Pre-service Secondary Teachers’ Perceptions on Artificial Intelligence Acceptance in Teaching – learning

Pre-service Secondary Teachers’ Perceptions on Artificial Intelligence Acceptance in Teaching – learning | 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 Pre-service Secondary Teachers’ Perceptions on Artificial Intelligence Acceptance in Teaching – learning Dr.Sanchari Bhunia, K Lalrinchhana, Prof. Lokanath Mishra, Dr Lalhriat puii This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7949370/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The application of Artificial Intelligence (AI) in the field of education in general and teacher education in particular has attracted great interest from the public, governments and academia. With intelligent machines enabling higher-order cognitive functions like thinking, perceiving, learning, problem-solving, and decision-making, along with advancements in data collection and aggregation, analytics, and computer processing power, artificial intelligence technology is having a steadily growing impact on teacher education. The aim of the study to analyses Pre-service Secondary Teachers’ Perceptions on Artificial Intelligence Acceptance in learning using the structure equation modeling (SEM). It also analyses the benefits and draw backs of use of AI in learning process of teacher education. Keeping in view the objectives of the study and nature of the problem Mixed mode method of research has been used for the present study. Data has collected from307 pre-service secondary teachers with a 5-point interval scale ranging strongly agree to strongly disagree developed by the researcher and Focus Group Discussion (FGD).The findings of the study reveals that the critical constructs of TAM theory are useful to measure pre-service Secondary Teachers’ perceived behavior towards their AI acceptance in learning. An AI technology may scan student voice, gauge how much they have learnt, and provide appropriate guidance or regulations. Similarly, Human psychology should not be ignored while accepting AI in teaching learning process and Preventive and supportive software should be developed. Artificial Intelligence Teacher Education Perceived Behavior TAM SEM Pre-service Teachers Figures Figure 1 Figure 2 Introduction Day-to-day Artificial Intelligence (AI) is becoming closer in human lives and has the power to completely change the way we learn, work, and interact. The way children learn and teachers teach could change if artificial intelligence is introduced into the classroom. All forms of electronically reinforced learning, processing, and teaching are included in artificial intelligence, which fosters a supportive environment. It is a well-organized application which offers flexibility, chances for cooperation and command over the educational process, enabling both students and teachers to successfully carry out the teaching process. It is an emerging field of educational technology (Akinwalere & Ivanov, 2022 ).The application of AI in the field ofeducation in general and teacher education in particular has attracted greatinterest from the public, governments and academia (Popenici & Kerr, 2017 ). The usage of Artificial intelligence is growing at an unprecedented rate & it is rapidlychanging the aspects of human life. (Xue & Wang, 2022 aS. Makridakis, 2017,) In recent years the use of Artificial Intelligence (AI) &Learning Analytics (LA) have effectively been introduced in the field of education. (Salas-Pilco et al., 2022). The impact of artificial intelligence technology, in college education, is developing continuously in the direction of in with intelligent machines enabling high-level cognitive processes like thinking, perceiving, learning, problem-solving, and decision-making, coupled with advancements in data collection and aggregation, analytics, and computer processing power, AI presents opportunities to complement and supplement human intelligence and enrich the way people live and work. (Kumar et al, 2019 ) Studying and using artificial intelligence is a crucial and essential part of the professional growth of pre-service and in-service teachers in higher education institutions.The use of AI in teacher education in India has opened new possibilities and challenges(Silander & Stigmar, 2019 AI can enhance a technology's capacity to expand students' knowledge and abilities, build on their strengths, and meet them where they are. Due to AI's ability to process natural forms of input and thefundamental advantages of AI models.Artificial intelligence is currently progressing at an accelerated pace, and this alreadyimpacts on the profound nature of services within higher education (Popenici & Kerr, 2017 ).AI technologies can be used to provide learners with personalized learning services such asdiagnosis, prediction, treatment, and prevention; for instance, in the prevention phase, AI technologies can be applied to accurately predict learners’ learning status and providepersonalized or individualized learning services to prevent and reduce the probability of theirlearning failure (Chu et al., 2022 ). Chassignol et al. ( 2018 ) highlighted the extensive application of AI in different areas, including content development, teaching methods, studentassessment, and communication between teacher and students. Artificial intelligence is not one thing, but anumerical increase in the number of modeling possibilities, as shown in Fig. 1 Croxford and Raffe ( 2015 ) admitted that there is an urgent need of AI implementationin the architecture of Indian teacher education. India is also gaining momentum for applicationof AI in teacher education (Chatterjee & Bhattacharjee, 2019).From the learner perspective in teacher education, one of the crucial objectives of AI usage isto provide personalized learning assistance based on students’ learning status, preferences, orpersonal characteristics (Hwang et al., 2020). For example, AI can provide learning materialsbased on learners’ needs (Christudas et al., 2018 ), diagnose learners’ strengths, weaknesses,and identify gaps (Liu et al., 2017 ), or provide automated feedback and promote collaborationamong students (Aluthman, 2016 ; Benhamdi et al., 2017 ). AI-enhanced technology has playedan essential role in teacher education from the instructor, learner and administrator perspectives,with its potential to open new opportunities and challenges for teacher education transformation (Ouyang et al., 2022 ).The NationalCouncil for Teacher Education (NCTE) has defined the ultimate goal of teacher training is to develop the skills and qualifications of future teachers so that they meet the requirements of the teaching profession and prepare them to meet future needs. It is important to understand thatArtificial Intelligence can support prospectiveteachers, through the provision of educational applications, in the same way asthese technologies are reshaping other fields.(Salas-Pilco et al., 2022). “The main purpose of developing artificialintelligence is to make computer combined with mechanical equipment competent for some complex work whichusually needs human intelligence and greatly reduce the burden of human beings”.(Xue & Wang, 2022 b).This study aims to analyses pre-service secondary teachers’ perception towards artificial intelligence (AI) acceptance in learning. Pre-service teachers’ acceptance of AI depends on their perception, belief, attitude,behavior, competency, skills, and capability to use. The perceptions of a pre-service teachers withregard to AI can consist of positive and/or negative aspects (Parasuraman & Colby, 2015 ). The aim of the study is to find out the perceptions of the pre-service secondary teachers of Mizoram with respect to acceptance of AI in learning and its benefits and drawbacks. The acceptance of AI by pre-service teachers is a function of both cognitive factors (such as perceived usefulness, ease of use, and perceived risk), affective factors (such as attitude and belief), and behavioral and competency-related dimensions (including skills, technological capability, and actual usage behavior). A holistic understanding of AI adoption must therefore integrate these multidimensional influences to design effective training, curriculum, and support systems." The present study used the critical constructs of Technology Acceptance Model(TAM) by Davis ( 1989 ) to understand their influence of the process of AI acceptance by pre-service secondary teachers in Mizoram. From the different similar studies, it is found that TAM and its component influencing AIacceptance at individual level. Two fundamental beliefs proposed by Davis 1989 are Perceived Usefulness (PU) and Perceived Ease of Use (PEOU).Technology Acceptance Model (TAM; Davis, 1989 ) has been one of the most influential models of technology acceptance, with two primary factors influencing an individual’s intention to use new technology: perceived ease of use and perceived usefulness. Although the TAM has been criticized for several reasons, it serves as a useful general framework and is consistent with several studies of older adults' intentions to use new technology. After that many external factors are included in TAM model (Lai & Zainal, 2015 ; Alkaline, 2016;Ikhsan & Sunaryo, 2020;) Perceived Usefulness (PU) is the belief that a technology enhances an individual’s performance (Venkatesh & Davis, 2000 ; Sun et al., 2018 and Liu et al., 2010 ).However, Verkasalo etal. ( 2010 ) revealed a contrary result to the effect which PU has on a person’s usage behaviorof a new technology system (Verkasalo et al., 2010 ). This inconsistency of results leads to thequestion to which extent a person integrates a new technology in current process. In the present study, the perceivedusefulness of AI technology may increase the behavioral intention to incorporate AI in teacher education.Perceived Ease of Use (PEU) is the degree to which an individual believes that using a particular system would be free of physical and mental effort (Davis, 1986; Venkatesh and Davis, 2000 ). According to TAM, perceived usefulness is also influenced by perceived ease of use because, the easier the system is to use the more useful it can be. Easy use of AI perceived by end-users in higher may have significant contribution on the actual acceptance of AI in its teaching and learning process.Similarly, Perceivedrisk was found to exert a strong inhibiting influence on TAM (Featherman & Pavlou, 2003 ). Itsignificantly affects end user intention to adopt an innovation or not (Rosati et al., 2022 ).Hutapea and Wijaya (2021), Chatterjee and Bhattacharjee (2019), Suroso et al. ( 2022 ), Rosatiet al. ( 2022 ), and Habib and Hamadneh (2021), revealed that the impact of PR negativelyrelated to the adoption of AI and several other new technologies at various contexts. The researcher assumedthat pre-service secondar teachers perceived usefulness of AI may have greater positive influence on the incorporation of AI in teaching and learning process at teacher education Programme of Mizoram. Consequently, the researcher formulated the hypothesis as: H1 Perceived Usefulness has a positive influence on the incorporation of Artificial Intelligence in teaching and learning process at teacher education Programme of Mizoram . H2a Perceived Ease of Use has a positive influence on the behavioral intention and perceived usefulness of pre-service secondary teachers to use Artificial Intelligence. H2b Perceived Ease of Use has a positive influence on the perceived usefulness of pre-service secondary teachers to use Artificial Intelligence. H3a Perceived Risk has a negative influence on the behavioral intention and perceived ease of use of pre-service secondary teachers of Mizoram to use Artificial Intelligence in teaching- learning. H3b Perceived Risk has a negative influence on perceived ease of use of pre-service secondary teachers of Mizoram to use Artificial Intelligence in teaching- learning. Methodology Research Methodology In alignment with the objectives of the study and the complex nature of the research problem, a mixed-method research design was employed. This approach integrates both quantitative and qualitative techniques, offering a comprehensive framework to explore and understand pre-service teachers’ acceptance of Artificial Intelligence (AI) in teaching and learning. By incorporating both types of data, the study aims to obtain a more holistic and nuanced understanding of the factors influencing AI adoption among future educators. Research Design The core of this study is rooted in a descriptive survey research design, which is particularly suitable for examining attitudes, perceptions, behaviors, and beliefs among a specific population. Descriptive research involves collecting, organizing, and tabulating data to uncover patterns, draw inferences, and interpret findings in the context of the study. This design allows the researcher to explore the prevailing sentiments and readiness of pre-service teachers to adopt AI in their educational practices. Methodological Approach Both quantitative and qualitative data collection methods were utilized to enhance the validity and reliability of the findings. The quantitative aspect primarily focused on administering structured questionnaires to a statistically significant sample of pre-service secondary teachers. The qualitative component included open-ended feedback, expert validation, and iterative item refinement during the scale development process.To test and validate the theoretical model proposed in the study, Structural Equation Modeling (SEM) was employed. SEM is a powerful multivariate statistical technique that enables researchers to test complex relationships among observed and latent variables. In this study, SEM was instrumental in validating the proposed hypotheses and examining the causal relationships among constructs such as perceived usefulness, perceived ease of use, perceived risk, behavioral intention, and actual acceptance of AI. Population and Sample The target population consisted of approximately 600 pre-service secondary teachers enrolled in four teacher training institutions offering the B.Ed. programme in the state of Mizoram, located in Northeast India. Mizoram is a relatively small state with a unique socio-cultural and ethnic composition. The region of Northeast India, comprising 8% of India's total geographical area, is home to over 220 ethnic groups and a rich diversity of dialects and cultures. According to the 2011 Census, the region has a population of around 40 million, accounting for about 3.1% of India’s population, comparable to the size of the Indian state of Odisha. The hill states—Arunachal Pradesh, Meghalaya, Mizoram, and Nagaland—are primarily inhabited by tribal communities, and Mizoram reflects this diversity within its own tribal populations. To ensure representative sampling and generalizability, simple random sampling was employed. Initially, the researcher intended to collect data from 100 pre-service teachers from each of the four teacher education institutions, yielding a planned sample size of 400 participants. However, due to logistical constraints and despite repeated visits to the institutions, responses were successfully obtained from 307 pre-service secondary teachers. These participants formed the final sample for analysis. Instrumentation and Scale Development The main data collection instrument was a structured questionnaire based on a 5-point Likert scale, ranging from Strongly Agree (5) to Strongly Disagree (1). The items were derived and adapted from prior validated scales in related studies, ensuring content relevance and theoretical alignment. The questionnaire aimed to measure various dimensions including perceptions, beliefs, competencies, attitudes, and behavioral intentions toward the use of AI in teaching and learning. The content validity of the instrument was established through a rigorous expert validation process. Specialists from diverse but relevant academic domains—Teacher Education, Computer Science, and Electronics and Communication—across reputed universities in India were consulted. Based on their expert feedback, several items were refined, modified, or removed to enhance clarity, relevance, and validity. The final version of the scale comprised 20 carefully curated items that were thematically and statistically aligned with the constructs of the study. Data Collection Procedure Data collection was carried out over a four-month period, from September to December 2023. The finalized questionnaire was distributed among the selected pre-service teachers across the four institutions. To ensure data quality, a filler item was included to detect non-serious or patterned responses. Participants who selected “Strongly Agree,” “Agree,” or “Undecided” on the filler item—indicating a potential lack of attention or sincerity—were excluded from the final data analysis. After this data cleaning step, responses from 307 participants were retained and subjected to further analysis.The final dataset was processed by assigning numerical values to each response according to the 5-point Likert scale, thereby converting qualitative perceptions into quantifiable data suitable for statistical modeling through SEM Table 1 Demographic Profile of Sample Pre-service Secondary Teachers (N = 307) Demographic Characteristics Category Frequencies Percentage Gender Male 128 41.6 Female 179 58.4 Stream Arts 148 48.2 Science 147 47.8 Commerce 12 04 Educational qualification Graduate 212 69 Post Graduate 95 31 Further, Focus Group Discussion (Mishra L 2016) was conducted in the Department of Education, Mizoram University for collection of qualitative data to check the acceptance, benefits and draw backs of AI in learning.Confidentiality, informed consent, and all necessary approvals were taken intoconsideration when conducting the research Findings The value ofMaximum Shared Variance (MSV) for each construct is estimated to be less than its corresponding value ofAverage Variance Extracted (AVE) (Hu & Bentler, 1999 ). The threshold value of LF is 0.7 (Barroso et al.2010), of AVE, CR, and MaxR (H) are 0.5, 0.7, and 0.7 (Hu & Bentler, 1999 ), respectively. The results of all the estimated values indicate the reliability and convergent validity of the model (CVM).The estimated loadingfactors (LF) of all the items are assessed to test the reliability of the items with reference totheir constructs. Similarly, the Discriminant validity test has been established to confirm if each itemis strongly related with its own construct and weekly related with its other constructs. The AverageVariance (AV) of each construct has been computed to confirm it. AV is the square root ofthe corresponding AVE. From Table-2 it appears that the value of AV for each construct shownin the diagonal place is greater than the corresponding correlation coefficients shown in offoff-diagonalplaces. It confirms the discriminant validity test. Table-2: Discriminant Validity Test Construct PU PEU PR BI AAI AVE No.Item PU 0.795 0.632 5 PEU 0.453 0.791 0.637 4 PR -0.172 -0.271 0.836 0.710 4 BI 0.583 0.721 -0.279 0.749 0.605 3 AAI 0.254 0.361 -0.137 0.679 0.751 0.673 4 Model fitness of the study was assessed with the help of structural equation modeling (SEM). Itis measured based on 10 goodness-of-fit (GOF) indices. AMOS 26 was used for the estimation. Table 3 shows that all the parameters are within the limits allowed in the standard, which confirms the suitability of the model. After calculating the various parameters, the structural modelwith the weights and significance level of the regression path is presented. As illustrated in Table 3 , all the GOF indices fall within their respective acceptable or ideal threshold ranges. Specifically, the Chi-square/df (CMIN/DF) value of 2.516 indicates a model with low discrepancy and adequate fit. Indices such as GFI (0.954), CFI (0.932), TLI (0.973), and NFI (0.951) all exceed the recommended cutoff of 0.90, reinforcing that the model exhibits a strong comparative and absolute fit. Furthermore, the RMSEA value of 0.036 signifies a very close approximation of the model to the true population parameters, which is particularly noteworthy as RMSEA is a highly sensitive and widely used indicator of fit quality. Similarly, the RMR value of 0.042 suggests that the residual differences between observed and predicted values are minimal. Collectively, these indices demonstrate that the hypothesized structural model is both statistically robust and theoretically sound. Therefore, the relationships proposed between the latent constructs—such as perceived usefulness, perceived ease of use, perceived risk, behavioral intention, and acceptance of AI—are well-supported by the empirical data. The result of the study shows that hypotheses H1, H2a, H2b, H3b, are supported whereas hypothesis H3a is not supported as the p-value is greater than 0.05 level of significance.it reveals that there are significant influences of Perceived Usefulness and Perceived Ease of Use on Acceptance of Artificial Intelligence (AAI) inteacher education. This model follows a TAM-based framework, showing that perceptions of usefulness and ease of use, along with perceived risk, shape behavioral intention, which in turn drives actual acceptance or adoption of AI in education Table 4 Path Analysis with Regression Weights Hypothesis Path β- Value C.R. p-Value Result H1 PU→ΑAI .374 7.462 p < 0.01 Supported H2a PEU→ΑAI .582 9.451 p < 0.01 Supported H2b PEU→PU .516 8.139 p 0.01 Not Supported H3b PR- PEU − .137 -5.26 p < 0.01 Supported The results indicates that perceived usefulness and perceived ease of use have positive influences and significant teacher education students to accept AI in their learning process The results of the structural equation modeling (SEM) reveal that perceived usefulness (PU) and perceived ease of use (PEU) significantly and positively influence the behavioral intention (BI) of pre-service teacher education students to accept and use Artificial Intelligence (AI) in their learning processes. These findings align with the foundational Technology Acceptance Model (TAM) proposed by Davis (1986, 1989 ), which posits that users' acceptance of new technologies is predominantly shaped by their perceptions of its usefulness and ease of operation.. The result also similar to the result of the study conducted by Chen ( 2019 ), Damerji ( 2019 ).This finding is also consistent with findings of previous related studies in different contexts (Davis et al., 1989; Venkatesh 1999; Davis, 1986;Venkatesh & Davis, 2000 ).In addition to these findings, the current study indicates that perceived risk (PR) does not have a direct and significant influence on the behavioral intention to use AI among pre-service teachers. However, PR was found to exert a significantly negative impact on perceived ease of use, indicating that concerns related to data security, privacy, algorithm transparency, or potential misuse may hinder users’ perceptions of how easily they can engage with AI system It also appears a good predictor of perceived usefulness. Perceivedrisk shows to have no direct significant influence on the behavioral intention to use AI but ithas shown significantly negative influence on perceived ease of AI use by students of teachereducation. This finding of the study is supported by the findings of previous related studies (Li& Huang, 2009; Mutahar et al 2018 ) in different contexts.Perceived usefulness and perceived ease of use are both evidence of acceptance. Yet both factors are impacted by other independent variables such as culture, and function, data quality, and system security (Azeroual et al., 2019c). Following different studies (Kollmann, 1998; Lucke, 1995; Venkatesh et al., 2007), in Western cultures, the perceived usefulness seems to be more important in determining the intentions and actual use, while ease of use appears to be the key in non-Western cultures.All secondary pre service teachers agreed that the major challenge with the use of AI is data protection,surveillance, bias, and privacy.Privacy and confidentiality are major issues when using AI with students in learning. In the Focus Group Discussion (FGD) participants are agreed that AI will be helpful in the teaching and learning process of teacher education. In the FGD it is also revel that “Artificial intelligence in teacher education can be used in many areas from individual learning, examination opportunities, face recognition system to taking attendance at the entrance to the class.’ and highlighted tools for the personalization of learning. Ms Nath a pre-service secondary teacher said that “The information of the student teachers can be tracked, assessed, and plans made regarding the profession that this student should pursue in the future as a benefit of artificial intelligence. Artificial intelligence tools can assist in evaluating exam results, student movements, and communication among students," Mr X a student of computers science doing B.Ed. that AI technology may scan students' voices, gauge how much they have learnt, and provide appropriate guidance or regulations, according to the teacher. Similarly, many participants said that Human psychology should not be ignored while accepting AI in teaching learning process, and Preventive and supportive software should be developed. Perceived Risk has a negative influence on perceived ease of use of pre-service secondary teachers of Mizoram to use Artificial Intelligence in learning is not accepted On the other side all most all the pre-service secondary teachers admitted that the humanistic value will be missed while accepting AI in teaching learning. Ms Y said that we will not need teachers in the distant future." Ms Z has the same opinion, stating, “Artificial intelligence will take over all educational tasks; even a teacher may not be needed.” Possible reasons for these concerns include the influence of dystopian robot movies and popular media, which some participants believed had come true. The questions asked to the participants included a descriptive aspect: While asking questions posed to the participants included a descriptive aspect: How do you define AI tools in learning aspects of teacher education students when artificial intelligence-supported educational environments the answers are converted into percentage considering different groups. The distribution of responses to this question between groups is shown. Table − 5 Distribution of benefit - drawback percentages by groups Sl no Groups Benefits (%) Drawbacks (%) 1 Gender Male 62 38 Female 49 51 2 Stream Arts 58 42 Science 85 15 Commerce 72 28 3 Educational qualification Graduate 57 43 Post Graduate 69 31 In this respect, it can be said that the participants from science and commerce stream viewed that AI in learning will be beneficial for the student teachers as well as teacher educators of teacher education of Mizoram. All pre-service teachers could evaluate the possible advantages and disadvantages of learning only from the point of view of teaching learning and could see potential problems in the future of teachers, although they seem to agree on the advantages of teaching-learning.. The assimilation of AI has been slower to develop the necessary human qualities of receptivity, versatility and understanding. However, there are many areas where the inherent strengths of AI help fill the "gaps" in learning and teaching. Artificial intelligence can be used in teacher training\educational institutions to improve the learning experience of secondary school teachers, reduce dropout\and create an individualized learning environment. This finding is very similar to the findings of study conducted by Pedro (2020). Similarly, Students teachers of Mizoram University have been using ChatGPT to write essays, assignments generate codes, solve mathematical solutions, and many others this finding is very similar to the findings of Halaweh ( 2023 ). From the responses of the students, it is seen that there has been a growing interest in best practices for using ChatGPT in higher education that also similar to the findings of the study conducted by Halaweh ( 2023 ). From the above analysis it is found that integration AI tools in teacher education would be very useful in online, independent learning of the students. Students’ teachers Perceive that anonymity afforded by AI would make them less self-conscious and, as a result, allow them to ask more questions in the class. Students’ teachers perceive that the anonymity would make them “less afraid to ask questions”, “wouldn’t feel bad about wasting the professor’s time”, and would be “less distracting to class”. Although students worry that AI could give unreliable answers and negatively impact their grades Conclusion Teacher education's adoption of AI tools and apps has created new opportunities as well as obstacles for teaching, learning, and administrative tasks. AI is progressively being used by Indian teacher education students. In India, the use of AI in teacher education is still in its infancy. The current study examined how pre-service secondary teachers felt about their acceptance of artificial intelligence in their teaching and learning process in response to their behaviors. The researcher supported the qualitative findings with quantitative findings of the study that would facilitate and quicken the participants' perceived behavior to accept artificial intelligence. The study used the TAM theory and its concepts to comprehend how pre-service secondary teachers saw their conduct in accepting AI in the teaching learning process. To the best of the researcher's knowledge, no research has been done to date that specifically applies TAM theory to comprehend how teacher education students in India perceive their behavior when it comes to accepting AI into their learning. The findings show that teacher education students' behavioral intention to use AI is positively influenced by the variables of perceived usefulness and perceived ease of usage. They have a strong ability to explain their behavioral intention to use AI. Perceived usefulness is mediated by perceived ease of usage. Once more, perceived danger functions as a mediator between components and has an adverse effect on perceived usability. Teacher education students perceived both positive and negative aspects of accepting AI in teaching Learning Process. The results of the study fill the gap by analyzing the influence of teacher education students’ perception on AI acceptance. It also shows good explanatory power to predict acceptance of AI by students of teacher education. Hence, authorities, policy makers, designers, developers, and decision makers of teacher education institutes must focus on the influential constructs of the research model to analyze teacher education students perceived behavior and their AI acceptance for effective integration of AI tools and applications in teaching and learning of teacher education in India. Declarations Declaration of Ethics: The researchers have obtained ethical approval from the Mizoram University Human Ethics Committee for conducting this research. The approval letter is with the corresponding author. All procedures performed in studies involving human participants were in accordance with the ethical standards of social science research most commonly the 1964 Declaration of Helsinki and its later amendments. Consent of Participation: Similarly, the researcher has obtained the participant's consent to participate in the study, and the results of the study will be published in the form of a journal article. Competing interests: The author(s) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Available statement - The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding - there is no funding for conducting and publishing this research Author's contributions: The corresponding author or the 1 st author has developed the conceptual framework and analyzed the data. The second and third authors collected the data and tabulated the data Consent to publish - NA Acknowledgment: The author(s) acknowledge the participants and the experts involved in this study. 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Journal of computer information systems, 58(3),193-203. https://doi.org/10.1080/08874417.2016.1222891 Suroso, I., Afandi, M. F., & Galushasti, A. (2022). Does Perceived Risk? A Study of Technology Acceptance Model on Online Shopping Intention. Academy of StrategicManagement Journal, 21(3), 1-12. Venkatesh, V. & Davis, F.D. (2000). Extrinsic and intrinsic motivation to use computers in theworkplace. Journal of Applied Psychology, 22 (14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x Verkasalo, H., López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2010). Analysis ofusers and non-users of smartphone applications. Telematics and Informatics, 27(3),242-255. https://doi.org/10.1016/j.tele.2009.11.001 Y. Xue and Y. Wang, (2022) “Artificial Intelligence for Education andTeaching,” Wireless Communications and Mobile Computing,vol.https://doi.org/10.1155/2022/4750018 Yajing Xue, Yijun Wang, (2022) Artificial Intelligence for Education and Teaching", Wireless Communications and Mobile Computing, vol. 2022, Article ID 4750018, 10 pages, 2022. https://doi.org/10.1155/2022/4750018 Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7949370","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560510048,"identity":"0b824834-6e98-4978-87d2-25a6870212ec","order_by":0,"name":"Dr.Sanchari Bhunia","email":"","orcid":"","institution":"Mizoram University","correspondingAuthor":false,"prefix":"Dr.","firstName":"Sanchari","middleName":"","lastName":"Bhunia","suffix":""},{"id":560510055,"identity":"77ace09d-399e-4190-85a7-2711fb63280c","order_by":1,"name":"K Lalrinchhana","email":"","orcid":"","institution":"Mizoram 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12:29:09","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127372,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7949370/v1/ab5032d758ac3a0085af48eb.html"},{"id":98779384,"identity":"110e10bd-cc8c-4b1e-974f-72281a6180f5","added_by":"auto","created_at":"2025-12-22 12:30:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139485,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eComponents, types, and subfields of AI based on Regona et al (2022)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7949370/v1/5c3b2fb17f6593782cc73c87.jpeg"},{"id":98748976,"identity":"177b8313-fb94-4ab3-95df-5bcbb3288673","added_by":"auto","created_at":"2025-12-22 08:59:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural Model with Path Weights and Significance Level\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7949370/v1/902c61bd15e48b424b8441ce.png"},{"id":100943907,"identity":"bad31456-9285-4e8c-811e-ce56e1b6ef8c","added_by":"auto","created_at":"2026-01-23 05:40:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1018690,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7949370/v1/a7686782-b3c9-4044-b455-fd5115951c7a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pre-service Secondary Teachers’ Perceptions on Artificial Intelligence Acceptance in Teaching – learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDay-to-day Artificial Intelligence (AI) is becoming closer in human lives and has the power to completely change the way we learn, work, and interact. The way children learn and teachers teach could change if artificial intelligence is introduced into the classroom. All forms of electronically reinforced learning, processing, and teaching are included in artificial intelligence, which fosters a supportive environment. It is a well-organized application which offers flexibility, chances for cooperation and command over the educational process, enabling both students and teachers to successfully carry out the teaching process. It is an emerging field of educational technology (Akinwalere \u0026amp; Ivanov, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).The application of AI in the field ofeducation in general and teacher education in particular has attracted greatinterest from the public, governments and academia (Popenici \u0026amp; Kerr, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The usage of Artificial intelligence is growing at an unprecedented rate \u0026amp; it is rapidlychanging the aspects of human life. (Xue \u0026amp; Wang, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003eaS. Makridakis, 2017,) In recent years the use of Artificial Intelligence (AI) \u0026amp;Learning Analytics (LA) have effectively been introduced in the field of education. (Salas-Pilco et al., 2022). The impact of artificial intelligence technology, in college education, is developing continuously in the direction of in with intelligent machines enabling high-level cognitive processes like thinking, perceiving, learning, problem-solving, and decision-making, coupled with advancements in data collection and aggregation, analytics, and computer processing power, AI presents opportunities to complement and supplement human intelligence and enrich the way people live and work. (Kumar et al, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Studying and using artificial intelligence is a crucial and essential part of the professional growth of pre-service and in-service teachers in higher education institutions.The use of AI in teacher education in India has opened new possibilities and challenges(Silander \u0026amp; Stigmar, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003eAI can enhance a technology's capacity to expand students' knowledge and abilities, build on their strengths, and meet them where they are. Due to AI's ability to process natural forms of input and thefundamental advantages of AI models.Artificial intelligence is currently progressing at an accelerated pace, and this alreadyimpacts on the profound nature of services within higher education (Popenici \u0026amp; Kerr, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).AI technologies can be used to provide learners with personalized learning services such asdiagnosis, prediction, treatment, and prevention; for instance, in the prevention phase, AI technologies can be applied to accurately predict learners\u0026rsquo; learning status and providepersonalized or individualized learning services to prevent and reduce the probability of theirlearning failure (Chu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Chassignol et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) highlighted the extensive application of AI in different areas, including content development, teaching methods, studentassessment, and communication between teacher and students. Artificial intelligence is not one thing, but anumerical increase in the number of modeling possibilities, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCroxford and Raffe (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) admitted that there is an urgent need of AI implementationin the architecture of Indian teacher education. India is also gaining momentum for applicationof AI in teacher education (Chatterjee \u0026amp; Bhattacharjee, 2019).From the learner perspective in teacher education, one of the crucial objectives of AI usage isto provide personalized learning assistance based on students\u0026rsquo; learning status, preferences, orpersonal characteristics (Hwang et al., 2020). For example, AI can provide learning materialsbased on learners\u0026rsquo; needs (Christudas et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), diagnose learners\u0026rsquo; strengths, weaknesses,and identify gaps (Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), or provide automated feedback and promote collaborationamong students (Aluthman, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Benhamdi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). AI-enhanced technology has playedan essential role in teacher education from the instructor, learner and administrator perspectives,with its potential to open new opportunities and challenges for teacher education transformation (Ouyang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).The NationalCouncil for Teacher Education (NCTE) has defined the ultimate goal of teacher training is to develop the skills and qualifications of future teachers so that they meet the requirements of the teaching profession and prepare them to meet future needs. It is important to understand thatArtificial Intelligence can support prospectiveteachers, through the provision of educational applications, in the same way asthese technologies are reshaping other fields.(Salas-Pilco et al., 2022). \u0026ldquo;The main purpose of developing artificialintelligence is to make computer combined with mechanical equipment competent for some complex work whichusually needs human intelligence and greatly reduce the burden of human beings\u0026rdquo;.(Xue \u0026amp; Wang, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003eb).This study aims to analyses pre-service secondary teachers\u0026rsquo; perception towards artificial intelligence (AI) acceptance in learning.\u003c/p\u003e \u003cp\u003ePre-service teachers\u0026rsquo; acceptance of AI depends on their perception, belief, attitude,behavior, competency, skills, and capability to use. The perceptions of a pre-service teachers withregard to AI can consist of positive and/or negative aspects (Parasuraman \u0026amp; Colby, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The aim of the study is to find out the perceptions of the pre-service secondary teachers of Mizoram with respect to acceptance of AI in learning and its benefits and drawbacks. The acceptance of AI by pre-service teachers is a function of both cognitive factors (such as perceived usefulness, ease of use, and perceived risk), affective factors (such as attitude and belief), and behavioral and competency-related dimensions (including skills, technological capability, and actual usage behavior). A holistic understanding of AI adoption must therefore integrate these multidimensional influences to design effective training, curriculum, and support systems.\" The present study used the critical constructs of Technology Acceptance Model(TAM) by Davis (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) to understand their influence of the process of AI acceptance by pre-service secondary teachers in Mizoram. From the different similar studies, it is found that TAM and its component influencing AIacceptance at individual level. Two fundamental beliefs proposed by Davis \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003eare Perceived Usefulness (PU) and Perceived Ease of Use (PEOU).Technology Acceptance Model (TAM; Davis, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) has been one of the most influential models of technology acceptance, with two primary factors influencing an individual\u0026rsquo;s intention to use new technology: perceived ease of use and perceived usefulness. Although the TAM has been criticized for several reasons, it serves as a useful general framework and is consistent with several studies of older adults' intentions to use new technology. After that many external factors are included in TAM model (Lai \u0026amp; Zainal, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Alkaline, 2016;Ikhsan \u0026amp; Sunaryo, 2020;)\u003c/p\u003e \u003cp\u003ePerceived Usefulness (PU) is the belief that a technology enhances an individual\u0026rsquo;s performance (Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e and Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).However, Verkasalo etal. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) revealed a contrary result to the effect which PU has on a person\u0026rsquo;s usage behaviorof a new technology system (Verkasalo et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This inconsistency of results leads to thequestion to which extent a person integrates a new technology in current process. In the present study, the perceivedusefulness of AI technology may increase the behavioral intention to incorporate AI in teacher education.Perceived Ease of Use (PEU) is the degree to which an individual believes that using a particular system would be free of physical and mental effort (Davis, 1986; Venkatesh and Davis, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). According to TAM, perceived usefulness is also influenced by perceived ease of use because, the easier the system is to use the more useful it can be. Easy use of AI perceived by end-users in higher may have significant contribution on the actual acceptance of AI in its teaching and learning process.Similarly, Perceivedrisk was found to exert a strong inhibiting influence on TAM (Featherman \u0026amp; Pavlou, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Itsignificantly affects end user intention to adopt an innovation or not (Rosati et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Hutapea and Wijaya (2021), Chatterjee and Bhattacharjee (2019), Suroso et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Rosatiet al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Habib and Hamadneh (2021), revealed that the impact of PR negativelyrelated to the adoption of AI and several other new technologies at various contexts. The researcher assumedthat pre-service secondar teachers perceived usefulness of AI may have greater positive influence on the incorporation of AI in teaching and learning process at teacher education Programme of Mizoram. Consequently, the researcher formulated the hypothesis as:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1 Perceived Usefulness has a positive influence on the incorporation of Artificial Intelligence in teaching and learning process at teacher education Programme of Mizoram\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eH2a \u003cem\u003ePerceived Ease of Use has a positive influence on the behavioral intention and perceived usefulness of pre-service secondary teachers to use Artificial Intelligence.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eH2b Perceived Ease of Use has a positive influence on the perceived usefulness of pre-service secondary teachers to use Artificial Intelligence.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eH3a Perceived Risk has a negative influence on the behavioral intention and perceived ease of use of pre-service secondary teachers of Mizoram to use Artificial Intelligence in teaching- learning.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eH3b Perceived Risk has a negative influence on perceived ease of use of pre-service secondary teachers of Mizoram to use Artificial Intelligence in teaching- learning.\u003c/em\u003e \u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eResearch Methodology\u003c/p\u003e \u003cp\u003eIn alignment with the objectives of the study and the complex nature of the research problem, a mixed-method research design was employed. This approach integrates both quantitative and qualitative techniques, offering a comprehensive framework to explore and understand pre-service teachers\u0026rsquo; acceptance of Artificial Intelligence (AI) in teaching and learning. By incorporating both types of data, the study aims to obtain a more holistic and nuanced understanding of the factors influencing AI adoption among future educators.\u003c/p\u003e \u003cp\u003eResearch Design\u003c/p\u003e \u003cp\u003eThe core of this study is rooted in a descriptive survey research design, which is particularly suitable for examining attitudes, perceptions, behaviors, and beliefs among a specific population. Descriptive research involves collecting, organizing, and tabulating data to uncover patterns, draw inferences, and interpret findings in the context of the study. This design allows the researcher to explore the prevailing sentiments and readiness of pre-service teachers to adopt AI in their educational practices.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMethodological Approach\u003c/h2\u003e \u003cp\u003eBoth quantitative and qualitative data collection methods were utilized to enhance the validity and reliability of the findings. The quantitative aspect primarily focused on administering structured questionnaires to a statistically significant sample of pre-service secondary teachers. The qualitative component included open-ended feedback, expert validation, and iterative item refinement during the scale development process.To test and validate the theoretical model proposed in the study, Structural Equation Modeling (SEM) was employed. SEM is a powerful multivariate statistical technique that enables researchers to test complex relationships among observed and latent variables. In this study, SEM was instrumental in validating the proposed hypotheses and examining the causal relationships among constructs such as perceived usefulness, perceived ease of use, perceived risk, behavioral intention, and actual acceptance of AI.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation and Sample\u003c/h3\u003e\n\u003cp\u003eThe target population consisted of approximately 600 pre-service secondary teachers enrolled in four teacher training institutions offering the B.Ed. programme in the state of Mizoram, located in Northeast India. Mizoram is a relatively small state with a unique socio-cultural and ethnic composition. The region of Northeast India, comprising 8% of India's total geographical area, is home to over 220 ethnic groups and a rich diversity of dialects and cultures. According to the 2011 Census, the region has a population of around 40\u0026nbsp;million, accounting for about 3.1% of India\u0026rsquo;s population, comparable to the size of the Indian state of Odisha. The hill states\u0026mdash;Arunachal Pradesh, Meghalaya, Mizoram, and Nagaland\u0026mdash;are primarily inhabited by tribal communities, and Mizoram reflects this diversity within its own tribal populations.\u003c/p\u003e \u003cp\u003eTo ensure representative sampling and generalizability, simple random sampling was employed. Initially, the researcher intended to collect data from 100 pre-service teachers from each of the four teacher education institutions, yielding a planned sample size of 400 participants. However, due to logistical constraints and despite repeated visits to the institutions, responses were successfully obtained from 307 pre-service secondary teachers. These participants formed the final sample for analysis.\u003c/p\u003e\n\u003ch3\u003eInstrumentation and Scale Development\u003c/h3\u003e\n\u003cp\u003eThe main data collection instrument was a structured questionnaire based on a 5-point Likert scale, ranging from Strongly Agree (5) to Strongly Disagree (1). The items were derived and adapted from prior validated scales in related studies, ensuring content relevance and theoretical alignment. The questionnaire aimed to measure various dimensions including perceptions, beliefs, competencies, attitudes, and behavioral intentions toward the use of AI in teaching and learning.\u003c/p\u003e \u003cp\u003eThe content validity of the instrument was established through a rigorous expert validation process. Specialists from diverse but relevant academic domains\u0026mdash;Teacher Education, Computer Science, and Electronics and Communication\u0026mdash;across reputed universities in India were consulted. Based on their expert feedback, several items were refined, modified, or removed to enhance clarity, relevance, and validity. The final version of the scale comprised 20 carefully curated items that were thematically and statistically aligned with the constructs of the study.\u003c/p\u003e\n\u003ch3\u003eData Collection Procedure\u003c/h3\u003e\n\u003cp\u003eData collection was carried out over a four-month period, from September to December 2023. The finalized questionnaire was distributed among the selected pre-service teachers across the four institutions. To ensure data quality, a filler item was included to detect non-serious or patterned responses. Participants who selected \u0026ldquo;Strongly Agree,\u0026rdquo; \u0026ldquo;Agree,\u0026rdquo; or \u0026ldquo;Undecided\u0026rdquo; on the filler item\u0026mdash;indicating a potential lack of attention or sincerity\u0026mdash;were excluded from the final data analysis. After this data cleaning step, responses from 307 participants were retained and subjected to further analysis.The final dataset was processed by assigning numerical values to each response according to the 5-point Likert scale, thereby converting qualitative perceptions into quantifiable data suitable for statistical modeling through SEM\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\u003eDemographic Profile of Sample Pre-service Secondary Teachers (N\u0026thinsp;=\u0026thinsp;307)\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Characteristics\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\u003eFrequencies\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e128\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e41.6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e179\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e58.4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eStream\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eArts\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e148\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e48.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eScience\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e147\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e47.8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCommerce\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e04\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eEducational qualification\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGraduate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e212\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e69\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePost Graduate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e95\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e31\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurther, Focus Group Discussion (Mishra L 2016) was conducted in the Department of Education, Mizoram University for collection of qualitative data to check the acceptance, benefits and draw backs of AI in learning.Confidentiality, informed consent, and all necessary approvals were taken intoconsideration when conducting the research\u003c/p\u003e"},{"header":"Findings","content":"\u003cp\u003eThe value ofMaximum Shared Variance (MSV) for each construct is estimated to be less than its corresponding value ofAverage Variance Extracted (AVE) (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The threshold value of LF is 0.7 (Barroso et al.2010), of AVE, CR, and MaxR (H) are 0.5, 0.7, and 0.7 (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), respectively. The results of all the estimated values indicate the reliability and convergent validity of the model (CVM).The estimated loadingfactors (LF) of all the items are assessed to test the reliability of the items with reference totheir constructs. Similarly, the Discriminant validity test has been established to confirm if each itemis strongly related with its own construct and weekly related with its other constructs. The AverageVariance (AV) of each construct has been computed to confirm it. AV is the square root ofthe corresponding AVE. From Table-2 it appears that the value of AV for each construct shownin the diagonal place is greater than the corresponding correlation coefficients shown in offoff-diagonalplaces. It confirms the discriminant validity test.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-2: Discriminant Validity Test\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePEU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo.Item\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.795\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.791\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.836\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.749\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.751\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\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\u003eModel fitness of the study was assessed with the help of structural equation modeling (SEM). Itis measured based on 10 goodness-of-fit (GOF) indices. AMOS 26 was used for the estimation. Table\u003c/p\u003e \u003cp\u003e3 shows that all the parameters are within the limits allowed in the standard, which confirms the suitability of the model. After calculating the various parameters, the structural modelwith the weights and significance level of the regression path is presented.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"609\" height=\"418\"\u003e\u003c/p\u003e\u003cp\u003eAs illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all the GOF indices fall within their respective acceptable or ideal threshold ranges. Specifically, the Chi-square/df (CMIN/DF) value of 2.516 indicates a model with low discrepancy and adequate fit. Indices such as GFI (0.954), CFI (0.932), TLI (0.973), and NFI (0.951) all exceed the recommended cutoff of 0.90, reinforcing that the model exhibits a strong comparative and absolute fit.\u003c/p\u003e \u003cp\u003eFurthermore, the RMSEA value of 0.036 signifies a very close approximation of the model to the true population parameters, which is particularly noteworthy as RMSEA is a highly sensitive and widely used indicator of fit quality. Similarly, the RMR value of 0.042 suggests that the residual differences between observed and predicted values are minimal.\u003c/p\u003e \u003cp\u003eCollectively, these indices demonstrate that the hypothesized structural model is both statistically robust and theoretically sound. Therefore, the relationships proposed between the latent constructs\u0026mdash;such as perceived usefulness, perceived ease of use, perceived risk, behavioral intention, and acceptance of AI\u0026mdash;are well-supported by the empirical data.\u003c/p\u003e \u003cp\u003eThe result of the study shows that hypotheses H1, H2a, H2b, H3b, are supported whereas hypothesis H3a is not supported as the p-value is greater than 0.05 level of significance.it reveals that there are significant influences of Perceived Usefulness and Perceived Ease of Use on Acceptance of Artificial Intelligence (AAI) inteacher education.\u003c/p\u003e \u003cp\u003eThis model follows a TAM-based framework, showing that perceptions of usefulness and ease of use, along with perceived risk, shape behavioral intention, which in turn drives actual acceptance or adoption of AI in education\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePath Analysis with Regression Weights\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eβ-\u003c/em\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU\u0026rarr;ΑAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep \u0026lt; 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEU\u0026rarr;ΑAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep \u0026lt; 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEU\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep \u0026lt; 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR\u0026rarr;ΑAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep \u0026gt; 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR- PEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results indicates that perceived usefulness and perceived ease of use have positive influences and significant teacher education students to accept AI in their learning process The results of the structural equation modeling (SEM) reveal that perceived usefulness (PU) and perceived ease of use (PEU) significantly and positively influence the behavioral intention (BI) of pre-service teacher education students to accept and use Artificial Intelligence (AI) in their learning processes. These findings align with the foundational Technology Acceptance Model (TAM) proposed by Davis (1986, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), which posits that users' acceptance of new technologies is predominantly shaped by their perceptions of its usefulness and ease of operation.. The result also similar to the result of the study conducted by Chen (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Damerji (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).This finding is also consistent with findings of previous related studies in different contexts (Davis et al., 1989; Venkatesh 1999; Davis, 1986;Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).In addition to these findings, the current study indicates that perceived risk (PR) does not have a direct and significant influence on the behavioral intention to use AI among pre-service teachers. However, PR was found to exert a significantly negative impact on perceived ease of use, indicating that concerns related to data security, privacy, algorithm transparency, or potential misuse may hinder users\u0026rsquo; perceptions of how easily they can engage with AI system It also appears a good predictor of perceived usefulness. Perceivedrisk shows to have no direct significant influence on the behavioral intention to use AI but ithas shown significantly negative influence on perceived ease of AI use by students of teachereducation. This finding of the study is supported by the findings of previous related studies (Li\u0026amp; Huang, 2009; Mutahar et al \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in different contexts.Perceived usefulness and perceived ease of use are both evidence of acceptance. Yet both factors are impacted by other independent variables such as culture, and function, data quality, and system security (Azeroual et al., 2019c). Following different studies (Kollmann, 1998; Lucke, 1995; Venkatesh et al., 2007), in Western cultures, the perceived usefulness seems to be more important in determining the intentions and actual use, while ease of use appears to be the key in non-Western cultures.All secondary pre service teachers agreed that the major challenge with the use of AI is data protection,surveillance, bias, and privacy.Privacy and confidentiality are major issues when using AI with students in learning. In the Focus Group Discussion (FGD) participants are agreed that AI will be helpful in the teaching and learning process of teacher education. In the FGD it is also revel that\u003c/p\u003e \u003cp\u003e\u0026ldquo;Artificial \u003cem\u003eintelligence in teacher education can be used in many areas from individual learning, examination opportunities, face recognition system to taking attendance at the entrance to the class.\u0026rsquo; and highlighted tools for the personalization of learning.\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMs Nath a pre-service secondary teacher said that\u003c/h2\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The information of the student teachers can be tracked, assessed, and plans made regarding the profession that this student should pursue in the future as a benefit of artificial intelligence. Artificial intelligence tools can assist in evaluating exam results, student movements, and communication among students,\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMr X a student of computers science doing B.Ed. that AI technology may scan students' voices, gauge how much they have learnt, and provide appropriate guidance or regulations, according to the teacher. Similarly, many participants said that Human psychology should not be ignored while accepting AI in teaching learning process, and Preventive and supportive software should be developed. Perceived Risk has a negative influence on perceived ease of use of pre-service secondary teachers of Mizoram to use Artificial Intelligence in learning is not accepted\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn the other side all most all the pre-service secondary teachers admitted that the humanistic value will be missed while accepting AI in teaching learning. Ms Y said that we will not need teachers in the distant future.\" Ms Z has the same opinion, stating, \u0026ldquo;Artificial intelligence will take over all educational tasks; even a teacher may not be needed.\u0026rdquo; Possible reasons for these concerns include the influence of dystopian robot movies and popular media, which some participants believed had come true. The questions asked to the participants included a descriptive aspect: While asking questions posed to the participants included a descriptive aspect: How do you define AI tools in learning aspects of teacher education students when artificial intelligence-supported educational environments the answers are converted into percentage considering different groups. The distribution of responses to this question between groups is shown.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable \u0026minus;\u0026thinsp;5 Distribution of benefit - drawback percentages by groups\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenefits (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDrawbacks (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e62\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e38\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e49\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eStream\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eArts\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e58\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e42\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eScience\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e85\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCommerce\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e72\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e28\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eEducational qualification\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGraduate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e57\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e43\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePost Graduate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e69\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e31\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this respect, it can be said that the participants from science and commerce stream viewed that AI in learning will be beneficial for the student teachers as well as teacher educators of teacher education of Mizoram. All pre-service teachers could evaluate the possible advantages and disadvantages of learning only from the point of view of teaching learning and could see potential problems in the future of teachers, although they seem to agree on the advantages of teaching-learning.. The assimilation of AI has been slower to develop the necessary human qualities of receptivity, versatility and understanding. However, there are many areas where the inherent strengths of AI help fill the \"gaps\" in learning and teaching. Artificial intelligence can be used in teacher training\\educational institutions to improve the learning experience of secondary school teachers, reduce dropout\\and create an individualized learning environment. This finding is very similar to the findings of study conducted by Pedro (2020).\u003c/p\u003e \u003cp\u003eSimilarly, Students teachers of Mizoram University have been using ChatGPT to write essays, assignments generate codes, solve mathematical solutions, and many others this finding is very similar to the findings of Halaweh (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From the responses of the students, it is seen that there has been a growing interest in best practices for using ChatGPT in higher education that also similar to the findings of the study conducted by Halaweh (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From the above analysis it is found that integration AI tools in teacher education would be very useful in online, independent learning of the students.\u003c/p\u003e \u003cp\u003eStudents\u0026rsquo; teachers Perceive that anonymity afforded by AI would make them less self-conscious and, as a result, allow them to ask more questions in the class. Students\u0026rsquo; teachers perceive that the anonymity would make them \u0026ldquo;less afraid to ask questions\u0026rdquo;, \u0026ldquo;wouldn\u0026rsquo;t feel bad about wasting the professor\u0026rsquo;s time\u0026rdquo;, and would be \u0026ldquo;less distracting to class\u0026rdquo;. Although students worry that AI could give unreliable answers and negatively impact their grades\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTeacher education's adoption of AI tools and apps has created new opportunities as well as obstacles for teaching, learning, and administrative tasks. AI is progressively being used by Indian teacher education students. In India, the use of AI in teacher education is still in its infancy. The current study examined how pre-service secondary teachers felt about their acceptance of artificial intelligence in their teaching and learning process in response to their behaviors. The researcher supported the qualitative findings with quantitative findings of the study that would facilitate and quicken the participants' perceived behavior to accept artificial intelligence. The study used the TAM theory and its concepts to comprehend how pre-service secondary teachers saw their conduct in accepting AI in the teaching learning process. To the best of the researcher's knowledge, no research has been done to date that specifically applies TAM theory to comprehend how teacher education students in India perceive their behavior when it comes to accepting AI into their learning. The findings show that teacher education students' behavioral intention to use AI is positively influenced by the variables of perceived usefulness and perceived ease of usage. They have a strong ability to explain their behavioral intention to use AI. Perceived usefulness is mediated by perceived ease of usage. Once more, perceived danger functions as a mediator between components and has an adverse effect on perceived usability. Teacher education students perceived both positive and negative aspects of accepting AI in teaching Learning Process. The results of the study fill the gap by analyzing the influence of teacher education students\u0026rsquo; perception on AI acceptance. It also shows good explanatory power to predict acceptance of AI by students of teacher education. Hence, authorities, policy makers, designers, developers, and decision makers of teacher education institutes must focus on the influential constructs of the research model to analyze teacher education students perceived behavior and their AI acceptance for effective integration of AI tools and applications in teaching and learning of teacher education in India.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Ethics:\u0026nbsp;\u003c/strong\u003eThe researchers have obtained ethical approval from the Mizoram University Human Ethics Committee for conducting this research. The approval letter is with the corresponding author. \u0026nbsp;All procedures performed in studies involving human participants were in accordance with the ethical standards of social science research most commonly the \u003cstrong\u003e1964 Declaration of Helsinki\u003c/strong\u003e and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent of Participation:\u003c/strong\u003e Similarly, the researcher has obtained the participant\u0026apos;s consent to participate in the study, and the results of the study will be published in the form of a journal article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe author(s) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Available statement\u003c/strong\u003e-\u0026nbsp;The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e- there is no funding for conducting and publishing this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contributions:\u0026nbsp;\u003c/strong\u003eThe corresponding author or the 1\u003csup\u003est\u003c/sup\u003e author has developed the conceptual framework and analyzed the data. The second and third authors collected the data and tabulated the data\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e- NA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eThe author(s) acknowledge the participants and the experts involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: not applicable\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAkinwalere, S. N., \u0026amp; Ivanov, V. (2022). Artificial intelligence in higher education: challenges and opportunities. Border Crossing,12(1),1-15.https://doi.org/10.21125/inted.2020.0644\u003c/li\u003e\n \u003cli\u003eAlKailani, M. (2016). Factors Affecting the Adoption of Internet Banking in Jordan: An Extended TAM Model. 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Wang, (2022) \u0026ldquo;Artificial Intelligence for Education andTeaching,\u0026rdquo; Wireless Communications and Mobile Computing,vol.https://doi.org/10.1155/2022/4750018\u003c/li\u003e\n \u003cli\u003eYajing Xue, Yijun Wang, (2022) Artificial Intelligence for Education and Teaching\u0026quot;, Wireless Communications and Mobile Computing, vol. 2022, Article ID 4750018, 10 pages, 2022. https://doi.org/10.1155/2022/4750018\u003c/li\u003e\n\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":"Artificial Intelligence, Teacher Education, Perceived Behavior, TAM, SEM, Pre-service Teachers","lastPublishedDoi":"10.21203/rs.3.rs-7949370/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7949370/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe application of Artificial Intelligence (AI) in the field of education in general and teacher education in particular has attracted great interest from the public, governments and academia. With intelligent machines enabling higher-order cognitive functions like thinking, perceiving, learning, problem-solving, and decision-making, along with advancements in data collection and aggregation, analytics, and computer processing power, artificial intelligence technology is having a steadily growing impact on teacher education. The aim of the study to analyses Pre-service Secondary Teachers\u0026rsquo; Perceptions on Artificial Intelligence Acceptance in learning using the structure equation modeling (SEM). It also analyses the benefits and draw backs of use of AI in learning process of teacher education. Keeping in view the objectives of the study and nature of the problem Mixed mode method of research has been used for the present study. Data has collected from307 pre-service secondary teachers with a 5-point interval scale ranging strongly agree to strongly disagree developed by the researcher and Focus Group Discussion (FGD).The findings of the study reveals that the critical constructs of TAM theory are useful to measure pre-service Secondary Teachers\u0026rsquo; perceived behavior towards their AI acceptance in learning. An AI technology may scan student voice, gauge how much they have learnt, and provide appropriate guidance or regulations. Similarly, Human psychology should not be ignored while accepting AI in teaching learning process and Preventive and supportive software should be developed.\u003c/p\u003e","manuscriptTitle":"Pre-service Secondary Teachers’ Perceptions on Artificial Intelligence Acceptance in Teaching – learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 08:58:57","doi":"10.21203/rs.3.rs-7949370/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":"314c220d-0a15-4473-8e96-50ec68d58fde","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T05:40:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 08:58:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7949370","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7949370","identity":"rs-7949370","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}