Perspectives of University Educators Regarding ChatGPT: A Validation Study Based on the Technology Acceptance Model

Perspectives of University Educators Regarding ChatGPT: A Validation Study Based on the Technology Acceptance Model | 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 Perspectives of University Educators Regarding ChatGPT: A Validation Study Based on the Technology Acceptance Model Muna Barakat, Nesreen A. Salim, Malik Sallam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3919524/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 Background ChatGPT is a generative artificial intelligence (AI) model that has a promising potential in higher education. Nevertheless, the integration of ChatGPT into higher education requires the assessment of university educators’ perspectives regarding this novel technology. This study aimed to develop and validate a survey instrument specifically tailored to assess ChatGPT usability and acceptability among university educators. Methods Development of the survey items was based on the Technology Acceptance Model (TAM) followed by expert content validity assessment and pilot testing to improve phrasing of the items. The survey instrument involved assessment of the demographic variables in addition to a total of 40 TAM-based items. The survey was distributed among educators in Jordan in two phases: February 2023–April 2023 and October 2023. The self-administered survey was distributed via a convenience approach among the contacts of the authors. Assessment of the construct validity was done through exploratory factor analysis (EFA) based on principal component analysis (PCA), while the internal consistency of the inferred constructs was checked using the Cronbach’s α. Results The final sample comprised 236 university educators, with 72% who heard of ChatGPT before the study ( n = 169), of whom 76 have already used ChatGPT (45%). The EFA showed a significant Bartlett’s test of sphericity ( P < .001) and adequate Kaiser-Meyer-Olkin measure (KMO = .698). The six constructs inferred through EFA explained a cumulative 64% of the variance in the educators’ attitude to ChatGPT. These constructs comprised 31 items classified into: (1) “Effectiveness” (α = .845), (2) “Anxiety” (α = .862), (3) “Technology readiness (α = .885), (4) Perceived usefulness (α = .848), (5) Social influence (α = .803), and (6) Perceived risk (α = .796). Conclusions This study identified six key constructs that could be exploited for comprehensive understanding of the university educators' attitude toward ChatGPT. The novel survey instrument herein termed “Ed-TAME-ChatGPT” involved positive influencing factors such as perceived usefulness and effectiveness, positive attitude to technology, and social influence in addition to negative factors including anxiety and perceived risk. The developed survey instrument can provide a robust framework for further investigation into the usability and acceptability of ChatGPT among university educators, given the nearly inevitable integration of generative AI into higher education. AI chatbots GPT education technology survey opinion knowledge practices KAP Figures Figure 1 Figure 2 BACKGROUND Higher education is set for a significant transformation with the impending integration of generative artificial intelligence (AI) as a routine practice among university students and educators [ 1 – 4 ]. This technological revolution represents a paradigm shifting concept regarding knowledge acquisition and dissemination, especially in the domain of higher education [ 5 , 6 ]. Generative AI as exemplified by widely used models such as ChatGPT (OpenAI, San Francisco, CA), can revolutionize teaching and learning methodologies in addition to its potential utility in expediting academic research [ 7 , 8 ]. Nevertheless, it is important to fully understand the implications of integrating ChatGPT among other generative AI models in higher education from the perspective of educational institutions, university educators, and students [ 9 , 10 ]. This comes in light of the well-recognized and described limitations and failures of such AI models including concerns regarding bias, inaccuracies, cybersecurity risk as well as the threats of declining critical thinking and analytical skills among students in addition to the potential job losses [ 8 , 11 – 18 ]. On the other hand, the generative AI models including ChatGPT can offer several benefits in higher education [ 19 – 21 ]. These AI models can engage students and educators in interactive, intelligent conversations while adapting to individual needs; thus, offering personalized learning experiences [ 3 , 13 , 22 ]. This personalized experience allows for the generation of educational content customized to varying students’ learning speeds, styles, and interests, making education an efficient and interesting experience [ 23 , 24 ]. For educators, ChatGPT can be a powerful tool for organizing study materials and rapidly generating a variety of examples and explanations across different subjects [ 23 ]. This capability can aid educators in simplifying complex concepts for students [ 23 ]. Additionally, generative AI models including ChatGPT hold promising potential for automating and standardizing the scoring of essays, potentially enhancing the efficiency and consistency of evaluations in various educational settings [ 25 , 26 ]. Furthermore, generative AI models can facilitate the digitalization of education and enhance the pedagogical methods’ efficiency and quality [ 27 – 29 ]. A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis and a recent review of ChatGPT in education revealed the future trajectory of the needed research regarding ChatGPT utility in education [ 30 , 31 ]. As stated earlier, the strengths of ChatGPT can be related to enhanced learning outcomes and improved resource efficiency [ 30 ]. The ChatGPT challenges include deepening the digital divide, overreliance on technology, and concerns about the quality of generated content [ 30 , 32 ]. Opportunities provided by ChatGPT arise through innovative pedagogies, while threats include ethical issues and data privacy issues [ 30 , 31 ]. Thus, the successful integration of ChatGPT in higher education mandates balancing ChatGPT benefits against its challenges. The integration of a novel technology in higher education necessitates understanding the perspectives of stakeholders, namely university students and educators alike [ 33 – 36 ]. In particular, educators’ attitudes, concerns, and readiness to embrace novel AI technologies significantly influence their adoption and effectiveness in teaching and learning environments [ 37 ]. Therefore, it is essential to assess university educators’ perspectives on novel AI tool such as ChatGPT which would be important for the successful implementation of these tools in higher education [ 38 – 40 ]. Our previous work involved two studies that focused on designing, validating, and confirming the validity of a survey instrument based on the Technology Acceptance Model (TAM) among students especially in health schools [ 41 , 42 ]. These studies showed that perceived usefulness, ease of use, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks were linked with positive attitudes to ChatGPT [ 41 , 42 ]. However, to the best of our knowledge very few studies have focused on the perspective of university educators [ 39 , 40 , 43 ]. Therefore, the aim of this study was to establish and test the validity of a TAM-based construct for understanding the acceptance and use of ChatGPT among academic personnel in Jordan. By establishing a survey instrument specifically tailored towards the perspectives of university educators, the implementation of ChatGPT in higher education can be guided by evidence. Thus, we aimed to develop a valid survey tool to guide the effective and responsible integration of ChatGPT in higher education settings as well as to highlight the feared risks and concerns among university educators towards this inevitable technology. This approach is particularly relevant considering the focus of the previous studies on university students, with university educators being a relevant yet an understudied group. METHODS Study Design The development of the survey instrument began with an extensive literature review that focused on the application of the TAM in understanding the acceptance of novel technologies by students and educators based on the original TAM framework [ 44 – 57 ]. Following the literature review, initial item development in the English language was performed by the authors (university educators with expertise in three diverse healthcare disciplines and with previous experience in survey construction and validation). To ensure content validity, the three authors engaged in an internal discussion with a subjective evaluation of the initial survey items in terms of clarity, comprehensiveness, and relevance as well as to identify and address any potential biases or issues in the wording of the items, such as vagueness or complexity. This internal discussion led to the identification of potential domains for inclusion in the final questionnaire comprising 40 items as follows: perceived usefulness, perceived ease of use, perceived risks, anxiety, attitude towards the technology, social influence, and effectiveness. This was followed by forward translation into Arabic and backward translation into English of the 40 items, which were done by the three bilingual authors with minor modifications. Then, pilot testing of the items in Arabic was done to ensure clarity among four university educators who were excluded from the final analysis. Based on the feedback received, minor language modifications were made to enhance the overall clarity of the survey items. The construct validity of the scale herein termed “Ed-TAME-ChatGPT” was checked following survey distribution, using 40 TAM-based items evaluated among the respondents who used ChatGPT before the study. The minimum sample size was estimated at approximately 80 participants based on the previous guidelines of having at least two study subjects per survey item for survey validation [ 58 ]. Ethics statement This study was approved by the Institutional Review Board (IRB) at the Faculty of Pharmacy, Applied Science Private University (Approval number: 2023-PHA-4, on 24 January 2023). Components of the Finalized Questionnaire and its Distribution The finalized survey in the Arabic language was uploaded to Google Forms and distributed using a convenience sampling approach. This involved reaching out to the professional contacts of the authors, requesting them to share the survey within their networks and encouraging further distribution. Participation in the survey was voluntary and there was no incentive for participation. The questionnaire began with a detailed introduction outlining the study objectives, accompanied by a mandatory electronic consent (e-consent) item, which was mandatory for the participants to proceed. This introductory section explicitly assured participants of their complete anonymity and privacy. This was followed by the demographic section with items to assess the following variables: (1) age; (2) sex; (3) nationality (Jordanian vs. non-Jordanian); School/College (health [medicine, dentistry, pharmacy, nursing, rehabilitation sciences, applied medical sciences, veterinary medicine, public health] vs. scientific [science, engineering, agriculture, information technology, nanotechnology, artificial intelligence] vs. humanities [Arts, Business, Sharia, Law, Islamic Studies, Physical Education, Arts, Languages, Tourism, Media, Archaeology]); (4) university [public vs. private]; (5) the highest educational degree obtained (PhD or fellowship degree vs. MSc or a specialty degree); (6) the country where the highest educational degree was attained (Jordan vs. other Arab country vs. USA vs. UK vs. EU vs. others); and (7) rank (full professor, associate professor [grouped as “tenured”] vs. assistant professor, lecturer, lecturer assistant [grouped as “without tenure” ]). The next section involved two screening items as follows: First, “Have you heard of ChatGPT before the study?”, with “yes” response required to move into the next item, while the answer of “no” resulted in survey submission. The next item was “Have you used ChatGPT before the study?” with “yes” resulting in the presentation of the full 40 TAM-based items. The final section involved the presentation of the 40 TAM-based items, with each item assessed on a 5-point Likert-scale with the following responses: strongly agree scored as 5, agree scored as 4, neutral/no opinion scored as 3, disagree scored as 2, and strongly disagree scored as 1. The scoring was reversed for the items implying negative attitude towards ChatGPT. The complete phrasing of the included items is presented in ( Table S1 ). Statistical and Data Analyses The statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 26.0 (Armonk, NY, USA: IBM Corp). Exploratory factor analysis (EFA) was employed to investigate the factorial structure of the Ed-TAME-ChatGPT scale which comprised a total of the 40 items. Principal component analysis (PCA) was used as the extraction method, with oblimin rotation to determine correlations between factors. The Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test of sphericity were applied to evaluate the data appropriateness for EFA. Subsequently, the internal consistency of the Ed-TAME-ChatGPT constructs inferred following EFA was assessed using the Cronbach’s α values. Scores for each construct were computed by dividing the total scores by the number of items within that construct, yielding a score range of 1–5. The overall Ed-TAME-ChatGPT scores were computed based on the overall average of Ed-TAME-ChatGPT involving the items of each construct. The scoring classification for both the TAME-ChatGPT and its individual constructs was categorized as follows: scores ranging from 1.00 to 2.33 indicated disagreement (negative), 2.34 to 3.67 indicated a neutral position, and 3.68 to 5.00 indicated agreement (positive). Associations between categorical variables were assessed using the χ 2 (chi-square) and the two-sided Fisher’s exact (FET) tests. Univariate analyses were conducted to assess the associations between Ed-TAME-ChatGPT and the demographic variables as well as the individualized constructs with statistical significance for P < .050. For multivariate analyses, predictor variables were selected based on a P value of < .100 in univariate analyses. Linear regression with Analysis of Variance (ANOVA) was employed to test the significance of the regression model, and multicollinearity was assessed using the Variance Inflation Factor (VIF), with a threshold value of 3.0 indicating potential concerns. RESULTS General Sample Features The final sample consisted of 236 educators, of whom 169 heard of ChatGPT before the study (71.6%), with only 76 who indicated the use of ChatGPT before the study (45.0% of those who heard of ChatGPT). The vast majority of the study sample were Jordanians (98.3%), and a majority obtained their highest educational degree outside the Arab countries. The full features of the study sample are illustrated in (Table 1 ). The percentage of those who used ChatGPT increased significantly over the study period from 33/99 during February 2023–April 2023 (43.4%) to 43/70 during October 2023 (56.6%. P 43 years 117 49.6% Sex Male 137 58.1% Female 99 41.9% Nationality Jordanian 232 98.3% Non-Jordanian 4 1.7% School Health 127 53.8% Scientific 51 21.6% Humanities 58 24.6% University Public 150 63.6% Private 86 36.4% Highest educational degree PhD 181 76.7% MSc 55 23.3% Country of highest attained degree USA 37 15.7% UK 54 22.9% Europe 29 12.3% Jordan 79 33.5% Arab country other than Jordan 16 6.8% Others 12 5.1% Australia 4 1.7% Canada 5 2.1% Country of highest attained degree Non-Arab 141 59.7% Arab 95 40.3% Rank Professor 55 23.3% Associate Professor 52 22.0% Assistant Professor 74 31.4% Lecturer 36 15.3% Teaching assistant 19 8.1% Academic rank With tenure 107 45.3% Without tenure 129 54.7% Have you heard of ChatGPT before the study? Yes 169 71.6% No 67 28.4% Have you used ChatGPT before the study? 1 Yes 76 45.0% No 93 55.0% 1 Among those who heard of ChatGPT. Factorability of the Correlation Matrix The EFA was conducted on a set of 40 items to identify underlying constructs that account for the variance in the responses. Bartlett’s test of sphericity was significant (χ²= 2226.833, df = 780, P < .001), indicating the factorability of the correlation matrix. The KMO measure of sampling adequacy was .698, indicating that the data were suitable for factorial analysis. The scree plot showed that the optimal number of underlying factors was six based on the eigenvalue cutoff specified at 1.5, which explained a cumulative total of 63.7% of the variance in the data as indicated in the scree plot (Fig. 1). Figure 1. Scree plot representing the eigenvalues of the factors identified through principal component analysis . The red line indicated the eigenvalue cutoff specified at 1.50. The eigenvalues for the six factors were 11.702, 4.964, 3.244, 2.148, 1.893, and 1.545, respectively. A total of 31 items out of the 40 tested items loaded significantly with absolute coefficient values > 0.50 on one of the six factors, and factor loadings ranging from absolute 0.515 to absolute 0.848. Based on the content of the items for each factor, these factors were named based on the original TAM as follows: Factor 1 was named “effectiveness” comprising 6 items (T3, T4, T6, T16, T17, T18) with a Cronbach alpha value of .845. Factor 2 was named “anxiety” comprising 5 items (T36, T37, T38, T39, T40) with a Cronbach alpha value of .862. Factor 3 was named “technology readiness” comprising 5 items (T31, T32, T33, T34, T35) with a Cronbach alpha value of .885. Factor 4 was termed “perceived usefulness” comprising 5 items (T1, T7, T8, T9, T10) with a Cronbach alpha value of .848. Factor 5 was termed “social influence” comprising 4 items (T22, T23, T24, T25) with a Cronbach alpha value of .803. Finally, factor 6 was termed “perceived risk” comprising 6 items (T19, T20, T26, T27, T29, T30) with a Cronbach alpha value of .796. The final educators TAME-ChatGPT (Ed-TAME-ChatGPT) validated items and constructs are illustrated in (Fig. 2). Figure 2. The final Ed-TAME-ChatGPT items and constructs . Attitudes of the Participating Educators to ChatGPT based on Ed-TAME-ChatGPT and the demographic/academic variables Univariate analysis revealed that only Ed-TAME-ChatGPT constructs were significantly associated with the overall Ed-TAME ChatGPT scores (Table 2 ). Table 2 Demographic, academic, and Ed-TAME-ChatGPT constructs’ association with the overall Ed-TAME-ChatGPT scores. Category Variable TAME categories Neutral Positive Count (%) Count (%) P value, χ 2 Age ≤ 43 years 31 (63.3) 18 (36.7) .767, .088 > 43 years 18 (66.7) 9 (33.3) Sex Male 27 (64.3) 15 (35.7) .970, .001 Female 22 (64.7) 12 (35.3) Nationality Jordanian 49 (66.2) 25 (33.8) .054, 3.728 Non-Jordanian 0 (0) 2 (100) School Health 30 (65.2) 16 (34.8) .551, 1.192 Scientific 14 (70.0) 6 (30.0) Humanities 5 (50.0) 5 (50.0) University Public 32 (69.6) 14 (30.4) .251, 1.319 Private 17 (56.7) 13 (43.3) Education PhD 38 (67.9) 18 (32.1) .302, 1.064 MSc 11 (55.0) 9 (45.0) Country of highest attained degree Non-Arab 31 (67.4) 15 (32.6) .510, .433 Arab 18 (60.0) 12 (40.0) Academic rank With tenure 26 (61.9) 16 (38.1) .603, .270 Without tenure 23 (67.6) 11 (32.4) Perceived usefulness categories Negative 4 (100) 0 (0) < .001, 21.776 Neutral 22 (100) 0 (0) Positive 23 (46.0) 27 (54.0) Effectiveness categories Negative 4 (100) 0 (0) < .001, 21.091 Neutral 24 (96.0) 1 (4.0) Positive 21 (44.7) 26 (55.3) Social influence categories Negative 6 (100) 0 (0) < .001, 22.659 Neutral 29 (87.9) 4 (12.1) Positive 14 (37.8) 23 (62.2) Perceived risk categories Positive 6 (33.3) 12 (66.7) < .001, 15.786 Neutral 24 (63.2) 14 (36.8) Negative 19 (95.0) 1 (5.0) Technology readiness categories Negative 1 (100) 0 (0) .048, 6.060 Neutral 12 (92.3) 1 (7.7) Positive 36 (58.1) 26 (41.9) Anxiety categories Positive 8 (44.4) 10 (55.6) .007, 9.894 Neutral 25 (61.0) 16 (39.0) Negative 16 (94.1) 1 (5.9) In multivariate analysis with the variables having P < .100 in univariate analysis, the ANOVA for the regression model yielded an F statistic of 14.343 with a P < 0.001, indicating the model robustness. The model, with an adjusted R 2 of 0.555 and a standard error of 0.322, identified several significant predictors. Nationality (β = 0.138, P = .084), perceived usefulness (β = 0.198, P = .030), and effectiveness (β = 0.240, P = .011) positively influenced ChatGPT usage. Social influence was also a positive predictor (β = 0.260, P = .004). In contrast, perceived risk emerged as a negative predictor (β=−0.320, P < .001). However, technology readiness (β = 0.108, P = 0.236) and anxiety (β=−0.104, P = .245) were not statistically significant (Table 3 ). Table 3 Regression analysis of the predictors influencing ChatGPT usage based on the Ed-TAME-ChatGPT constructs. Model Coefficients 1 Adjusted R 2 = 0.555, SE = 0.322 Unstandardized Coefficients Standardized Coefficients T statistic P value VIF 3 ANOVA F statistic = 14.343, P value < .001 B SE 2 Beta Constant 0.787 0.392 2.009 0.049 Nationality 0.413 0.236 0.138 1.753 0.084 1.047 Perceived usefulness categories 0.162 0.073 0.198 2.218 0.030 1.343 Effectiveness categories 0.194 0.074 0.240 2.621 0.011 1.414 Social influence categories 0.197 0.067 0.260 2.947 0.004 1.307 Perceived risk categories −0.216 0.057 −0.320 −3.817 < .001 1.180 Technology readiness categories 0.120 0.100 0.108 1.195 0.236 1.367 Anxiety categories −0.073 0.062 −0.104 −1.172 0.245 1.322 1 Dependent Variable: Overall Ed-TAME-ChatGPT usage score; 2 SE: Standard error; 3 VIF: Variance inflation factor. Statistically significant P values are highlighted in bold style. DISCUSSION In the present study we explored the validity of a novel survey instrument specifically designed to assess the attitude to ChatGPT among university educators. Throughout the study period, a notable increase in the number of university academics who self-reported the use of ChatGPT was observed. This finding is indicative of ChatGPT’s growing popularity as a tool in higher education among students and academics alike as indicated by recent studies [ 35 , 41 , 59 – 63 ]. The trend of increasing ChatGPT usage suggests the recognition of generative AI potential as a valuable aid in teaching and learning processes. This shift can also be indicative of an impending broader transformation in educational methodologies manifested in increasing digitization and incorporation of AI in higher educational settings. To reach reliable conclusions regarding university educators' attitudes towards ChatGPT, it is imperative to pursue such an aim using validated methodologies. This approach is necessary to ensure that the investigation of such an aim accurately achieve a comprehensive understanding of the motivators and barriers that could enhance or preclude the integration of generative AI in higher education. One popular, practical, and highly relevant framework that was used in this study is the TAM, with its suitable applicability to assess educators’ adoption of a novel technology shown in a meta-analysis by Scherer et al. [ 51 ]. In this study, six constructs were identified based on the TAM and appear to explain a substantial degree of the educators’ attitude towards ChatGPT. These constructs were effectiveness, anxiety, technology readiness, perceived usefulness, social influence, and perceived risk. Reflection on these TAM-based constructs suggests the relevance of psychological, social, and practical factors that influence the adoption and integration of new technologies such as ChatGPT among educators in higher educational settings. The identification of these factors highlights the complex nature of the process of AI integration into higher education, which was previously shown in various similar contexts. For example, several factors were identified to severely affect the quality of distance-based online learning particularly during COVID-19 pandemic such as unreliable internet availability, the lack of motivation, psychological distress, and institutional support [ 64 – 66 ]. A systematic review by Regmi & Jones identified highlighted the complexity of e-learning in health sciences education in terms of challenges such as poor motivation, the need for resources and relevance of information technology (IT) skills [ 67 ]. Under the lens of TAM, the acceptance and adoption of previous digital education focused mainly on the students’ perspectives and identified several constructs were identified as relevant determinants of attitudes to such technologies such as the perceived usefulness, perceived ease of use, self-efficacy, behavioral intention, social influences, among others [ 49 , 68 – 71 ]. In the current study, the perceived usefulness emerged as a significant predictor of attitude towards ChatGPT among the participating university educators. This result is understandable based on the previous evidence showing the greater likelihood of embracing a novel technology if it is perceived as beneficial [ 72 – 74 ], which was recently shown by Wang et al. in the context of AI adoption in e-commerce [ 75 ]. Usefulness in the context of generative AI such as ChatGPT pertains to its potential to enhance learning outcomes, to facilitate personalized education, and to streamline the tedious administrative tasks [ 76 , 77 ]. Thus, understanding and addressing the university educators’ perceptions of ChatGPT usefulness appears critical for the acceptance and adoption of ChatGPT among this relevant group in higher education. In this validation study, effectiveness emerged as an important construct reflecting the evident impact of perceived technology effectiveness on educational objectives. The anticipation of significant benefits from generative AI such as ChatGPT in enhancing student engagement and interaction with academic tasks can play a crucial role in creating a positive attitude towards it among university educators [ 78 ]. Additionally, the expectation that ChatGPT can enhance the overall quality of education, is manifested in higher perceived effectiveness which would be regarded as an important factor for adopting ChatGPT to achieve academic excellence. Social influence was identified as an important construct expected to play a significant role in shaping university educators' acceptance of ChatGPT. This construct captures the influence of peer opinions, institutional culture, and prevailing educational trends on the decision-making process regarding the adoption of new technologies such as ChatGPT. Endorsements of adoption generative AI from colleagues and the broader academic community appeared to play an important role in shaping university educators' willingness to acceptance and utilize ChatGPT in educational settings. Therefore, it is important to recognize and consider the role of social influence in facilitating the widespread adoption of generative AI technologies in higher education. The role of perceiving AI as a social norm was revealed in a recent study by Rahiman & Kodikal among academic staff members in Asia, which further supports the identification of social influence as a construct in this study [ 79 ]. Technology readiness was the fourth key construct identified in this study in relation to ChatGPT usage among university educators. This construct involved the assessment of university educators’ willingness and capacity to adopt and effectively use novel technological tools. Facilitating technology readiness involves providing necessary resources, training, and support, which would enable the university educators to confidently integrate ChatGPT into their teaching practices. A meta-analysis on the technology readiness impact on the use of technology highlighted the importance of considering the motivators (optimism and innovativeness) and inhibitors (insecurity and discomfort) for further dissecting this important factor in driving the adoption and use of novel technologies [ 80 ]. Conversely, the barriers to ChatGPT use as identified in the current study were represented by two constructs namely the anxiety and perceived risk. Anxiety represents a key factor in determining the hesitancy among university educators. This issue could be ascribed to apprehension regarding the educators digital proficiency, as well as concerns over the potential impact of AI on conventional teaching methodologies, which was comprehensively reviewed by Hao Yu [ 27 , 81 ]. Mitigating the educators’ anxiety can be achieved through targeted training, technological support, and clear guidelines for the ethical and responsible use of ChatGPT, which could lead to its successful integration into educational practices. Furthermore, the perception of risks associated with technological adoption, including ChatGPT appeared to play an important role in its acceptance. The perceptions of risk could stem from concerns regarding data privacy, the potential spread of misinformation, and the negative effects on students’ critical thinking abilities [ 8 , 10 , 16 , 31 ]. Addressing these concerns by establishing ethical standards for ChatGPT use and transparency by AI developer is imperative for enhance trust in ChatGPT. The importance of perceived risks as an important determinant of ChatGPT adoption and use has been recognized as a recurrent result in various recent studies among students, which highlight its central role as a factor determining the attitude to this AI technology in education [ 41 , 42 , 82 – 85 ]. In this study, albeit with a very small sample size, multivariate analysis showed the central role of four Ed-TAME-ChatGPT constructs (perceived risk, social influence, effectiveness, and perceived usefulness), to significantly shape the attitudes to ChatGPT use. This result needs further confirmation in future studies with larger sample size to enable reaching a conclusive and comprehensive evidence regarding the role of these factors among the demographic and academic variables in driving ChatGPT use among university educators. Future research should also involve confirmatory factor analysis to further validate the reliability of the constructs identified in this study. Additionally, we call for open utilization of Ed-TAME-ChatGPT tool to explore the attitude of university educators attitude to ChatGPT. Such an investigation can provide deeper insights into the effective and ethical integration of generative AI technologies in educational settings, contributing to the advancement of AI-enhanced learning. This is particularly important considering the limited number of studies involving university educators. Finally, this study is not without limitations, which should be considered as follows. First, the small sample size could limit the generalizability of the results. The difficulty in reaching a larger sample size could be related to the lengthy nature of this exploratory survey which would cause respondent fatigue [ 86 ]. Second, the potential for selection bias should be considered in light of the participants’ recruitment approach. Utilizing a convenience sampling approach which involved the authors' professional networks may limit the representativeness of university educators included in the study. Third, the restriction of data collection to Jordan could introduce a cultural limitations to the study based on the expected variation in the educational systems, technological infrastructure, and cultural attitudes towards AI across different regions. Consequently, confirmation of the study findings should be considered by conducting future multinational studies using a non-probability sampling approach. CONCLUSIONS The Ed-TAME-ChatGPT survey instrument tested and validated in this study can offer a comprehensive framework for understanding university educators' attitudes towards ChatGPT. The results highlighted the significance of considering perceptions of risks, usefulness, attitudes towards technology, along with anxiety, social influence, and effectiveness when adopting ChatGPT in higher education among university educators. The insights gained by this validated instrument is crucial for higher education institutions, academics, and educational policymakers to strategically facilitate the effective and ethical use of ChatGPT in higher education. It can also aid to identify potential barriers that could hinder the adoption of this transformative AI technology. Future studies in various settings are recommended to confirm and build upon the findings of this study to further elucidate the factors that would influence the successful adoption of ChatGPT in higher educational settings, which would be essential to the advancement of AI-enhanced learning. Abbreviations AI Artificial intelligence ANOVA Analysis of Variance Ed-TAME-ChatGPT Educators attitude to ChatGPT through Edited Technology Acceptance Model EFA Exploratory factor analysis FET Two-sided Fisher’s exact test KMO Kaiser-Meyer-Olkin measure PCA Principal component analysis TAM Technology acceptance model VIF Variance Inflation Factor Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board (IRB) at the Faculty of Pharmacy, Applied Science Private University (Approval number: 2023-PHA-4, on 24 January 2023). E-consent was obtained from all participants. Consent for publication Not applicable. Data Availability Statement The datasets used and/or analysed during the current study are available from the corresponding author (M.S.) on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no funding. Author Contributions Conceptualization: M.S.; Data curation: M.B., N.A.S., M.S.; Formal analysis: M.S.; Investigation: M.B. and M.S.; Methodology: M.B., N.A.S., M.S.; Visualization: M.S.; Project administration: M.B., M.S.; Supervision: M.B., M.S.; Writing - original draft: M.S.; Writing - review & editing: M.B., N.A.S., M.S.; All authors contributed to the article and approved the submitted version. References Grassini S. Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings. Educ Sci. 2023;13(7):692. 10.3390/educsci13070692 . Fütterer T, Fischer C, Alekseeva A, Chen X, Tate T, Warschauer M, et al. ChatGPT in education: global reactions to AI innovations. Sci Rep. 2023;13(1):15310. 10.1038/s41598-023-42227-6 . Kamalov F, Santandreu Calonge D, Gurrib I. New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability. 2023;15(16):12451. 10.3390/su151612451 . Southworth J, Migliaccio K, Glover J, Glover JN, Reed D, McCarty C, et al. Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers Education: Artif Intell. 2023;4:100127. 10.1016/j.caeai.2023.100127 . Sallam M, Salim NA, Al-Tammemi AB, Barakat M, Fayyad D, Hallit S, et al. ChatGPT Output Regarding Compulsory Vaccination and COVID-19 Vaccine Conspiracy: A Descriptive Study at the Outset of a Paradigm Shift in Online Search for Information. Cureus. 2023;15(2):e35029. 10.7759/cureus.35029 . Epub 20230215. Alam A, Mohanty A. Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Eng. 2023;10(2):2283282. 10.1080/23311916.2023.2283282 . Lin Z. Why and how to embrace AI such as ChatGPT in your academic life. R Soc Open Sci. 2023;10(8):230658. 10.1098/rsos.230658 . Epub 20230823. Sallam M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthc (Basel). 2023;11(6):887. 10.3390/healthcare11060887 . Epub 20230319. Michel-Villarreal R, Vilalta-Perdomo E, Salinas-Navarro DE, Thierry-Aguilera R, Gerardou FS. Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT. Educ Sci. 2023;13(9):856. 10.3390/educsci13090856 . Dempere J, Modugu K, Hesham A, Ramasamy LK. The impact of ChatGPT on higher education. Front Educ. 2023;8:1206936. 10.3389/feduc.2023.1206936 . Ahmad SF, Han H, Alam MM, Rehmat MK, Irshad M, Arraño-Muñoz M, et al. Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanit Social Sci Commun. 2023;10(1):311. 10.1057/s41599-023-01787-8 . Kooli C. Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions. Sustainability. 2023;15(7):5614. 10.3390/su15075614 . Sallam M, Salim NA, Barakat M, Al-Tammemi AB. ChatGPT applications in medical, dental, pharmacy, and public health education: A descriptive study highlighting the advantages and limitations. Narra J. 2023;3(1):e103. 10.52225/narra.v3i1.103 . George B, Wooden O. Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sci. 2023;13(9):196. 10.3390/admsci13090196 . Harrer S. Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. eBioMedicine. 2023;90:104512. 10.1016/j.ebiom.2023.104512 . Ray PP. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things Cyber-Physical Syst. 2023;3:121–54. 10.1016/j.iotcps.2023.04.003 . Tlili A, Shehata B, Adarkwah MA, Bozkurt A, Hickey DT, Huang R, et al. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn Environ. 2023;10(1):15. 10.1186/s40561-023-00237-x . Lo CK. What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature. Educ Sci. 2023;13(4):410. 10.3390/educsci13040410 . Chiu TKF. Future research recommendations for transforming higher education with generative AI. Computers Education: Artif Intell. 2024;6:100197. 10.1016/j.caeai.2023.100197 . Crompton H, Burke D. Artificial intelligence in higher education: the state of the field. Int J Educational Technol High Educ. 2023;20(1):22. 10.1186/s41239-023-00392-8 . Arif TB, Munaf U, Ul-Haque I. The future of medical education and research: Is ChatGPT a blessing or blight in disguise? Med Educ Online. 2023;28(1):2181052. 10.1080/10872981.2023.2181052 . Leng L. Challenge, integration, and change: ChatGPT and future anatomical education. Med Educ Online. 2024;29(1):2304973. 10.1080/10872981.2024.2304973 . Epub 20240113. Javaid M, Haleem A, Singh RP, Khan S, Khan IH. Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. BenchCouncil Trans Benchmarks Stand Evaluations. 2023;3(2):100115. 10.1016/j.tbench.2023.100115 . Rahman MM, Watanobe Y. ChatGPT for Education and Research: Opportunities, Threats, and Strategies. Appl Sci. 2023;13(9):5783. 10.3390/app13095783 . Mizumoto A, Eguchi M. Exploring the potential of using an AI language model for automated essay scoring. Res Methods Appl Linguistics. 2023;2(2):100050. 10.1016/j.rmal.2023.100050 . Huang C-W, Coleman M, Gachago D, Van Belle J-P, editors. Using ChatGPT to Encourage Critical AI Literacy Skills and for Assessment in Higher Education. ICT Education; 2024 2024//; Cham: Springer Nature Switzerland. Yu H. The application and challenges of ChatGPT in educational transformation: New demands for teachers' roles. Heliyon. 2024;10(2):e24289. 10.1016/j.heliyon.2024.e24289 . Montenegro-Rueda M, Fernández-Cerero J, Fernández-Batanero JM, López-Meneses E. Impact of the Implementation of ChatGPT in Education: A Systematic Review. Computers. 2023;12(8):153. 10.3390/computers12080153 . Ilieva G, Yankova T, Klisarova-Belcheva S, Dimitrov A, Bratkov M, Angelov D. Effects of Generative Chatbots in Higher Education. Information. 2023;14(9):492. 10.3390/info14090492 . Farrokhnia M, Banihashem SK, Noroozi O, Wals A. A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations Educ Teach Int. 2023;1–15. 10.1080/14703297.2023.2195846 . Sallam M, Al-Farajat A, Egger J. Envisioning the Future of ChatGPT in Healthcare: Insights and Recommendations from a Systematic Identification of Influential Research and a Call for Papers. Jordan Med J. 2024;58(1). Gill SS, Xu M, Patros P, Wu H, Kaur R, Kaur K et al. Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots. Internet of Things and Cyber-Physical Systems. 2024;4:19–23. 10.1016/j.iotcps.2023.06.002 . Mishra P, Koehler MJ. Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers Coll Record. 2006;108(6):1017–54. 10.1111/j.1467-9620.2006.00684.x . Gkrimpizi T, Peristeras V, Magnisalis I. Classification of Barriers to Digital Transformation in Higher Education Institutions: Systematic Literature Review. Educ Sci. 2023;13(7):746. 10.3390/educsci13070746 . Weidener L, Fischer M. Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects. JMIR Med Educ. 2024;10:e51247. 10.2196/51247 . Epub 20240105. Veras M, Dyer JO, Rooney M, Barros Silva PG, Rutherford D, Kairy D. Usability and Efficacy of Artificial Intelligence Chatbots (ChatGPT) for Health Sciences Students: Protocol for a Crossover Randomized Controlled Trial. JMIR Res Protoc. 2023;12:e51873. 10.2196/51873 . Epub 20231124. Luckin R, Cukurova M, Kent C, du Boulay B. Empowering educators to be AI-ready. Computers Education: Artif Intell. 2022;3:100076. 10.1016/j.caeai.2022.100076 . Chan CKY. A comprehensive AI policy education framework for university teaching and learning. Int J Educational Technol High Educ. 2023;20(1):38. 10.1186/s41239-023-00408-3 . Adarkwah MA, Amponsah S, van Wyk MM, Huang R, Tlili A, Shehata B, et al. Awareness and acceptance of ChatGPT as a generative conversational AI for transforming education by Ghanaian academics: A two-phase study. J Appl Learn Teach. 2023;6(2). 10.37074/jalt.2023.6.2.26 . Kiryakova G, Angelova N. ChatGPT—A Challenging Tool for the University Professors in Their Teaching Practice. Educ Sci. 2023;13(10):1056. 10.3390/educsci13101056 . Abdaljaleel M, Barakat M, Alsanafi M, Salim NA, Abazid H, Malaeb D, et al. A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Sci Rep. 2024;14(1):1983. 10.1038/s41598-024-52549-8 . Sallam M, Salim NA, Barakat M, Al-Mahzoum K, Al-Tammemi AB, Malaeb D, et al. Assessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation Study. JMIR Med Educ. 2023;9:e48254. 10.2196/48254 . Epub 20230905. Livberber T, Ayvaz S. The impact of Artificial Intelligence in academia: Views of Turkish academics on ChatGPT. Heliyon. 2023;9(9):e19688. 10.1016/j.heliyon.2023.e19688 . Epub 20230901. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319–40. 10.2307/249008 . Marangunić N, Granić A. Technology acceptance model: a literature review from 1986 to 2013. Univ Access Inf Soc. 2015;14(1):81–95. 10.1007/s10209-014-0348-1 . Malaquias RF, Malaquias FFO, Hwang Y. Understanding technology acceptance features in learning through a serious game. Comput Hum Behav. 2018;87:395–402. 10.1016/j.chb.2018.06.008 . Farahat T. Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia - Social Behav Sci. 2012;64:95–104. 10.1016/j.sbspro.2012.11.012 . Rosli MS, Saleh NS, Md. Ali A, Abu Bakar S, Mohd Tahir L. A Systematic Review of the Technology Acceptance Model for the Sustainability of Higher Education during the COVID-19 Pandemic and Identified Research Gaps. Sustainability. 2022;14(18):11389. 10.3390/su141811389 . Zobeidi T, Homayoon SB, Yazdanpanah M, Komendantova N, Warner LA. Employing the TAM in predicting the use of online learning during and beyond the COVID-19 pandemic. Front Psychol. 2023;14:1104653. 10.3389/fpsyg.2023.1104653 . Epub 20230217. Al-Hattami HM. Understanding perceptions of academics toward technology acceptance in accounting education. Heliyon. 2023;9(1):e13141. 10.1016/j.heliyon.2023.e13141 . Scherer R, Siddiq F, Tondeur J. The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Comput Educ. 2019;128:13–35. 10.1016/j.compedu.2018.09.009 . Granić A, Marangunić N. Technology acceptance model in educational context: A systematic literature review. Br J Edu Technol. 2019;50(5):2572–93. 10.1111/bjet.12864 . Antonietti C, Cattaneo A, Amenduni F. Can teachers’ digital competence influence technology acceptance in vocational education? Comput Hum Behav. 2022;132:107266. 10.1016/j.chb.2022.107266 . Murillo GG, Novoa-Hernández P, Rodríguez RS. Technology Acceptance Model and Moodle: A systematic mapping study. Inform Dev. 2020;37(4):617–32. 10.1177/0266666920959367 . Mailizar M, Almanthari A, Maulina S. Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. Contemp Educational Technol. 2021;13:ep298. 10.30935/cedtech/9709 . Cheung R, Vogel D. Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Comput Educ. 2013;63:160–75. 10.1016/j.compedu.2012.12.003 . Venkatesh V, Morris MG, Davis GB, Davis FD. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003;27(3):425–78. 10.2307/30036540 . MacCallum R, Widaman K, Zhang S, Hong S. Sample Size in Factor Analysis. Psychol Methods - PSYCHOL METHODS. 1999;4:84–99. 10.1037/1082-989X.4.1.84 . Ibrahim H, Liu F, Asim R, Battu B, Benabderrahmane S, Alhafni B, et al. Perception, performance, and detectability of conversational artificial intelligence across 32 university courses. Sci Rep. 2023;13(1):12187. 10.1038/s41598-023-38964-3 . Epub 20230824. Strzelecki A. Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innov High Educ. 2023. 10.1007/s10755-023-09686-1 . von Garrel J, Mayer J. Artificial Intelligence in studies—use of ChatGPT and AI-based tools among students in Germany. Humanit Social Sci Commun. 2023;10(1):799. 10.1057/s41599-023-02304-7 . Faruk L, Rohan R, Nin U, Pal D. University Students’ Acceptance and Usage of Generative AI (ChatGPT) from a Psycho-Technical Perspective. IAIT 2023: 13th International Conference on Advances in Information Technology2023. p. 1–8. Tiwari CK, Bhat MA, Khan ST, Subramaniam R, Khan MAI. What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education. 2023;ahead- of-print(ahead-of-print ). 10.1108/ITSE-04-2023-0061 . Akour A, Al-Tammemi AB, Barakat M, Kanj R, Fakhouri HN, Malkawi A, et al. The Impact of the COVID-19 Pandemic and Emergency Distance Teaching on the Psychological Status of University Teachers: A Cross-Sectional Study in Jordan. Am J Trop Med Hyg. 2020;103(6):2391–9. 10.4269/ajtmh.20-0877 . Epub 20201027. Alqudah I, Barakat M, Muflih SM, Alqudah A. Undergraduates’ perceptions and attitudes towards online learning at Jordanian universities during COVID-19. Interact Learn Environ. 2023;31(9):5781–98. 10.1080/10494820.2021.2018617 . Saleem F, Alnasrallah W, Malik MI, Ur Rehman S. Factors Affecting the Quality of Online Learning During COVID-19: Evidence From a Developing Economy. Front Educ. 2022;7:847571. 10.3389/feduc.2022.847571 . Regmi K, Jones L. A systematic review of the factors – enablers and barriers – affecting e-learning in health sciences education. BMC Med Educ. 2020;20(1):91. 10.1186/s12909-020-02007-6 . Saleh SS, Nat M, Aqel M. Sustainable Adoption of E-Learning from the TAM Perspective. Sustainability. 2022;14(6):3690. 10.3390/su14063690 . Alassafi MO. E-learning intention material using TAM: A case study. Mater Today Proc. 2022;61:873-7. Epub 20211028. 10.1016/j.matpr.2021.09.457 . Tawafak RM, Al-Rahmi WM, Almogren AS, Al Adwan MN, Safori A, Attar RW, et al. Analysis of E-Learning System Use Using Combined TAM and ECT Factors. Sustainability. 2023;15(14):11100. 10.3390/su151411100 . Chahal J, Rani N. Exploring the acceptance for e-learning among higher education students in India: combining technology acceptance model with external variables. J Comput High Educ. 2022;34(3):844–67. 10.1007/s12528-022-09327-0 . Mohammed B, Mohammad Amin K, Rawad H. Embracing Technological Change in Higher Education. In: Lee W, Sharon W, editors. Higher Education. Rijeka: IntechOpen; 2021. Ch. 12. Nguyen B, Simkin L. The Internet of Things (IoT) and marketing: the state of play, future trends and the implications for marketing. J Mark Manage. 2017;33(1–2):1–6. 10.1080/0267257X.2016.1257542 . Al-Adwan AS, Li N, Al-Adwan A, Abbasi GA, Albelbisi NA, Habibi A. Extending the Technology Acceptance Model (TAM) to Predict University Students’ Intentions to Use Metaverse-Based Learning Platforms. Educ Inform Technol. 2023;28(11):15381–413. 10.1007/s10639-023-11816-3 . Wang C, Ahmad SF, Bani Ahmad Ayassrah AYA, Awwad EM, Irshad M, Ali YA, et al. An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon. 2023;9(8):e18349. 10.1016/j.heliyon.2023.e18349 . Rejeb A, Rejeb K, Appolloni A, Treiblmaier H, Iranmanesh M. Exploring the impact of ChatGPT on education: A web mining and machine learning approach. Int J Manage Educ. 2024;22(1):100932. 10.1016/j.ijme.2024.100932 . Ruiz-Rojas LI, Acosta-Vargas P, De-Moreta-Llovet J, Gonzalez-Rodriguez M. Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix. Sustainability. 2023;15(15):11524. 10.3390/su151511524 . Yilmaz R, Karaoglan Yilmaz FG. The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Computers Education: Artif Intell. 2023;4:100147. 10.1016/j.caeai.2023.100147 . Rahiman HU, Kodikal R. Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Educ. 2024;11(1):2293431. 10.1080/2331186X.2023.2293431 . Blut M, Wang C. Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. J Acad Mark Sci. 2020;48(4):649–69. 10.1007/s11747-019-00680-8 . Hadi Mogavi R, Deng C, Kim J, Zhou P, Kwon YD, Metwally A et al. Exploring User Perspectives on ChatGPT: Applications, Perceptions, and Implications for AI-Integrated Education. arXiv. 2023. 10.48550/arXiv.2305.13114 . Albayati H. Investigating undergraduate students' perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Computers Education: Artif Intell. 2024;6:100203. 10.1016/j.caeai.2024.100203 . Jo H, Bang Y. Analyzing ChatGPT adoption drivers with the TOEK framework. Sci Rep. 2023;13(1):22606. 10.1038/s41598-023-49710-0 . Menon D, Shilpa K. Chatting with ChatGPT: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model. Heliyon. 2023;9(11):e20962. 10.1016/j.heliyon.2023.e20962 . Sallam M, Elsayed W, Al-Shorbagy M, Barakat M, Khatib EL, Ghach S et al. W,. ChatGPT Usage and Attitudes are Driven by Perceptions of Usefulness, Ease of Use, Risks, and Psycho-Social Impact: A Study among University Students in the UAE. Research Square. 2024. 10.21203/rs.3.rs-3905717/v1 . Jeong D, Aggarwal S, Robinson J, Kumar N, Spearot A, Park DS. Exhaustive or exhausting? Evidence on respondent fatigue in long surveys. J Dev Econ. 2023;161:102992. 10.1016/j.jdeveco.2022.102992 . Additional Declarations No competing interests reported. Supplementary Files TablesS1.docx 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3919524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271148812,"identity":"10f599c2-464d-46bb-906b-95af0c8b35e0","order_by":0,"name":"Muna Barakat","email":"","orcid":"","institution":"Applied Science Private University","correspondingAuthor":false,"prefix":"","firstName":"Muna","middleName":"","lastName":"Barakat","suffix":""},{"id":271148813,"identity":"2bcc7c0b-fdb2-47d9-9562-ad1b87294965","order_by":1,"name":"Nesreen A. Salim","email":"","orcid":"","institution":"University of Jordan","correspondingAuthor":false,"prefix":"","firstName":"Nesreen","middleName":"A.","lastName":"Salim","suffix":""},{"id":271148814,"identity":"db999134-f9eb-475d-ac2c-6ccc150105d8","order_by":2,"name":"Malik Sallam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFAC5oaDDUCKjx1IfOABiSQwMEjg1cII0cLGDGTOIFYLI0wLM1gHSAs+oNt+sPHgDIY6eTZm5qebbWQOM/Cz5xgwWFTg1mJ2JrHh4AaGw4ZtzGxmt3N4DjNI9rwxYJA4g0fLAaCWBwwHGNuYGSBaDG4AbZFsw6Pl/EOQljr7Nmb2b7ctgFrsCWq5AXYYc2IbM4/ZbQaQLRIEtQBtmWFwOBmopexmD086j8SZZwUH8PrlfPLhjz0Vdbb97O3bbvzssZbjb0/e+FgCT4hBgAGUZuxhAEfNYfxRiQJ+QLV+IF7LKBgFo2AUDH8AABJwUQkyrsQlAAAAAElFTkSuQmCC","orcid":"","institution":"University of Jordan","correspondingAuthor":true,"prefix":"","firstName":"Malik","middleName":"","lastName":"Sallam","suffix":""}],"badges":[],"createdAt":"2024-02-02 04:14:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3919524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3919524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50708339,"identity":"89e3ad1f-aa3d-41cf-aae7-77beb862de79","added_by":"auto","created_at":"2024-02-06 06:50:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68812,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScree plot representing the eigenvalues of the factors identified through principal component analysis\u003c/strong\u003e. The red line indicated the eigenvalue cutoff specified at 1.50.\u003c/p\u003e","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3919524/v1/9aa07d54c3e51969d65a5f5e.png"},{"id":50708802,"identity":"17250c03-37e2-4783-96b5-4f8273e4db20","added_by":"auto","created_at":"2024-02-06 06:58:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":608041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe final Ed-TAME-ChatGPT items and constructs\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3919524/v1/a4e4a486db879d8490b8d382.png"},{"id":58338823,"identity":"e77d38e4-ef24-4ddb-96eb-0fecc1c01800","added_by":"auto","created_at":"2024-06-14 06:16:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1990908,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3919524/v1/5ed2bb56-cb56-49a5-a8a2-c6d0811301e3.pdf"},{"id":50708338,"identity":"e8907274-600a-4226-9546-625599b42bc2","added_by":"auto","created_at":"2024-02-06 06:50:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15818,"visible":true,"origin":"","legend":"","description":"","filename":"TablesS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3919524/v1/082f655442cfb65dcaf0f601.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Perspectives of University Educators Regarding ChatGPT: A Validation Study Based on the Technology Acceptance Model","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eHigher education is set for a significant transformation with the impending integration of generative artificial intelligence (AI) as a routine practice among university students and educators [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This technological revolution represents a paradigm shifting concept regarding knowledge acquisition and dissemination, especially in the domain of higher education [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Generative AI as exemplified by widely used models such as ChatGPT (OpenAI, San Francisco, CA), can revolutionize teaching and learning methodologies in addition to its potential utility in expediting academic research [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nevertheless, it is important to fully understand the implications of integrating ChatGPT among other generative AI models in higher education from the perspective of educational institutions, university educators, and students [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This comes in light of the well-recognized and described limitations and failures of such AI models including concerns regarding bias, inaccuracies, cybersecurity risk as well as the threats of declining critical thinking and analytical skills among students in addition to the potential job losses [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, the generative AI models including ChatGPT can offer several benefits in higher education [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These AI models can engage students and educators in interactive, intelligent conversations while adapting to individual needs; thus, offering personalized learning experiences [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This personalized experience allows for the generation of educational content customized to varying students\u0026rsquo; learning speeds, styles, and interests, making education an efficient and interesting experience [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For educators, ChatGPT can be a powerful tool for organizing study materials and rapidly generating a variety of examples and explanations across different subjects [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This capability can aid educators in simplifying complex concepts for students [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, generative AI models including ChatGPT hold promising potential for automating and standardizing the scoring of essays, potentially enhancing the efficiency and consistency of evaluations in various educational settings [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, generative AI models can facilitate the digitalization of education and enhance the pedagogical methods\u0026rsquo; efficiency and quality [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis and a recent review of ChatGPT in education revealed the future trajectory of the needed research regarding ChatGPT utility in education [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As stated earlier, the strengths of ChatGPT can be related to enhanced learning outcomes and improved resource efficiency [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The ChatGPT challenges include deepening the digital divide, overreliance on technology, and concerns about the quality of generated content [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Opportunities provided by ChatGPT arise through innovative pedagogies, while threats include ethical issues and data privacy issues [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Thus, the successful integration of ChatGPT in higher education mandates balancing ChatGPT benefits against its challenges.\u003c/p\u003e \u003cp\u003eThe integration of a novel technology in higher education necessitates understanding the perspectives of stakeholders, namely university students and educators alike [\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In particular, educators\u0026rsquo; attitudes, concerns, and readiness to embrace novel AI technologies significantly influence their adoption and effectiveness in teaching and learning environments [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, it is essential to assess university educators\u0026rsquo; perspectives on novel AI tool such as ChatGPT which would be important for the successful implementation of these tools in higher education [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur previous work involved two studies that focused on designing, validating, and confirming the validity of a survey instrument based on the Technology Acceptance Model (TAM) among students especially in health schools [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These studies showed that perceived usefulness, ease of use, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks were linked with positive attitudes to ChatGPT [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, to the best of our knowledge very few studies have focused on the perspective of university educators [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study was to establish and test the validity of a TAM-based construct for understanding the acceptance and use of ChatGPT among academic personnel in Jordan. By establishing a survey instrument specifically tailored towards the perspectives of university educators, the implementation of ChatGPT in higher education can be guided by evidence. Thus, we aimed to develop a valid survey tool to guide the effective and responsible integration of ChatGPT in higher education settings as well as to highlight the feared risks and concerns among university educators towards this inevitable technology. This approach is particularly relevant considering the focus of the previous studies on university students, with university educators being a relevant yet an understudied group.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThe development of the survey instrument began with an extensive literature review that focused on the application of the TAM in understanding the acceptance of novel technologies by students and educators based on the original TAM framework [\u003cspan additionalcitationids=\"CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFollowing the literature review, initial item development in the English language was performed by the authors (university educators with expertise in three diverse healthcare disciplines and with previous experience in survey construction and validation). To ensure content validity, the three authors engaged in an internal discussion with a subjective evaluation of the initial survey items in terms of clarity, comprehensiveness, and relevance as well as to identify and address any potential biases or issues in the wording of the items, such as vagueness or complexity. This internal discussion led to the identification of potential domains for inclusion in the final questionnaire comprising 40 items as follows: perceived usefulness, perceived ease of use, perceived risks, anxiety, attitude towards the technology, social influence, and effectiveness. This was followed by forward translation into Arabic and backward translation into English of the 40 items, which were done by the three bilingual authors with minor modifications. Then, pilot testing of the items in Arabic was done to ensure clarity among four university educators who were excluded from the final analysis. Based on the feedback received, minor language modifications were made to enhance the overall clarity of the survey items. The construct validity of the scale herein termed \u0026ldquo;Ed-TAME-ChatGPT\u0026rdquo; was checked following survey distribution, using 40 TAM-based items evaluated among the respondents who used ChatGPT before the study. The minimum sample size was estimated at approximately 80 participants based on the previous guidelines of having at least two study subjects per survey item for survey validation [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e This study was approved by the Institutional Review Board (IRB) at the Faculty of Pharmacy, Applied Science Private University (Approval number: 2023-PHA-4, on 24 January 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eComponents of the Finalized Questionnaire and its Distribution\u003c/h2\u003e \u003cp\u003eThe finalized survey in the Arabic language was uploaded to Google Forms and distributed using a convenience sampling approach. This involved reaching out to the professional contacts of the authors, requesting them to share the survey within their networks and encouraging further distribution. Participation in the survey was voluntary and there was no incentive for participation.\u003c/p\u003e \u003cp\u003eThe questionnaire began with a detailed introduction outlining the study objectives, accompanied by a mandatory electronic consent (e-consent) item, which was mandatory for the participants to proceed. This introductory section explicitly assured participants of their complete anonymity and privacy.\u003c/p\u003e \u003cp\u003eThis was followed by the demographic section with items to assess the following variables: (1) age; (2) sex; (3) nationality (Jordanian vs. non-Jordanian); School/College (health [medicine, dentistry, pharmacy, nursing, rehabilitation sciences, applied medical sciences, veterinary medicine, public health] vs. scientific [science, engineering, agriculture, information technology, nanotechnology, artificial intelligence] vs. humanities [Arts, Business, Sharia, Law, Islamic Studies, Physical Education, Arts, Languages, Tourism, Media, Archaeology]); (4) university [public vs. private]; (5) the highest educational degree obtained (PhD or fellowship degree vs. MSc or a specialty degree); (6) the country where the highest educational degree was attained (Jordan vs. other Arab country vs. USA vs. UK vs. EU vs. others); and (7) rank (full professor, associate professor [grouped as \u0026ldquo;tenured\u0026rdquo;] vs. assistant professor, lecturer, lecturer assistant [grouped as \u0026ldquo;without tenure\u0026rdquo; ]).\u003c/p\u003e \u003cp\u003eThe next section involved two screening items as follows: First, \u0026ldquo;Have you heard of ChatGPT before the study?\u0026rdquo;, with \u0026ldquo;yes\u0026rdquo; response required to move into the next item, while the answer of \u0026ldquo;no\u0026rdquo; resulted in survey submission. The next item was \u0026ldquo;Have you used ChatGPT before the study?\u0026rdquo; with \u0026ldquo;yes\u0026rdquo; resulting in the presentation of the full 40 TAM-based items.\u003c/p\u003e \u003cp\u003eThe final section involved the presentation of the 40 TAM-based items, with each item assessed on a 5-point Likert-scale with the following responses: strongly agree scored as 5, agree scored as 4, neutral/no opinion scored as 3, disagree scored as 2, and strongly disagree scored as 1. The scoring was reversed for the items implying negative attitude towards ChatGPT. The complete phrasing of the included items is presented in (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical and Data Analyses\u003c/h2\u003e \u003cp\u003eThe statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 26.0 (Armonk, NY, USA: IBM Corp).\u003c/p\u003e \u003cp\u003eExploratory factor analysis (EFA) was employed to investigate the factorial structure of the Ed-TAME-ChatGPT scale which comprised a total of the 40 items. Principal component analysis (PCA) was used as the extraction method, with oblimin rotation to determine correlations between factors. The Kaiser-Meyer-Olkin (KMO) measure and Bartlett\u0026rsquo;s test of sphericity were applied to evaluate the data appropriateness for EFA.\u003c/p\u003e \u003cp\u003eSubsequently, the internal consistency of the Ed-TAME-ChatGPT constructs inferred following EFA was assessed using the Cronbach\u0026rsquo;s α values. Scores for each construct were computed by dividing the total scores by the number of items within that construct, yielding a score range of 1\u0026ndash;5. The overall Ed-TAME-ChatGPT scores were computed based on the overall average of Ed-TAME-ChatGPT involving the items of each construct. The scoring classification for both the TAME-ChatGPT and its individual constructs was categorized as follows: scores ranging from 1.00 to 2.33 indicated disagreement (negative), 2.34 to 3.67 indicated a neutral position, and 3.68 to 5.00 indicated agreement (positive).\u003c/p\u003e \u003cp\u003eAssociations between categorical variables were assessed using the χ\u003csup\u003e2\u003c/sup\u003e (chi-square) and the two-sided Fisher\u0026rsquo;s exact (FET) tests. Univariate analyses were conducted to assess the associations between Ed-TAME-ChatGPT and the demographic variables as well as the individualized constructs with statistical significance for \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.050. For multivariate analyses, predictor variables were selected based on a \u003cem\u003eP\u003c/em\u003e value of \u0026lt;\u0026thinsp;.100 in univariate analyses. Linear regression with Analysis of Variance (ANOVA) was employed to test the significance of the regression model, and multicollinearity was assessed using the Variance Inflation Factor (VIF), with a threshold value of 3.0 indicating potential concerns.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Sample Features\u003c/h2\u003e \u003cp\u003eThe final sample consisted of 236 educators, of whom 169 heard of ChatGPT before the study (71.6%), with only 76 who indicated the use of ChatGPT before the study (45.0% of those who heard of ChatGPT).\u003c/p\u003e \u003cp\u003eThe vast majority of the study sample were Jordanians (98.3%), and a majority obtained their highest educational degree outside the Arab countries. The full features of the study sample are illustrated in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe percentage of those who used ChatGPT increased significantly over the study period from 33/99 during February 2023\u0026ndash;April 2023 (43.4%) to 43/70 during October 2023 (56.6%. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, FET).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral features of educators who participated in the study (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;236).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;43 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;43 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJordanian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Jordanian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScientific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumanities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest educational degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry of highest attained degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJordan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArab country other than Jordan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry of highest attained degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Arab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate Professor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssistant Professor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLecturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeaching assistant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith tenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout tenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you heard of ChatGPT before the study?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you used ChatGPT before the study?\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eAmong those who heard of ChatGPT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFactorability of the Correlation Matrix\u003c/h2\u003e \u003cp\u003eThe EFA was conducted on a set of 40 items to identify underlying constructs that account for the variance in the responses. Bartlett\u0026rsquo;s test of sphericity was significant (χ\u0026sup2;= 2226.833, df\u0026thinsp;=\u0026thinsp;780, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating the factorability of the correlation matrix. The KMO measure of sampling adequacy was .698, indicating that the data were suitable for factorial analysis.\u003c/p\u003e \u003cp\u003eThe scree plot showed that the optimal number of underlying factors was six based on the eigenvalue cutoff specified at 1.5, which explained a cumulative total of 63.7% of the variance in the data as indicated in the scree plot (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1. Scree plot representing the eigenvalues of the factors identified through principal component analysis\u003c/b\u003e. The red line indicated the eigenvalue cutoff specified at 1.50.\u003c/p\u003e \u003cp\u003eThe eigenvalues for the six factors were 11.702, 4.964, 3.244, 2.148, 1.893, and 1.545, respectively. A total of 31 items out of the 40 tested items loaded significantly with absolute coefficient values\u0026thinsp;\u0026gt;\u0026thinsp;0.50 on one of the six factors, and factor loadings ranging from absolute 0.515 to absolute 0.848.\u003c/p\u003e \u003cp\u003eBased on the content of the items for each factor, these factors were named based on the original TAM as follows: Factor 1 was named \u0026ldquo;effectiveness\u0026rdquo; comprising 6 items (T3, T4, T6, T16, T17, T18) with a Cronbach alpha value of .845. Factor 2 was named \u0026ldquo;anxiety\u0026rdquo; comprising 5 items (T36, T37, T38, T39, T40) with a Cronbach alpha value of .862. Factor 3 was named \u0026ldquo;technology readiness\u0026rdquo; comprising 5 items (T31, T32, T33, T34, T35) with a Cronbach alpha value of .885. Factor 4 was termed \u0026ldquo;perceived usefulness\u0026rdquo; comprising 5 items (T1, T7, T8, T9, T10) with a Cronbach alpha value of .848. Factor 5 was termed \u0026ldquo;social influence\u0026rdquo; comprising 4 items (T22, T23, T24, T25) with a Cronbach alpha value of .803. Finally, factor 6 was termed \u0026ldquo;perceived risk\u0026rdquo; comprising 6 items (T19, T20, T26, T27, T29, T30) with a Cronbach alpha value of .796. The final educators TAME-ChatGPT (Ed-TAME-ChatGPT) validated items and constructs are illustrated in (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2. The final Ed-TAME-ChatGPT items and constructs\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAttitudes of the Participating Educators to ChatGPT based on Ed-TAME-ChatGPT and the demographic/academic variables\u003c/h2\u003e \u003cp\u003eUnivariate analysis revealed that only Ed-TAME-ChatGPT constructs were significantly associated with the overall Ed-TAME ChatGPT scores (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, academic, and Ed-TAME-ChatGPT constructs\u0026rsquo; association with the overall Ed-TAME-ChatGPT scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTAME categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCount (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value, χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;43 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.767, .088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;43 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.970, .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJordanian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.054, 3.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Jordanian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.551, 1.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScientific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumanities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.251, 1.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.302, 1.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry of highest attained degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Arab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.510, .433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith tenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.603, .270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout tenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived usefulness categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001, 21.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffectiveness categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001, 21.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (96.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial influence categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001, 22.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived risk categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001, 15.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology readiness categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.048, 6.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.007, 9.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn multivariate analysis with the variables having \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.100 in univariate analysis, the ANOVA for the regression model yielded an F statistic of 14.343 with a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating the model robustness. The model, with an adjusted R\u003csup\u003e2\u003c/sup\u003e of 0.555 and a standard error of 0.322, identified several significant predictors. Nationality (β\u0026thinsp;=\u0026thinsp;0.138, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.084), perceived usefulness (β\u0026thinsp;=\u0026thinsp;0.198, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.030), and effectiveness (β\u0026thinsp;=\u0026thinsp;0.240, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011) positively influenced ChatGPT usage. Social influence was also a positive predictor (β\u0026thinsp;=\u0026thinsp;0.260, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004). In contrast, perceived risk emerged as a negative predictor (β=\u0026minus;0.320, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). However, technology readiness (β\u0026thinsp;=\u0026thinsp;0.108, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.236) and anxiety (β=\u0026minus;0.104, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.245) were not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression analysis of the predictors influencing ChatGPT usage based on the Ed-TAME-ChatGPT constructs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCoefficients\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.555, SE\u0026thinsp;=\u0026thinsp;0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANOVA F statistic\u0026thinsp;=\u0026thinsp;14.343, \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived usefulness categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffectiveness categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial influence categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived risk categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;3.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology readiness categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eDependent Variable: Overall Ed-TAME-ChatGPT usage score; \u003csup\u003e2\u003c/sup\u003eSE: Standard error; \u003csup\u003e3\u003c/sup\u003eVIF: Variance inflation factor. Statistically significant \u003cem\u003eP\u003c/em\u003e values are highlighted in bold style.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the present study we explored the validity of a novel survey instrument specifically designed to assess the attitude to ChatGPT among university educators. Throughout the study period, a notable increase in the number of university academics who self-reported the use of ChatGPT was observed. This finding is indicative of ChatGPT\u0026rsquo;s growing popularity as a tool in higher education among students and academics alike as indicated by recent studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan additionalcitationids=\"CR60 CR61 CR62\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The trend of increasing ChatGPT usage suggests the recognition of generative AI potential as a valuable aid in teaching and learning processes. This shift can also be indicative of an impending broader transformation in educational methodologies manifested in increasing digitization and incorporation of AI in higher educational settings.\u003c/p\u003e \u003cp\u003eTo reach reliable conclusions regarding university educators' attitudes towards ChatGPT, it is imperative to pursue such an aim using validated methodologies. This approach is necessary to ensure that the investigation of such an aim accurately achieve a comprehensive understanding of the motivators and barriers that could enhance or preclude the integration of generative AI in higher education. One popular, practical, and highly relevant framework that was used in this study is the TAM, with its suitable applicability to assess educators\u0026rsquo; adoption of a novel technology shown in a meta-analysis by Scherer et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, six constructs were identified based on the TAM and appear to explain a substantial degree of the educators\u0026rsquo; attitude towards ChatGPT. These constructs were effectiveness, anxiety, technology readiness, perceived usefulness, social influence, and perceived risk. Reflection on these TAM-based constructs suggests the relevance of psychological, social, and practical factors that influence the adoption and integration of new technologies such as ChatGPT among educators in higher educational settings. The identification of these factors highlights the complex nature of the process of AI integration into higher education, which was previously shown in various similar contexts. For example, several factors were identified to severely affect the quality of distance-based online learning particularly during COVID-19 pandemic such as unreliable internet availability, the lack of motivation, psychological distress, and institutional support [\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. A systematic review by Regmi \u0026amp; Jones identified highlighted the complexity of e-learning in health sciences education in terms of challenges such as poor motivation, the need for resources and relevance of information technology (IT) skills [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Under the lens of TAM, the acceptance and adoption of previous digital education focused mainly on the students\u0026rsquo; perspectives and identified several constructs were identified as relevant determinants of attitudes to such technologies such as the perceived usefulness, perceived ease of use, self-efficacy, behavioral intention, social influences, among others [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan additionalcitationids=\"CR69 CR70\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the current study, the perceived usefulness emerged as a significant predictor of attitude towards ChatGPT among the participating university educators. This result is understandable based on the previous evidence showing the greater likelihood of embracing a novel technology if it is perceived as beneficial [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], which was recently shown by Wang et al. in the context of AI adoption in e-commerce [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Usefulness in the context of generative AI such as ChatGPT pertains to its potential to enhance learning outcomes, to facilitate personalized education, and to streamline the tedious administrative tasks [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Thus, understanding and addressing the university educators\u0026rsquo; perceptions of ChatGPT usefulness appears critical for the acceptance and adoption of ChatGPT among this relevant group in higher education.\u003c/p\u003e \u003cp\u003eIn this validation study, effectiveness emerged as an important construct reflecting the evident impact of perceived technology effectiveness on educational objectives. The anticipation of significant benefits from generative AI such as ChatGPT in enhancing student engagement and interaction with academic tasks can play a crucial role in creating a positive attitude towards it among university educators [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Additionally, the expectation that ChatGPT can enhance the overall quality of education, is manifested in higher perceived effectiveness which would be regarded as an important factor for adopting ChatGPT to achieve academic excellence.\u003c/p\u003e \u003cp\u003eSocial influence was identified as an important construct expected to play a significant role in shaping university educators' acceptance of ChatGPT. This construct captures the influence of peer opinions, institutional culture, and prevailing educational trends on the decision-making process regarding the adoption of new technologies such as ChatGPT. Endorsements of adoption generative AI from colleagues and the broader academic community appeared to play an important role in shaping university educators' willingness to acceptance and utilize ChatGPT in educational settings. Therefore, it is important to recognize and consider the role of social influence in facilitating the widespread adoption of generative AI technologies in higher education. The role of perceiving AI as a social norm was revealed in a recent study by Rahiman \u0026amp; Kodikal among academic staff members in Asia, which further supports the identification of social influence as a construct in this study [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTechnology readiness was the fourth key construct identified in this study in relation to ChatGPT usage among university educators. This construct involved the assessment of university educators\u0026rsquo; willingness and capacity to adopt and effectively use novel technological tools. Facilitating technology readiness involves providing necessary resources, training, and support, which would enable the university educators to confidently integrate ChatGPT into their teaching practices. A meta-analysis on the technology readiness impact on the use of technology highlighted the importance of considering the motivators (optimism and innovativeness) and inhibitors (insecurity and discomfort) for further dissecting this important factor in driving the adoption and use of novel technologies [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConversely, the barriers to ChatGPT use as identified in the current study were represented by two constructs namely the anxiety and perceived risk. Anxiety represents a key factor in determining the hesitancy among university educators. This issue could be ascribed to apprehension regarding the educators digital proficiency, as well as concerns over the potential impact of AI on conventional teaching methodologies, which was comprehensively reviewed by Hao Yu [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Mitigating the educators\u0026rsquo; anxiety can be achieved through targeted training, technological support, and clear guidelines for the ethical and responsible use of ChatGPT, which could lead to its successful integration into educational practices.\u003c/p\u003e \u003cp\u003eFurthermore, the perception of risks associated with technological adoption, including ChatGPT appeared to play an important role in its acceptance. The perceptions of risk could stem from concerns regarding data privacy, the potential spread of misinformation, and the negative effects on students\u0026rsquo; critical thinking abilities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Addressing these concerns by establishing ethical standards for ChatGPT use and transparency by AI developer is imperative for enhance trust in ChatGPT. The importance of perceived risks as an important determinant of ChatGPT adoption and use has been recognized as a recurrent result in various recent studies among students, which highlight its central role as a factor determining the attitude to this AI technology in education [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan additionalcitationids=\"CR83 CR84\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, albeit with a very small sample size, multivariate analysis showed the central role of four Ed-TAME-ChatGPT constructs (perceived risk, social influence, effectiveness, and perceived usefulness), to significantly shape the attitudes to ChatGPT use. This result needs further confirmation in future studies with larger sample size to enable reaching a conclusive and comprehensive evidence regarding the role of these factors among the demographic and academic variables in driving ChatGPT use among university educators.\u003c/p\u003e \u003cp\u003eFuture research should also involve confirmatory factor analysis to further validate the reliability of the constructs identified in this study. Additionally, we call for open utilization of Ed-TAME-ChatGPT tool to explore the attitude of university educators attitude to ChatGPT. Such an investigation can provide deeper insights into the effective and ethical integration of generative AI technologies in educational settings, contributing to the advancement of AI-enhanced learning. This is particularly important considering the limited number of studies involving university educators.\u003c/p\u003e \u003cp\u003eFinally, this study is not without limitations, which should be considered as follows. First, the small sample size could limit the generalizability of the results. The difficulty in reaching a larger sample size could be related to the lengthy nature of this exploratory survey which would cause respondent fatigue [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Second, the potential for selection bias should be considered in light of the participants\u0026rsquo; recruitment approach. Utilizing a convenience sampling approach which involved the authors' professional networks may limit the representativeness of university educators included in the study. Third, the restriction of data collection to Jordan could introduce a cultural limitations to the study based on the expected variation in the educational systems, technological infrastructure, and cultural attitudes towards AI across different regions. Consequently, confirmation of the study findings should be considered by conducting future multinational studies using a non-probability sampling approach.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe Ed-TAME-ChatGPT survey instrument tested and validated in this study can offer a comprehensive framework for understanding university educators' attitudes towards ChatGPT. The results highlighted the significance of considering perceptions of risks, usefulness, attitudes towards technology, along with anxiety, social influence, and effectiveness when adopting ChatGPT in higher education among university educators. The insights gained by this validated instrument is crucial for higher education institutions, academics, and educational policymakers to strategically facilitate the effective and ethical use of ChatGPT in higher education. It can also aid to identify potential barriers that could hinder the adoption of this transformative AI technology. Future studies in various settings are recommended to confirm and build upon the findings of this study to further elucidate the factors that would influence the successful adoption of ChatGPT in higher educational settings, which would be essential to the advancement of AI-enhanced learning.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEd-TAME-ChatGPT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEducators attitude to ChatGPT through Edited Technology Acceptance Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExploratory factor analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTwo-sided Fisher\u0026rsquo;s exact test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKMO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaiser-Meyer-Olkin measure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTechnology acceptance model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariance Inflation Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board (IRB) at the Faculty of Pharmacy, Applied Science Private University (Approval number: 2023-PHA-4, on 24 January 2023). E-consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author (M.S.) on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e M.S.; \u003cstrong\u003eData curation:\u003c/strong\u003e M.B., N.A.S., M.S.; \u003cstrong\u003eFormal analysis:\u003c/strong\u003e M.S.; \u003cstrong\u003eInvestigation:\u003c/strong\u003e M.B. and M.S.; \u003cstrong\u003eMethodology:\u003c/strong\u003e M.B., N.A.S., M.S.; \u003cstrong\u003eVisualization:\u003c/strong\u003e M.S.; \u003cstrong\u003eProject administration:\u003c/strong\u003e M.B., M.S.; \u003cstrong\u003eSupervision:\u003c/strong\u003e M.B., M.S.; \u003cstrong\u003eWriting - original draft:\u003c/strong\u003e M.S.; \u003cstrong\u003eWriting - review \u0026amp; editing:\u003c/strong\u003e M.B., N.A.S., M.S.; \u003cstrong\u003eAll authors contributed to the article and approved the submitted version.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGrassini S. Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings. Educ Sci. 2023;13(7):692. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/educsci13070692\u003c/span\u003e\u003cspan address=\"10.3390/educsci13070692\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF\u0026uuml;tterer T, Fischer C, Alekseeva A, Chen X, Tate T, Warschauer M, et al. ChatGPT in education: global reactions to AI innovations. Sci Rep. 2023;13(1):15310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-42227-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-42227-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamalov F, Santandreu Calonge D, Gurrib I. New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability. 2023;15(16):12451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/su151612451\u003c/span\u003e\u003cspan address=\"10.3390/su151612451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouthworth J, Migliaccio K, Glover J, Glover JN, Reed D, McCarty C, et al. Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers Education: Artif Intell. 2023;4:100127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.caeai.2023.100127\u003c/span\u003e\u003cspan address=\"10.1016/j.caeai.2023.100127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSallam M, Salim NA, Al-Tammemi AB, Barakat M, Fayyad D, Hallit S, et al. ChatGPT Output Regarding Compulsory Vaccination and COVID-19 Vaccine Conspiracy: A Descriptive Study at the Outset of a Paradigm Shift in Online Search for Information. Cureus. 2023;15(2):e35029. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.35029\u003c/span\u003e\u003cspan address=\"10.7759/cureus.35029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20230215.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam A, Mohanty A. Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Eng. 2023;10(2):2283282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/23311916.2023.2283282\u003c/span\u003e\u003cspan address=\"10.1080/23311916.2023.2283282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Z. Why and how to embrace AI such as ChatGPT in your academic life. R Soc Open Sci. 2023;10(8):230658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rsos.230658\u003c/span\u003e\u003cspan address=\"10.1098/rsos.230658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20230823.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSallam M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthc (Basel). 2023;11(6):887. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/healthcare11060887\u003c/span\u003e\u003cspan address=\"10.3390/healthcare11060887\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20230319.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichel-Villarreal R, Vilalta-Perdomo E, Salinas-Navarro DE, Thierry-Aguilera R, Gerardou FS. Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT. Educ Sci. 2023;13(9):856. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/educsci13090856\u003c/span\u003e\u003cspan address=\"10.3390/educsci13090856\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDempere J, Modugu K, Hesham A, Ramasamy LK. The impact of ChatGPT on higher education. Front Educ. 2023;8:1206936. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/feduc.2023.1206936\u003c/span\u003e\u003cspan address=\"10.3389/feduc.2023.1206936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad SF, Han H, Alam MM, Rehmat MK, Irshad M, Arra\u0026ntilde;o-Mu\u0026ntilde;oz M, et al. Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanit Social Sci Commun. 2023;10(1):311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1057/s41599-023-01787-8\u003c/span\u003e\u003cspan address=\"10.1057/s41599-023-01787-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKooli C. Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions. Sustainability. 2023;15(7):5614. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/su15075614\u003c/span\u003e\u003cspan address=\"10.3390/su15075614\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSallam M, Salim NA, Barakat M, Al-Tammemi AB. ChatGPT applications in medical, dental, pharmacy, and public health education: A descriptive study highlighting the advantages and limitations. Narra J. 2023;3(1):e103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.52225/narra.v3i1.103\u003c/span\u003e\u003cspan address=\"10.52225/narra.v3i1.103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorge B, Wooden O. Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sci. 2023;13(9):196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/admsci13090196\u003c/span\u003e\u003cspan address=\"10.3390/admsci13090196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrer S. Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. eBioMedicine. 2023;90:104512. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2023.104512\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2023.104512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay PP. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things Cyber-Physical Syst. 2023;3:121\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.iotcps.2023.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.iotcps.2023.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTlili A, Shehata B, Adarkwah MA, Bozkurt A, Hickey DT, Huang R, et al. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn Environ. 2023;10(1):15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40561-023-00237-x\u003c/span\u003e\u003cspan address=\"10.1186/s40561-023-00237-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLo CK. What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature. Educ Sci. 2023;13(4):410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/educsci13040410\u003c/span\u003e\u003cspan address=\"10.3390/educsci13040410\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiu TKF. Future research recommendations for transforming higher education with generative AI. Computers Education: Artif Intell. 2024;6:100197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.caeai.2023.100197\u003c/span\u003e\u003cspan address=\"10.1016/j.caeai.2023.100197\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrompton H, Burke D. Artificial intelligence in higher education: the state of the field. Int J Educational Technol High Educ. 2023;20(1):22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s41239-023-00392-8\u003c/span\u003e\u003cspan address=\"10.1186/s41239-023-00392-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArif TB, Munaf U, Ul-Haque I. The future of medical education and research: Is ChatGPT a blessing or blight in disguise? Med Educ Online. 2023;28(1):2181052. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10872981.2023.2181052\u003c/span\u003e\u003cspan address=\"10.1080/10872981.2023.2181052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeng L. Challenge, integration, and change: ChatGPT and future anatomical education. Med Educ Online. 2024;29(1):2304973. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10872981.2024.2304973\u003c/span\u003e\u003cspan address=\"10.1080/10872981.2024.2304973\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20240113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJavaid M, Haleem A, Singh RP, Khan S, Khan IH. Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. BenchCouncil Trans Benchmarks Stand Evaluations. 2023;3(2):100115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tbench.2023.100115\u003c/span\u003e\u003cspan address=\"10.1016/j.tbench.2023.100115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MM, Watanobe Y. ChatGPT for Education and Research: Opportunities, Threats, and Strategies. Appl Sci. 2023;13(9):5783. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/app13095783\u003c/span\u003e\u003cspan address=\"10.3390/app13095783\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMizumoto A, Eguchi M. Exploring the potential of using an AI language model for automated essay scoring. Res Methods Appl Linguistics. 2023;2(2):100050. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rmal.2023.100050\u003c/span\u003e\u003cspan address=\"10.1016/j.rmal.2023.100050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang C-W, Coleman M, Gachago D, Van Belle J-P, editors. Using ChatGPT to Encourage Critical AI Literacy Skills and for Assessment in Higher Education. ICT Education; 2024 2024//; Cham: Springer Nature Switzerland.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu H. The application and challenges of ChatGPT in educational transformation: New demands for teachers' roles. Heliyon. 2024;10(2):e24289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2024.e24289\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e24289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontenegro-Rueda M, Fern\u0026aacute;ndez-Cerero J, Fern\u0026aacute;ndez-Batanero JM, L\u0026oacute;pez-Meneses E. Impact of the Implementation of ChatGPT in Education: A Systematic Review. Computers. 2023;12(8):153. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/computers12080153\u003c/span\u003e\u003cspan address=\"10.3390/computers12080153\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIlieva G, Yankova T, Klisarova-Belcheva S, Dimitrov A, Bratkov M, Angelov D. Effects of Generative Chatbots in Higher Education. Information. 2023;14(9):492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/info14090492\u003c/span\u003e\u003cspan address=\"10.3390/info14090492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarrokhnia M, Banihashem SK, Noroozi O, Wals A. A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations Educ Teach Int. 2023;1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/14703297.2023.2195846\u003c/span\u003e\u003cspan address=\"10.1080/14703297.2023.2195846\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSallam M, Al-Farajat A, Egger J. Envisioning the Future of ChatGPT in Healthcare: Insights and Recommendations from a Systematic Identification of Influential Research and a Call for Papers. Jordan Med J. 2024;58(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGill SS, Xu M, Patros P, Wu H, Kaur R, Kaur K et al. Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots. Internet of Things and Cyber-Physical Systems. 2024;4:19\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.iotcps.2023.06.002\u003c/span\u003e\u003cspan address=\"10.1016/j.iotcps.2023.06.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra P, Koehler MJ. Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers Coll Record. 2006;108(6):1017\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1467-9620.2006.00684.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-9620.2006.00684.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGkrimpizi T, Peristeras V, Magnisalis I. Classification of Barriers to Digital Transformation in Higher Education Institutions: Systematic Literature Review. Educ Sci. 2023;13(7):746. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/educsci13070746\u003c/span\u003e\u003cspan address=\"10.3390/educsci13070746\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeidener L, Fischer M. Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects. JMIR Med Educ. 2024;10:e51247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/51247\u003c/span\u003e\u003cspan address=\"10.2196/51247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20240105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeras M, Dyer JO, Rooney M, Barros Silva PG, Rutherford D, Kairy D. Usability and Efficacy of Artificial Intelligence Chatbots (ChatGPT) for Health Sciences Students: Protocol for a Crossover Randomized Controlled Trial. JMIR Res Protoc. 2023;12:e51873. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/51873\u003c/span\u003e\u003cspan address=\"10.2196/51873\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20231124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuckin R, Cukurova M, Kent C, du Boulay B. Empowering educators to be AI-ready. Computers Education: Artif Intell. 2022;3:100076. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.caeai.2022.100076\u003c/span\u003e\u003cspan address=\"10.1016/j.caeai.2022.100076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan CKY. A comprehensive AI policy education framework for university teaching and learning. Int J Educational Technol High Educ. 2023;20(1):38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s41239-023-00408-3\u003c/span\u003e\u003cspan address=\"10.1186/s41239-023-00408-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdarkwah MA, Amponsah S, van Wyk MM, Huang R, Tlili A, Shehata B, et al. Awareness and acceptance of ChatGPT as a generative conversational AI for transforming education by Ghanaian academics: A two-phase study. J Appl Learn Teach. 2023;6(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.37074/jalt.2023.6.2.26\u003c/span\u003e\u003cspan address=\"10.37074/jalt.2023.6.2.26\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiryakova G, Angelova N. ChatGPT\u0026mdash;A Challenging Tool for the University Professors in Their Teaching Practice. Educ Sci. 2023;13(10):1056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/educsci13101056\u003c/span\u003e\u003cspan address=\"10.3390/educsci13101056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdaljaleel M, Barakat M, Alsanafi M, Salim NA, Abazid H, Malaeb D, et al. A multinational study on the factors influencing university students\u0026rsquo; attitudes and usage of ChatGPT. Sci Rep. 2024;14(1):1983. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-52549-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-52549-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSallam M, Salim NA, Barakat M, Al-Mahzoum K, Al-Tammemi AB, Malaeb D, et al. Assessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation Study. JMIR Med Educ. 2023;9:e48254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/48254\u003c/span\u003e\u003cspan address=\"10.2196/48254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20230905.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLivberber T, Ayvaz S. The impact of Artificial Intelligence in academia: Views of Turkish academics on ChatGPT. Heliyon. 2023;9(9):e19688. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2023.e19688\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e19688\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20230901.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2307/249008\u003c/span\u003e\u003cspan address=\"10.2307/249008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarangunić N, Granić A. Technology acceptance model: a literature review from 1986 to 2013. Univ Access Inf Soc. 2015;14(1):81\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10209-014-0348-1\u003c/span\u003e\u003cspan address=\"10.1007/s10209-014-0348-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalaquias RF, Malaquias FFO, Hwang Y. Understanding technology acceptance features in learning through a serious game. Comput Hum Behav. 2018;87:395\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chb.2018.06.008\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2018.06.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarahat T. Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia - Social Behav Sci. 2012;64:95\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sbspro.2012.11.012\u003c/span\u003e\u003cspan address=\"10.1016/j.sbspro.2012.11.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosli MS, Saleh NS, Md. Ali A, Abu Bakar S, Mohd Tahir L. A Systematic Review of the Technology Acceptance Model for the Sustainability of Higher Education during the COVID-19 Pandemic and Identified Research Gaps. Sustainability. 2022;14(18):11389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/su141811389\u003c/span\u003e\u003cspan address=\"10.3390/su141811389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZobeidi T, Homayoon SB, Yazdanpanah M, Komendantova N, Warner LA. Employing the TAM in predicting the use of online learning during and beyond the COVID-19 pandemic. Front Psychol. 2023;14:1104653. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2023.1104653\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2023.1104653\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20230217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Hattami HM. Understanding perceptions of academics toward technology acceptance in accounting education. Heliyon. 2023;9(1):e13141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2023.e13141\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e13141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScherer R, Siddiq F, Tondeur J. The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers\u0026rsquo; adoption of digital technology in education. Comput Educ. 2019;128:13\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.compedu.2018.09.009\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2018.09.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGranić A, Marangunić N. Technology acceptance model in educational context: A systematic literature review. Br J Edu Technol. 2019;50(5):2572\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bjet.12864\u003c/span\u003e\u003cspan address=\"10.1111/bjet.12864\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntonietti C, Cattaneo A, Amenduni F. Can teachers\u0026rsquo; digital competence influence technology acceptance in vocational education? Comput Hum Behav. 2022;132:107266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chb.2022.107266\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2022.107266\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurillo GG, Novoa-Hern\u0026aacute;ndez P, Rodr\u0026iacute;guez RS. Technology Acceptance Model and Moodle: A systematic mapping study. Inform Dev. 2020;37(4):617\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0266666920959367\u003c/span\u003e\u003cspan address=\"10.1177/0266666920959367\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMailizar M, Almanthari A, Maulina S. Examining Teachers\u0026rsquo; Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. Contemp Educational Technol. 2021;13:ep298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.30935/cedtech/9709\u003c/span\u003e\u003cspan address=\"10.30935/cedtech/9709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung R, Vogel D. Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Comput Educ. 2013;63:160\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.compedu.2012.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2012.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh V, Morris MG, Davis GB, Davis FD. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003;27(3):425\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2307/30036540\u003c/span\u003e\u003cspan address=\"10.2307/30036540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacCallum R, Widaman K, Zhang S, Hong S. Sample Size in Factor Analysis. Psychol Methods - PSYCHOL METHODS. 1999;4:84\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/1082-989X.4.1.84\u003c/span\u003e\u003cspan address=\"10.1037/1082-989X.4.1.84\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbrahim H, Liu F, Asim R, Battu B, Benabderrahmane S, Alhafni B, et al. Perception, performance, and detectability of conversational artificial intelligence across 32 university courses. Sci Rep. 2023;13(1):12187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-38964-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-38964-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20230824.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrzelecki A. Students\u0026rsquo; Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innov High Educ. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10755-023-09686-1\u003c/span\u003e\u003cspan address=\"10.1007/s10755-023-09686-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Garrel J, Mayer J. Artificial Intelligence in studies\u0026mdash;use of ChatGPT and AI-based tools among students in Germany. Humanit Social Sci Commun. 2023;10(1):799. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1057/s41599-023-02304-7\u003c/span\u003e\u003cspan address=\"10.1057/s41599-023-02304-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaruk L, Rohan R, Nin U, Pal D. University Students\u0026rsquo; Acceptance and Usage of Generative AI (ChatGPT) from a Psycho-Technical Perspective. IAIT 2023: 13th International Conference on Advances in Information Technology2023. p. 1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiwari CK, Bhat MA, Khan ST, Subramaniam R, Khan MAI. What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education. 2023;ahead-\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eof-print(ahead-of-print\u003c/span\u003e\u003cspan address=\"http://of-print(ahead-of-print\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1108/ITSE-04-2023-0061\u003c/span\u003e\u003cspan address=\"10.1108/ITSE-04-2023-0061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkour A, Al-Tammemi AB, Barakat M, Kanj R, Fakhouri HN, Malkawi A, et al. The Impact of the COVID-19 Pandemic and Emergency Distance Teaching on the Psychological Status of University Teachers: A Cross-Sectional Study in Jordan. Am J Trop Med Hyg. 2020;103(6):2391\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4269/ajtmh.20-0877\u003c/span\u003e\u003cspan address=\"10.4269/ajtmh.20-0877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20201027.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlqudah I, Barakat M, Muflih SM, Alqudah A. Undergraduates\u0026rsquo; perceptions and attitudes towards online learning at Jordanian universities during COVID-19. Interact Learn Environ. 2023;31(9):5781\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10494820.2021.2018617\u003c/span\u003e\u003cspan address=\"10.1080/10494820.2021.2018617\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleem F, Alnasrallah W, Malik MI, Ur Rehman S. Factors Affecting the Quality of Online Learning During COVID-19: Evidence From a Developing Economy. Front Educ. 2022;7:847571. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/feduc.2022.847571\u003c/span\u003e\u003cspan address=\"10.3389/feduc.2022.847571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegmi K, Jones L. A systematic review of the factors \u0026ndash; enablers and barriers \u0026ndash; affecting e-learning in health sciences education. BMC Med Educ. 2020;20(1):91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12909-020-02007-6\u003c/span\u003e\u003cspan address=\"10.1186/s12909-020-02007-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleh SS, Nat M, Aqel M. Sustainable Adoption of E-Learning from the TAM Perspective. Sustainability. 2022;14(6):3690. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/su14063690\u003c/span\u003e\u003cspan address=\"10.3390/su14063690\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlassafi MO. E-learning intention material using TAM: A case study. Mater Today Proc. 2022;61:873-7. Epub 20211028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.matpr.2021.09.457\u003c/span\u003e\u003cspan address=\"10.1016/j.matpr.2021.09.457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTawafak RM, Al-Rahmi WM, Almogren AS, Al Adwan MN, Safori A, Attar RW, et al. Analysis of E-Learning System Use Using Combined TAM and ECT Factors. Sustainability. 2023;15(14):11100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/su151411100\u003c/span\u003e\u003cspan address=\"10.3390/su151411100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChahal J, Rani N. Exploring the acceptance for e-learning among higher education students in India: combining technology acceptance model with external variables. J Comput High Educ. 2022;34(3):844\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12528-022-09327-0\u003c/span\u003e\u003cspan address=\"10.1007/s12528-022-09327-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammed B, Mohammad Amin K, Rawad H. Embracing Technological Change in Higher Education. In: Lee W, Sharon W, editors. Higher Education. Rijeka: IntechOpen; 2021. Ch. 12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen B, Simkin L. The Internet of Things (IoT) and marketing: the state of play, future trends and the implications for marketing. J Mark Manage. 2017;33(1\u0026ndash;2):1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/0267257X.2016.1257542\u003c/span\u003e\u003cspan address=\"10.1080/0267257X.2016.1257542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Adwan AS, Li N, Al-Adwan A, Abbasi GA, Albelbisi NA, Habibi A. Extending the Technology Acceptance Model (TAM) to Predict University Students\u0026rsquo; Intentions to Use Metaverse-Based Learning Platforms. Educ Inform Technol. 2023;28(11):15381\u0026ndash;413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10639-023-11816-3\u003c/span\u003e\u003cspan address=\"10.1007/s10639-023-11816-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Ahmad SF, Bani Ahmad Ayassrah AYA, Awwad EM, Irshad M, Ali YA, et al. An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon. 2023;9(8):e18349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2023.e18349\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e18349\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRejeb A, Rejeb K, Appolloni A, Treiblmaier H, Iranmanesh M. Exploring the impact of ChatGPT on education: A web mining and machine learning approach. Int J Manage Educ. 2024;22(1):100932. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijme.2024.100932\u003c/span\u003e\u003cspan address=\"10.1016/j.ijme.2024.100932\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz-Rojas LI, Acosta-Vargas P, De-Moreta-Llovet J, Gonzalez-Rodriguez M. Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix. Sustainability. 2023;15(15):11524. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/su151511524\u003c/span\u003e\u003cspan address=\"10.3390/su151511524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYilmaz R, Karaoglan Yilmaz FG. The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Computers Education: Artif Intell. 2023;4:100147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.caeai.2023.100147\u003c/span\u003e\u003cspan address=\"10.1016/j.caeai.2023.100147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahiman HU, Kodikal R. Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Educ. 2024;11(1):2293431. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/2331186X.2023.2293431\u003c/span\u003e\u003cspan address=\"10.1080/2331186X.2023.2293431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlut M, Wang C. Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. J Acad Mark Sci. 2020;48(4):649\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11747-019-00680-8\u003c/span\u003e\u003cspan address=\"10.1007/s11747-019-00680-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadi Mogavi R, Deng C, Kim J, Zhou P, Kwon YD, Metwally A et al. Exploring User Perspectives on ChatGPT: Applications, Perceptions, and Implications for AI-Integrated Education. arXiv. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48550/arXiv.2305.13114\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2305.13114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbayati H. Investigating undergraduate students' perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Computers Education: Artif Intell. 2024;6:100203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.caeai.2024.100203\u003c/span\u003e\u003cspan address=\"10.1016/j.caeai.2024.100203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJo H, Bang Y. Analyzing ChatGPT adoption drivers with the TOEK framework. Sci Rep. 2023;13(1):22606. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-49710-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-49710-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenon D, Shilpa K. Chatting with ChatGPT: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model. Heliyon. 2023;9(11):e20962. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2023.e20962\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e20962\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSallam M, Elsayed W, Al-Shorbagy M, Barakat M, Khatib EL, Ghach S et al. W,. ChatGPT Usage and Attitudes are Driven by Perceptions of Usefulness, Ease of Use, Risks, and Psycho-Social Impact: A Study among University Students in the UAE. Research Square. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21203/rs.3.rs-3905717/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-3905717/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong D, Aggarwal S, Robinson J, Kumar N, Spearot A, Park DS. Exhaustive or exhausting? Evidence on respondent fatigue in long surveys. J Dev Econ. 2023;161:102992. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jdeveco.2022.102992\u003c/span\u003e\u003cspan address=\"10.1016/j.jdeveco.2022.102992\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"AI chatbots, GPT, education, technology, survey, opinion, knowledge, practices, KAP","lastPublishedDoi":"10.21203/rs.3.rs-3919524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3919524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChatGPT is a generative artificial intelligence (AI) model that has a promising potential in higher education. Nevertheless, the integration of ChatGPT into higher education requires the assessment of university educators\u0026rsquo; perspectives regarding this novel technology. This study aimed to develop and validate a survey instrument specifically tailored to assess ChatGPT usability and acceptability among university educators.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDevelopment of the survey items was based on the Technology Acceptance Model (TAM) followed by expert content validity assessment and pilot testing to improve phrasing of the items. The survey instrument involved assessment of the demographic variables in addition to a total of 40 TAM-based items. The survey was distributed among educators in Jordan in two phases: February 2023\u0026ndash;April 2023 and October 2023. The self-administered survey was distributed via a convenience approach among the contacts of the authors. Assessment of the construct validity was done through exploratory factor analysis (EFA) based on principal component analysis (PCA), while the internal consistency of the inferred constructs was checked using the Cronbach\u0026rsquo;s α.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe final sample comprised 236 university educators, with 72% who heard of ChatGPT before the study (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;169), of whom 76 have already used ChatGPT (45%). The EFA showed a significant Bartlett\u0026rsquo;s test of sphericity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and adequate Kaiser-Meyer-Olkin measure (KMO\u0026thinsp;=\u0026thinsp;.698). The six constructs inferred through EFA explained a cumulative 64% of the variance in the educators\u0026rsquo; attitude to ChatGPT. These constructs comprised 31 items classified into: (1) \u0026ldquo;Effectiveness\u0026rdquo; (α\u0026thinsp;=\u0026thinsp;.845), (2) \u0026ldquo;Anxiety\u0026rdquo; (α\u0026thinsp;=\u0026thinsp;.862), (3) \u0026ldquo;Technology readiness (α\u0026thinsp;=\u0026thinsp;.885), (4) Perceived usefulness (α\u0026thinsp;=\u0026thinsp;.848), (5) Social influence (α\u0026thinsp;=\u0026thinsp;.803), and (6) Perceived risk (α\u0026thinsp;=\u0026thinsp;.796).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study identified six key constructs that could be exploited for comprehensive understanding of the university educators' attitude toward ChatGPT. The novel survey instrument herein termed \u0026ldquo;Ed-TAME-ChatGPT\u0026rdquo; involved positive influencing factors such as perceived usefulness and effectiveness, positive attitude to technology, and social influence in addition to negative factors including anxiety and perceived risk. The developed survey instrument can provide a robust framework for further investigation into the usability and acceptability of ChatGPT among university educators, given the nearly inevitable integration of generative AI into higher education.\u003c/p\u003e","manuscriptTitle":"Perspectives of University Educators Regarding ChatGPT: A Validation Study Based on the Technology Acceptance Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 06:50:07","doi":"10.21203/rs.3.rs-3919524/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":"4dae01a2-30a0-42b4-b707-cc9b8d9f9c57","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-14T06:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 06:50:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3919524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3919524","identity":"rs-3919524","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}