFaculty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTenure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYearsEmployed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgeGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e10-May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eArchitecture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eNon-tenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eArts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eNon-tenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e10-May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eComputer Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e10-May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eComputer Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eEconomics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eEconomics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eNon-tenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eEngineering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e4-Jan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eEngineering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eEnvironmental Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eNon-tenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eForeign Languages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eNon-tenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eHumanities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eHumanities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eInformation Technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eInternational Relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eLaw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e4-Jan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eLaw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eMathematics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e10-May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eMedicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eNon-tenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;=55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eMedicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003ePublic Administration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eNon-tenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;=55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eSocial Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eNon-tenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eSocial Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eSports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003eTourism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eTenured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e4-Jan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6102%;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7797%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 3: Correlation Matrix of AI Variables\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.411%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2761%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI_\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLiteracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived_Usefulness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived_Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0307%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWillingness_Adopt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment_Change_Intent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9448%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitutional_Support\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.411%;\"\u003e\n \u003cp\u003eAI_Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2761%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e-0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0307%;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9571%;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9448%;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.411%;\"\u003e\n \u003cp\u003ePerceived_Usefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2761%;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e-0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0307%;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9571%;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9448%;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.411%;\"\u003e\n \u003cp\u003ePerceived_Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2761%;\"\u003e\n \u003cp\u003e-0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e-0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0307%;\"\u003e\n \u003cp\u003e-0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9571%;\"\u003e\n \u003cp\u003e-0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9448%;\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.411%;\"\u003e\n \u003cp\u003eWillingness_Adopt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2761%;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e-0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0307%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9571%;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9448%;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.411%;\"\u003e\n \u003cp\u003eAssessment_Change_Intent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2761%;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e-0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0307%;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9571%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9448%;\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.411%;\"\u003e\n \u003cp\u003eInstitutional_Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2761%;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1902%;\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0307%;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9571%;\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9448%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e\n\u003ch3\u003eTable 4: ANOVA Results for Faculty Differences\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3904%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.2192%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3904%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3904%;\"\u003e\n \u003cp\u003eAI_Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.2192%;\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3904%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3904%;\"\u003e\n \u003cp\u003ePerceived_Usefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.2192%;\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3904%;\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3904%;\"\u003e\n \u003cp\u003eWillingness_Adopt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.2192%;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3904%;\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 5: Regression Analysis of Willingness to Adopt\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd_Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e\u003cstrong\u003et_value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep_value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003econst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e1.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003e2.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003eAI_Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003e5.770\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003ePerceived_Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e-0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003e-1.943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003eInstitutional_Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9147%;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.0853%;\"\u003e\n \u003cp\u003e-0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 6: PCA Explained Variance\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrincipal_Component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExplained_Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 7: K-Means Cluster Centroids\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1914%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI_Literacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2654%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived_Usefulness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0494%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived_Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0432%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWillingness_Adopt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4321%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment_Change_Intent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitutional_Support\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.037%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1914%;\"\u003e\n \u003cp\u003e2.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2654%;\"\u003e\n \u003cp\u003e2.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0494%;\"\u003e\n \u003cp\u003e2.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0432%;\"\u003e\n \u003cp\u003e2.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4321%;\"\u003e\n \u003cp\u003e3.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e2.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.037%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1914%;\"\u003e\n \u003cp\u003e4.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2654%;\"\u003e\n \u003cp\u003e4.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0494%;\"\u003e\n \u003cp\u003e1.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0432%;\"\u003e\n \u003cp\u003e4.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4321%;\"\u003e\n \u003cp\u003e4.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e3.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.037%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1914%;\"\u003e\n \u003cp\u003e3.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2654%;\"\u003e\n \u003cp\u003e3.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0494%;\"\u003e\n \u003cp\u003e2.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0432%;\"\u003e\n \u003cp\u003e3.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4321%;\"\u003e\n \u003cp\u003e2.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e2.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.037%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 8: Chi-Square Cross-tab of Gender vs Willingness to Adopt\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.33981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.8835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.69256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep_value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.33981%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.8835%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e31.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.69256%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.33981%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.8835%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e31.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.69256%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.33981%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.8835%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.53074%;\"\u003e\n \u003cp\u003e31.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.69256%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe research reviewed perceptions, experiences, and behavioural intentions of educators regarding the topic of artificial intelligence in higher education, specifically in the area of literacy, perceived usefulness, perceived risk, institutional support, and willingness to use AI-driven tools [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The findings presented a consistent perspective of institutional and personal determinants of AI adoption. These are the main findings that are discussed within the context of previous research, scientific arguments, and general implications on educational practice.\u003c/p\u003e \u003cp\u003eThe descriptive analysis (Table\u0026nbsp;1) revealed that teachers were a diverse group of faculty with demographic backgrounds, which implied that the sample encompassed varied disciplinary views. Suggested average values of AI-related variables indicated fairly moderate literacy and readiness to adopt the system [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The average score of AI literacy was more than three, showing a moderate level of knowledge, though not a high one. The perceived usefulness had a similar level of large-scale usefulness, which implies that educators recognized the value of AI at least when they were only moderately familiar with the tool [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The perceived risk scores were lower than other variables, which means that the educators did not perceive AI as really threatening. The readiness to adopt had a moderate-to-high mean score, and the coefficient of variance indicated that the responses of different individuals were not similar, but rather reflected their personal willingness to adopt [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Change in intent of assessment and institutional support was also observed to follow the same pattern with the intent to change assessment strategies among the educators, but lacked complete faith in the structures of support in their institutions.\u003c/p\u003e \u003cp\u003eTrends occurring among the AI variables (Table\u0026nbsp;3) gave basic information on the cognitive and behavioural connections between literacy, perceptions, and adoption. The positive correlation between AI literacy and perceived usefulness was high, which showed that educators who were more literate viewed AI tools as useful, which corresponded to previous studies on technology acceptance [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. A comparable correlation was observed between perceived usefulness and willingness to adopt, which indicated a central route associated with the Technology Acceptance Model, in which perceived usefulness was a determining factor in behavioural intention [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The perceived risk and willingness did not have a positive relationship, implying that the perceived threat of errors or fairness, or student abuse lowered the adoption willingness [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In spite of the fact that this negative association was moderate, it meant that risk perception was an important factor in adoption behaviour. Institutional support demonstrated positive correlations with literacy and usefulness, but was modest, indicating that supportive environments increased the confidence of educators, but its effect on behavioural intention was relatively small [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe outcomes of the ANOVA (Table\u0026nbsp;4) provided insignificant differences in faculty groups in terms of AI literacy, perceived usefulness, or willingness to adopt. This insignificance implied that faculty discipline was not a determining factor in the development of AI preparedness [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. These results were in contrast to the previous literature that argued that the disciplines that were more digitally active, e.g., engineering or computer science, were more likely to exhibit greater AI preparedness. The homogeneity in this study implied that maybe the institutional culture has homogenized AI exposure by the different faculties, or that the disciplinary differences were superseded by institutional practices or training access, or experience [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;5 provides more information on willingness to adopt AI predictors, as well as the regression analysis. AI literacy was identified as the best positive predictor, with a statistically significant coefficient, which showed that teachers among the most literate were more likely to use AI tools in teaching and assessment [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The perceived risk had a negative coefficient, meaning that the fears of reliability or ethics led to a moderate decrease in the adoption readiness, but the relationship was not quite strong and slightly short of the strong level of significance [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Although theoretically important, the concept of institutional support did not prove to be a significant predictor in this dataset. This trend implied that individual competence and risk assessment were more important than institutional consideration in affecting behavioural intentions, and this trend has been replicated across many higher education research works in which autonomy and self-efficacy have been found to influence adoption behaviour more than organisational instructions [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of the PCA (Table\u0026nbsp;6) indicated that the perceptions and behaviours related to AI created a moderately consistent pattern, where the first principal component accounted for over 50 percent of all the variance. This element was probably a general aspect of AI preparedness, including literacy, utility, and adoption intention [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The latter elements were more specific or nuanced, e.g., risk perception or institutional factors. Such trends favored the assumption that the attitude of educators to AI was not that fragmented but rather represented a general dimension of readiness [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe outcomes of the clustering (Table\u0026nbsp;7) also demonstrated heterogeneity of educators. A cluster of teachers that showed moderate literacy yet high risk perceptions and reduced willingness to adopt represented one cluster [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. A different cluster was comprised of high literacy educators who perceived high usefulness, and willingness to adopt, a group probably of early adopters who are deeply interested in the integration of AI. The third cluster was an intermediate group whose scores on variables gave moderate results [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This stratification revealed that the adoption of AI in higher education did not occur evenly but in the ways of early, moderate, and hesitant adopters- in line with diffusion of innovation theory [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe willingness to adopt between the two genders was analyzed with Chi-Square (Table\u0026nbsp;8), which did not show any statistically significant relationship. The scores of adoption did not significantly differ across the groups of female, male, and other genders [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This finding was in line with some of the current studies that indicated that there are fewer gender differences in the adoption of technology in the sense that people are exposed to digital tools in various academic settings [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCombining these findings, they put forward the hypothesis that the willingness of educators to adopt AI was primarily conditional on their literacy and perceived usefulness and not their demographics or faculty affiliation. The findings were consistent with the existing literature that demonstrated familiarity and perceived pedagogical value to continue as key factors in the adoption of AI [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Previous research in the field of educational technology always stressed that knowledge and utility were the main driving forces, and risk and institutional pressure were the secondary or indirect influences. The mediocre correlation between the perceived risk and readiness to adopt seen in this paper also resonated with past debates on the subject, which mention issues of transparency, mistakes, academic integrity, and ethical concerns as obstacles to the adoption of AI [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCognitive and behavioural theories can explain the strong impact that literacy has on adoption scientifically. People who possessed better knowledge of emerging technologies usually displayed more confidence, less uncertainty, as well as self-efficacy, which enhanced their willingness to adopt. On the same note, the perceived usefulness was a logical assessment of anticipated gains that acted as a great motivator of behaviour in the academic environment [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Perceived risk also played a role in the cautious behaviour because it was perceived as an element of how educators cognitively evaluated possible negative events to occur. The little impact of institutional support may be explained by the variation in the perceived organisational commitment or the lack of structural support, which diminished its predictive power of predictions [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe conclusions had a number of implications. To begin with, the correlation between literacy and adoption was too high to ignore the fact that, with specific training in place, it may be possible to gain more adoption. Enhancing the knowledge of educators concerning AI tools can help to decrease the risk-related issues and improve perceived usefulness [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Second, the cluster patterns revealed that various training strategies should be used since early adopters, careful adopters, and moderate adopters can be supported in different ways. Third, there were no differences in disciplines in terms of discipline, and this indicated that institutions were able to develop cross-faculty professional development approaches and not discipline-specific interventions. Fourth, the small role played by the perception of risk reflected that ethical, reliability, and transparency issues were still relevant towards building the trust of educators in AI [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the useful information offered in the study, there were some limitations. The sample size was not that big, thus it might have limited the identification of small group differences or less strong associations. The subjective aspect of the data collected might have caused a bias because of social desirability or variation in the meaning of the questions to the respondents [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The limitation of the cross-sectional design was that it was not possible to sense the change of attitudes over time, because the sphere of AI technologies was rapidly developing. Moreover, the different meanings that institutional support might have had among respondents may have been the cause of its lower power [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, the research proved that the readiness of educators to use AI was determined by their literacy and perceived usefulness, with a secondary role in risk perception. Demographic characteristics contributed a little, and institutional support proved to be not a predictive force. These results were added to the general knowledge on the concept of AI adoption in higher education and outlined the avenues that could be used to improve the preparedness and trust of educators.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe results of the current research proved that the level of AI literacy, perceived usefulness, and readiness to use AI-assisted assessment practices in university educators were moderate, whereas the level of perceived risk was rather low. The analysis has addressed all the goals of the research, measured the willingness of the educators, found strong correlations between major psychological and institutional factors, and described the structural patterns of educators' responses. Very positive relations between AI literacy, perceived usefulness, and willingness to adopt established that the higher the familiarity with AI, the higher the adoption tendencies, but perceived risk demonstrated a negative correlation. Despite no material differences being found across faculties, the multivariate analyses, i.e., regression, PCA, and clustering, provided a clear empirical formulation of the adoption behavior. The research had a scientific impact because it presented a data-driven profile of the AI adoption patterns of educators in the field of higher education, which is evidence-based planning by the institution. The findings indicated that the institutional support could still be a variable parameter, which is the area where it can be improved. Future studies can increase the sample heterogeneity, use longitudinal follow-up of the adoption behavior, and use experiments to measure the effectiveness of the training interventions in increasing the willingness of educators to adopt AI-based assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eConflict of Interest\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to express their sincere gratitude to all the educators who participated in this study, generously sharing their time, experiences, and insights. We also thank the University Education Academy and the cross-faculty Community of Practice at [University Name, anonymized if required] for their assistance in participant recruitment. We are grateful to our colleagues for their valuable feedback during the development of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAithal, P. S., \u0026amp; Maiya, A. K. (2023). Innovations in higher education industry\u0026ndash;Shaping the future. International Journal of Case Studies in Business, IT, and Education (IJCSBE), 7(4), 283-311.\u003c/li\u003e\n \u003cli\u003ePandey, J. K. (2024). Unlocking the power and future potential of generative AI in government transformation. Transforming Government: People, Process and Policy.\u003c/li\u003e\n \u003cli\u003eKooli, C. (2023). Chatbots in education and research: A critical examination of ethical implications and solutions. 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British journal of educational technology, 53(4), 914-931.\u003c/li\u003e\n \u003cli\u003eCarian, E. K., \u0026amp; Hill, J. D. (2021). Using frameworks of social desirability to teach subjectivity in interviews. Teaching Sociology, 49(4), 381-393.\u003c/li\u003e\n \u003cli\u003eGerlich, M. (2023). Perceptions and acceptance of artificial intelligence: A multi-dimensional study. Social Sciences, 12(9), 502.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"IMCRA – International Meetings and Conferences Research Association","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Academic integrity, Azerbaijan, Generative artificial intelligence, Higher education, Qualitative research","lastPublishedDoi":"10.21203/rs.3.rs-8330375/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8330375/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe fast adoption of generative artificial intelligence (GAI) in higher education has created a dire necessity to comprehend the professional reaction of educators, but empirical studies that aim to explain their attitude and changes are limited, especially in the new educational setting, such as in Azerbaijan. This gap was addressed in the current study, which qualitatively investigated the Azerbaijani university teachers' attitude to GAI, their subsequent pedagogical practices, and their perceived problems and areas of support. The data was gathered through a two-phase qualitative design and an online survey (n\u0026thinsp;=\u0026thinsp;30) and semi-structured interviews (n\u0026thinsp;=\u0026thinsp;8) with educators of various faculties of a research-intensive university. Thematic analysis through inductive methods showed that educators saw the potential of GAI in individualized learning and administrative efficiency, but with moderate literacy (Mean\u0026thinsp;=\u0026thinsp;3.42) and moderate willingness to adopt (Mean\u0026thinsp;=\u0026thinsp;3.21), the academic integrity, assessment validity, and \u0026lsquo;AIgiarism\u0026rsquo; were very important concerns. The essential results were the high positive correlation of AI literacy and perceived usefulness (r\u0026thinsp;=\u0026thinsp;0.759) and the active adaptation of educators who were actively engaging in assessment redesign and process-oriented work, but they stated that they received inconsistent institutional support (Mean\u0026thinsp;=\u0026thinsp;3.04, SD\u0026thinsp;=\u0026thinsp;1.1). This paper concludes that the implementation of GAI needs to be contextualized through professional development initiatives and sound institutional policies, which directly resolve the ethical and pedagogical issues of educators. It provides evidence supporting the importance of educator-centered support when adopting responsible AI in higher education modernization.\u003c/p\u003e","manuscriptTitle":"Educators’ Perceptions and Pedagogical Approaches in the Era of Generative Ai Integration: A Qualitative Study in Higher Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 07:43:02","doi":"10.21203/rs.3.rs-8330375/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9196d7cc-eab8-4bf8-91fd-489285d92632","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59535785,"name":"Special Education"}],"tags":[],"updatedAt":"2025-12-12T07:43:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 07:43:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8330375","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8330375","identity":"rs-8330375","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}