Cyberpsychology: Validity of the AI Chatbots Usage Scale for University Students

Cyberpsychology: Validity of the AI Chatbots Usage Scale for University Students | 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 Cyberpsychology: Validity of the AI Chatbots Usage Scale for University Students Ashraf Ibrahim, Mohamed Nemt-allah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5953281/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 This study developed and validated the AI Chatbots Usage Scale for assessing university students' engagement with artificial intelligence chatbots in higher education. The research employed a quantitative methodology with 374 male undergraduate students from Al-Azhar university. Through rigorous psychometric analysis, including exploratory and confirmatory factor analyses, a four-factor structure emerged: Ease of Use, Perceived Usefulness, Trust, and Accessibility. The scale demonstrated excellent reliability (McDonald's ω = .911, Cronbach's α = .911) and strong construct validity, supported by good model fit indices ( CMIN/DF = 1.622, CFI = .940, RMSEA = .041). Factor analysis revealed that the four dimensions collectively explained 47.360% of the total variance, with factor loadings ranging from .519 to .729. The final 27-item scale showed robust internal consistency across all factors, with the highest mean scores observed in Ease of Use ( M = 29.07, SD = 6.02) and the strongest correlation between Trust and Perceived Usefulness (.899). These findings provide educators and researchers with a validated instrument for measuring AI chatbot usage in academic settings, while offering insights for improving implementation strategies in higher education. The scale's psychometric properties support its utility for evaluating and enhancing AI chatbot integration in educational contexts. AI chatbots educational technology scale validation higher education psychometric analysis Figures Figure 1 Introduction The integration of artificial intelligence (AI) chatbots in higher education has emerged as a transformative force, revolutionizing traditional teaching and learning paradigms across global academic institutions. Meta-analytic evidence suggests that chatbot technology yields medium-to-high effects on learning outcomes, particularly in areas of explicit reasoning and knowledge retention (Deng & Yu, 2023). While Lin and Yu (2023) identified diverse applications across educational contexts, Adiguzel et al. (2023) highlighted both the transformative potential and practical challenges of implementation. This technological evolution spans multiple domains, from language acquisition and cultural learning (Mageira et al., 2022; Shimichev & Rotanova, 2023) to personalized tutoring and automated educational services (Khidir & Sa'ari, 2022; Pérez et al., 2020). The adoption landscape of chatbots in higher education reveals complex patterns of student engagement and institutional integration. Research indicates that students' adoption of chatbots is primarily influenced by perceived convenience, enhanced performance expectations, and habitual use patterns (Malik et al., 2021; Almahri et al., 2024). Empirical studies demonstrate that chatbots can function as effective educational agents, providing support comparable to human tutors (Pérez et al., 2020) and significantly improving academic performance and student-teacher interactions (Chamorro-Atalaya et al., 2023). While Wadhawan et al. (2023) highlight the success of 24/7 information accessibility, students maintain some skepticism regarding chatbots' precision and reliability, particularly in specialized fields such as medical education (Al Kahf et al., 2023) and administrative support systems (Parrales-Bravo et al., 2024). The significance of understanding student interactions with AI tools has become a crucial focus in higher education research (Crawford et al., 2024; Fošner, 2024). Studies by Ezeoguine and Eteng-Uket (2024) and Romero-Rodríguez et al. (2023) indicate that while students increasingly recognize AI tools' efficiency and potential for personalized learning experiences, current data reveals that only 27.5% of university students demonstrate awareness and active usage of these technologies (Ezeanya et al., 2024). This limited adoption rate underscores the need for more effective integration strategies and better understanding of student engagement patterns. Baker et al. (2023) emphasized the psychological implications of classroom chatbots as intermediaries toward computational intelligence, while Pernaa et al. (2024) noted the significance of personal learning environments enabled by AI chatbots in shaping individual educational experiences. Current research by Cheng (2023) indicates that chatbots positively influence students' motivation and problem-solving capabilities, suggesting the need for robust psychometric instruments to measure students' engagement with these technologies. The implementation of chatbots has proven especially beneficial in specific contexts, such as language learning, where studies have shown enhanced motivation and satisfaction levels among students (Klimova & Seraj, 2023; Vanichvasin, 2021). These findings are particularly significant given the increasing globalization of higher education and the growing demand for language learning support systems. Despite these advancements, there remains a significant gap in the literature regarding validated scales specifically designed to measure chatbot usage patterns among university students (Köhler & Hartig, 2024; Lin, 2024). While existing research has explored various aspects such as chatbot acceptance and implementation, comprehensive measurement tools that capture the multifaceted nature of student-chatbot interactions are notably lacking. Studies by Bubaš et al. (2023) and Hultberg et al. (2024) indicate that student perceptions and usage patterns vary significantly across academic disciplines and educational levels, highlighting the need for flexible and robust assessment frameworks. Recent efforts to address this measurement gap include the development of usability scales like BUS-15 (Borsci et al., 2021) and personality inference metrics (Fan et al., 2023). Integration models such as UTAUT and ECM have identified key factors influencing adoption, including perceived usefulness, confirmation, and personal innovativeness (Tian et al., 2024). Notably, Nemt-allah et al. (2024) established a comprehensive ChatGPT Usage Scale revealing three key dimensions: Academic Writing Aid, Academic Task Support, and Reliance and Trust. These findings complement earlier research by Strzelecki (2023), who identified varying adoption patterns across academic disciplines and key motivational factors influencing student engagement with AI chatbots. Implementation studies by Tian et al. (2024) and Rahim et al. (2022) have illuminated the crucial roles of trust, perceived confirmation, and performance expectancy in adoption, while innovative projects like MERLIN have demonstrated positive outcomes in students' learning processes (Neo, 2022). Halvankar (2024) emphasizes the sophisticated AI algorithms underlying these systems' ability to interpret and respond to user demands effectively, highlighting the technical complexity of creating reliable and responsive chatbot systems for educational contexts. However, significant concerns persist regarding the impact of chatbots on academic integrity and social engagement (Amoozadeh et al., 2024; Ralhan, 2024). Research by Schei et al. (2024), Ramadan et al. (2024), and Rudolph et al. (2023) has raised important considerations about accuracy, critical thinking, and the potential for over-reliance on AI systems. While AI-driven systems can enhance student engagement through personalized assistance (Fenu et al., 2024; Fox et al., 2024), the potential negative effects on psychological well-being and social interaction necessitate careful consideration in implementation strategies (Vázquez-Parra et al., 2024). The complexity of these challenges is further highlighted by the work of Hmoud et al. (2024) and Tao et al. (2024), who have contributed valuable insights into assessment frameworks and usage patterns across different academic contexts. Their research emphasizes the need for balanced approaches that maximize the benefits of chatbot technology while mitigating potential risks to academic integrity and student development. This growing body of evidence suggests that while chatbots offer significant potential for enhancing educational experiences, their implementation must be guided by robust empirical research and validated assessment tools. This study aims to address the critical need for a standardized measurement tool by developing and validating a comprehensive scale for assessing chatbot AI usage among university students. The research builds upon existing frameworks while incorporating new dimensions that reflect the evolving nature of AI-assisted learning in higher education. Through this investigation, we seek to provide educators, researchers, and administrators with a reliable instrument for understanding and optimizing chatbot interactions in educational settings. The development of such a tool is crucial for several reasons: it will enable more accurate assessment of implementation effectiveness, facilitate comparative studies across different educational contexts, and provide valuable insights for improving chatbot design and integration strategies. Ultimately, this research aims to contribute to the growing body of knowledge in educational technology and cyberpsychology, helping to shape the future of AI-enhanced learning in higher education. Method Research Design The present study employed a quantitative research methodology to develop and validate a AI Chatbots Usage Scale for university students. The research was conducted during the 2023–2024 academic year at the Faculty of Education at Tafhna Al-Ashraf, Dakahlia, Egypt. The study aimed to establish the psychometric properties of the scale while examining patterns of AI chatbot usage among undergraduate students. Participants The sample consisted of 374 male undergraduate students, which represented 62.33% of the total sample of 600, who were active users of AI chatbots. Participants ranged in age from 17 to 23 years ( M = 18.52, SD = 1.23), with the majority (80.5%) being 18 years old. The sample distribution across academic years showed a concentration in the third year (83.2%), followed by first year (13.4%), fourth year (3.2%), and second year (0.3%). This distribution provided a robust representation of students with sufficient exposure to academic requirements and potential chatbot applications. Academic specializations within the sample reflected the diverse nature of the faculty's programs. Special Education represented the largest group (40.1%), followed by English (25.9%), Arabic (20.1%), and French (10.4%), with smaller representations from Psychology (1.6%) and History (1.9%). This distribution allowed for examination of chatbot usage patterns across different academic disciplines and learning contexts. The sample demonstrated varied academic performance levels, with 11.2% achieving excellent standing, 21.4% very good, 38% good, 17.6% acceptable, and 1.3% weak, while 10.4% were new students without established academic records. The sociodemographic profile of participants revealed important characteristics of the study population. A significant majority (73%) resided in rural areas, with the remainder (27%) from urban locations. Living arrangements varied, with 67.1% residing with family, 25.1% in independent housing, and 7.8% in university housing. Parental education levels showed considerable variation, with fathers' education distributed across primary (4%), preparatory (4.3%), secondary (17.9%), university (43%), and postgraduate (10.2%) levels, while mothers' education followed a similar pattern: primary (3.5%), preparatory (2.7%), secondary (28.3%), university (32.4%), and postgraduate (9.1%). Instruments The research instrument consisted of an initial pool of 50 items designed to assess various dimensions of AI chatbot usage among university students. The scale development process involved comprehensive literature review, expert consultation, and pilot testing. Items were rated on a 5-point Likert scale ranging from 1 (Never) to 5 (Always), allowing for nuanced measurement of usage patterns and attitudes. The response options were clearly defined as Always (5), Often (4), Sometimes (3), Rarely (2), and Never (1) to ensure consistent interpretation across participants. Data Collection Procedures Data collection procedures were standardized to ensure reliability and validity of responses. All participants were active users of AI chatbots, which was established as a prerequisite for inclusion in the study. The research team administered the scale under controlled conditions, providing clear instructions and ensuring participant anonymity. The 100% response rate indicated successful engagement with the target population and effective data collection procedures. The demographic questionnaire accompanying the scale gathered detailed information about participants' academic and personal characteristics. This included age, academic year, specialization, academic performance, residence location, living arrangements, and parental education levels. These variables were coded systematically to facilitate comprehensive analysis of potential relationships with chatbot usage patterns. Statistical analyses were conducted using SPSS version 27.0 and AMOS version 26.0 to examine the psychometric properties of the scale. The analysis plan included several stages: initial item analysis, factor analysis to establish construct validity, reliability assessment through internal consistency measures, and examination of relationships between demographic variables and scale scores. The significance level was set at p < .05 for all statistical tests, following standard research practices in social sciences. Results The psychometric analysis of the AI Chatbots Usage Scale yielded robust findings that support its validity and reliability for male university students in Egyptian higher education. The analysis process included several statistical procedures to establish the scale's psychometric properties, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and various reliability assessments. Initial analysis of sampling adequacy yielded promising results. The Kaiser-Meyer-Olkin (KMO) measure produced a value of .941, indicating excellent sampling adequacy for factor analysis. Bartlett's Test of Sphericity was significant (χ² = 5817.818, df = 703, p < .001), confirming the correlation matrix's suitability for factor analysis. These preliminary findings supported proceeding with the factor analysis. The exploratory factor analysis, conducted using principal component analysis with Varimax rotation, revealed a four-factor structure that collectively explained 47.360% of the total variance. The variance explanation was distributed across the factors as follows: Ease of Use (15.215%), Perceived Usefulness (13.072%), Trust (9.623%), and Accessibility (7.815%). This structure emerged as the most interpretable solution, with items demonstrating clear and meaningful loadings on their respective factors, as shown in Table 1 . Table 1 Factor Loading Analysis of 27-Item AI Chatbots Usage Scale Items Ease of Use Perceived Usefulness Trust Accessibility M SD Item total correlation 23. I find AI Chatbots adaptable across different academic subjects .690 3.59 1.08 .64 ** 28. Navigating between academic topics in AI Chatbots is straightforward .636 3.72 1.01 .62 ** 27. I can efficiently access AI Chatbots throughout my academic day .624 3.56 1.12 .59 ** 24. The AI system's responses are easily comprehensible .623 3.64 1.06 .53 ** 22. Formulating academic questions in AI Chatbots is straightforward .616 3.63 1.07 .57 ** 39. I can utilize AI Chatbots for academic purposes without complications .599 3.66 1.01 .59 ** 33. I can effectively use AI Chatbots in various academic settings .575 3.53 1.01 .56 ** 42. I can effectively modify my queries to obtain more precise academic information .557 3.71 1.15 .59 ** 5. The AI system aids in summarizing and reviewing lecture content comprehensively .695 3.11 1.22 .59 ** 4. AI Chatbots help me optimize my study schedule effectively .656 3.21 1.17 .53 ** 3. The AI Chatbots system improves the quality of my academic writing and reports .611 3.57 1.06 .58 ** 9. The AI system provides relevant examples that enhance my subject comprehension .602 3.47 1.11 .57 ** 2. AI chatbots assist me in completing my university assignments efficiently .589 3.29 1.09 .59 ** 8. AI Chatbots facilitate accurate translation of academic terminology .574 3.64 1.06 .60 ** 7. The AI system enhances my preparation for examinations and presentations .556 3.56 1.14 .62 ** 12. I feel secure sharing my academic queries with the AI system .691 3.17 1.10 .60 ** 11. I feel secure sharing my academic queries with the AI system .686 3.19 1.04 .56 ** 16. I trust the AI system's recommendations regarding academic references .586 3.36 1.03 .58 ** 17. I have confidence in the confidentiality of my academic interactions with AI .581 3.32 1.09 .53 ** 13. I rely on AI Chatbots to verify my academic information .540 3.15 1.07 .69 ** 15. I feel confident using AI Chatbots for university research purposes .519 3.29 1.08 .65 ** 46. The system is easily accessible in libraries and classrooms .729 3.23 1.14 .46 ** 47. AI Chatbots are compatible with university laboratory equipment .662 3.28 1.19 .36 ** 45. I can use AI Chatbots even with limited internet connectivity .632 2.81 1.25 .36 ** 50. Clear guidance for academic use of AI Chatbots is readily available .626 3.07 1.12 .40 ** 41. I can access AI Chatbots from anywhere within the university campus .594 3.04 1.16 .52 ** 30. Previously saved AI Chatbots are easily retrievable for review .557 3.10 1.17 .56 ** Note . ** p < .01. As presented in Table 2 , factor loadings for the final 27-item scale showed strong psychometric properties. The Ease of Use factor comprised eight items with loadings ranging from .557 to .690, reflecting students' perceptions of the straightforward nature of AI chatbot interactions. The Perceived Usefulness factor included seven items with loadings from .556 to .695, capturing the practical academic benefits of AI chatbot usage. The Trust factor contained six items with loadings between .519 and .691, addressing students' confidence in AI chatbot systems. The Accessibility factor encompassed six items with loadings from .557 to .729, focusing on the technical and physical availability of AI chatbots in academic settings. The confirmatory factor analysis provided strong support for the four-factor structure. The model demonstrated good fit to the data across multiple indices: CMIN/DF = 1.622, GFI = .905, AGFI = .888, NFI = .858, CFI = .940, and RMSEA = .041 (90% CI [.034, .047]). These values indicate that the four-factor model provides an adequate representation of the scale's underlying structure for the target population. The standardized factor loadings from the CFA ranged from .495 to .744, with all loadings being statistically significant (p < .001), supporting the convergent validity of the scale. The structural relationships between factors and their respective items are illustrated in Fig. 1 . The reliability analysis revealed strong internal consistency for both the overall scale and its individual factors, as detailed in Table 2 . The overall scale demonstrated excellent reliability with McDonald's ω = .911 (95% CI [.898, .924]), Cronbach's α = .911 (95% CI [.897, .923]), and Guttman's λ2 = .913. The average inter-item correlation of .279 indicated good internal consistency without redundancy among items. Table 2 Reliability Coefficients and Descriptive Statistics for the AI Chatbots Usage Scale Variable McDonald's ω Cronbach's α Guttman's λ2 Greatest Lower Bound M SD Ease of Use 0.856 0.855 0.856 0.885 29.07 6.02 Perceived Usefulness 0.828 0.827 0.829 0.863 23.63 5.53 Trust 0.807 0.806 0.807 0.830 19.50 4.59 Accessibility 0.754 0.752 0.754 0.788 18.55 4.72 Total Score 0.911 0.911 0.913 0.956 90.76 16.47 As shown in Table 2 , individual factors showed satisfactory reliability coefficients: Ease of Use (ω = .856, α = .855, λ2 = .856), Perceived Usefulness (ω = .828, α = .827, λ2 = .829), Trust (ω = .807, α = .806, λ2 = .807), and Accessibility (ω = .754, α = .752, λ2 = .754). The construct validity of the scale was further supported by the Average Variance Extracted (AVE) value of .625 and Composite Reliability (CR) of .866, both exceeding recommended thresholds. The Maximum Reliability coefficient (MaxR(H)) of .903 provided additional evidence of the scale's strong reliability. The Greatest Lower Bound (GLB) values for both the overall scale (.956) and individual factors (ranging from .788 to .885) suggested that the true reliability of the scale is likely to be high, even under conservative estimates. Item-total correlations ranged from .36 to .69 (p < .001), indicating that all items contributed meaningfully to the overall scale score. The factor correlations ranged from moderate to strong (.558 to .899), demonstrating that the factors were related but sufficiently distinct to represent separate components of AI chatbot usage in academic settings. The highest correlation was observed between Trust and Perceived Usefulness (.899), suggesting a strong relationship between students' trust in AI chatbots and their perception of its academic utility. Mean scores across the factors revealed interesting patterns of AI chatbot usage among the sample. Ease of Use showed the highest mean score ( M = 29.07, SD = 6.02), followed by Perceived Usefulness ( M = 23.07, SD = 5.16), Trust ( M = 19.50, SD = 4.59), and Accessibility ( M = 18.55, SD = 4.72). These results suggest that while students generally find AI chatbots easy to use, there may be room for improvement in areas related to accessibility and trust. These results collectively suggest that the AI Chatbots Usage Scale demonstrates strong psychometric properties when applied to male university students in Egyptian higher education, with evidence supporting both its factorial validity and reliability. The four-factor structure provides a comprehensive framework for understanding AI chatbot usage patterns in academic settings, while the high reliability coefficients indicate that the scale provides consistent measurements of the construct. Discussion The present study's development and validation of the AI Chatbots Usage Scale provides significant insights into how university students engage with AI chatbots in educational settings. The findings reveal a robust four-factor structure that both aligns with and extends existing theoretical frameworks, while offering practical implications for educational technology implementation. The four-factor structure of the AI Chatbots Usage Scale demonstrates strong theoretical alignment with established technology adoption frameworks, particularly the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM). The Ease of Use factor corresponds directly with UTAUT's effort expectancy construct, as evidenced by Hidayat-ur-Rehman and Ibrahim (2023), who found that educators' effort expectancy significantly influences their intention to adopt chatbots in educational settings. Similarly, Al-Sharafi et al. (2022) demonstrated that perceived usefulness, which aligns with UTAUT's performance expectancy, is among the most significant factors affecting the sustainable use of AI-based chatbots in education. The Trust factor emerges as a critical extension to traditional technology adoption models, particularly in the context of AI chatbots in education. Chen et al. (2024) identified trust as a significant influence on AI chatbot adoption among research scholars, while Rahim et al. (2022) emphasized the role of perceived trust in determining the effectiveness of chatbot adoption in higher education institutions. This alignment suggests that trust serves as a fundamental psychological prerequisite for technology acceptance, extending beyond the basic constructs of conventional adoption frameworks. The scale's strong reliability coefficients (McDonald's ω = .911, Cronbach's α = .911) align with and even exceed the reliability standards found in recent chatbot assessment tools, such as those developed by Nemt-allah et al. (2024) and Borsci et al. (2021). These values indicate exceptional internal consistency, making it a reliable tool for both research and practical applications in educational settings. The high Greatest Lower Bound value (.956) further supports the robustness of the scale, suggesting that its true reliability may be even higher than indicated by traditional reliability measures. The moderate to strong factor correlations (.558 to .899) demonstrate that while the four dimensions are related, they remain sufficiently distinct to capture different aspects of AI chatbot usage. This finding aligns with recent work by Tian et al. (2024), who identified similar patterns in their study of trust, perceived confirmation, and performance expectancy in chatbot adoption. The high mean score for Ease of Use (M = 29.07) can be attributed to its fundamental role in shaping students' initial acceptance and continued usage of AI chatbots. As demonstrated by Liu et al. (2024) and Goli et al. (2023), perceived ease of use directly influences users' attitudes and behavioral intentions toward AI chatbot adoption. This finding is particularly relevant for university students who, as digital natives, have high expectations for seamless technological interactions. The prominence of ease of use as a leading factor aligns with Pillai and Sivathanu's (2020) research, which identifies it as a crucial predictor of adoption intention. The strong correlation between Trust and Perceived Usefulness (.899) reflects their reciprocal relationship in shaping user acceptance and behavior. Research by Prakash et al. (2023) emphasizes how conversational cues and perceived functional attributes contribute to building trust, ultimately determining behavioral intentions to use AI chatbots. This finding is further supported by Yen and Chiang (2020), who found that characteristics such as credibility, competence, and anthropomorphism positively influence trust, which in turn amplifies perceived usefulness. The relatively lower scores for Accessibility (M = 18.55) may be attributed to several underlying factors related to user interface design and technical barriers in AI chatbot implementation. This finding aligns with Aditi et al. (2024), who emphasized that chatbot accessibility is heavily dependent on the quality of user interface design and adherence to inclusive digital environment standards. Additionally, Nadarzynski et al. (2019) found that while users may be generally receptive to chatbot technology, technical hesitancy and interface-related barriers can significantly compromise engagement levels. The predominantly male sample from Egyptian higher education provides important insights while raising considerations about generalizability. The cultural and educational context of Egyptian universities, particularly regarding technology adoption and implementation, may influence how students perceive and interact with AI chatbots. The diverse academic specializations represented in the sample (ranging from Special Education to French) suggest broad applicability across disciplines, though discipline-specific patterns of usage and acceptance may warrant further investigation. Based on the factor scores, several recommendations emerge for improving AI chatbot implementation in educational settings. First, institutions should prioritize maintaining and enhancing the ease of use of AI chatbot interfaces, as this factor significantly influences adoption. Second, efforts should be made to address accessibility barriers through improved infrastructure and more seamless integration with existing educational platforms. Third, building trust through transparency about AI capabilities and limitations could enhance both trust and perceived usefulness simultaneously. Educational technology developers can use these findings to guide feature prioritization and interface design. The strong relationship between trust and perceived usefulness suggests that developers should focus on features that demonstrate reliability and accuracy, while maintaining transparent communication about the system's capabilities and limitations. Several limitations should be considered when interpreting these results. The predominantly male sample from a single cultural context limits generalizability across gender and cultural groups. Future validation studies should include more diverse samples across different cultural and educational contexts. Longitudinal studies could help establish the scale's stability over time and its predictive validity for actual AI chatbot usage patterns. These findings have significant implications for institutional policies regarding AI chatbot integration in higher education. The scale can serve as a valuable tool for evaluating technology initiatives and guiding resource allocation decisions. Institutions should consider using this scale as part of their assessment strategy when implementing AI chatbot solutions, particularly focusing on accessibility improvements and trust-building measures. The scale's robust psychometric properties make it a valuable tool for measuring the effectiveness of AI chatbot implementations and informing evidence-based decision-making in educational technology investments. However, institutions should consider their specific contextual factors when applying these findings to policy decisions. Conclusion The AI Chatbots Usage Scale represents a significant contribution to the field of educational technology assessment, providing a reliable and valid tool for measuring student engagement with AI chatbots. The four-factor structure offers a comprehensive framework for understanding and evaluating AI chatbot implementation in educational settings, while the specific factor findings provide practical guidance for improvement and optimization. Future research should focus on validating these findings across diverse populations and contexts, while educational institutions can use this scale to inform their technology integration strategies and policies. Abbreviations AI Artificial Intelligence AGFI Adjusted Goodness of Fit Index AMOS Analysis of Moment Structures AVE Average Variance Extracted CFA Confirmatory Factor Analysis CFI Comparative Fit Index CMIN/DF Chi-square/Degrees of Freedom CR Composite Reliability ECM Expectation Confirmation Model EFA Exploratory Factor Analysis GFI Goodness of Fit Index GLB Greatest Lower Bound KMO Kaiser-Meyer-Olkin M Mean MaxR(H) Maximum Reliability coefficient NFI Normed Fit Index RMSEA Root Mean Square Error of Approximation SD Standard Deviation SPSS Statistical Package for Social Sciences TAM Technology Acceptance Model UTAUT Unified Theory of Acceptance and Use of Technology Declarations Ethical Approval The study protocol was approved by the Research Ethics Committee of the Faculty of Education at Al-Azhar University, Dakahlia, Egypt (Ref. No. EDU-REC-2024-0310). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Consent for publication Not Applicable. Consent to participate Written informed consent was obtained from all student participants. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Funding Not applicable Competing interests The authors declare no competing interests. Author contributions AI designed the research methodology and performed the statistical analyses, including factor analyses, reliability testing, and data visualization. MN conducted the literature review and developed the theoretical framework. AI implemented the software analysis using SPSS and AMOS, while MN prepared the initial scale items in English. AI validated the statistical procedures and interpreted the results, while MN refined the language and content of the scale items. Both authors contributed to data collection and interpretation. MN wrote the original manuscript draft, and AI provided critical revisions for statistical content. Both authors read and approved the final manuscript. Acknowledgements The authors express gratitude to the Faculty of Education at Tafhna Al-Ashraf, Dakahlia, undergraduate students, an expert committee, colleagues, Al-Azhar University, and anonymous reviewers for their support, participation, and contributions to the translation and adaptation of the Scale, as well as the support of their department. References Adiguzel, T., Kaya, M., & Cansu, F. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology , 15(3), ep429. https://doi.org/10.30935/cedtech/13152. Aditi, A., Saxena, H., Prof, G., & Malik, A. (2024). 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Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digital Health , 5, 2055207619871808. https://doi.org/10.1177/2055207619871808. Nemt-Allah, M., Khalifa, W., Badawy, M., Elbably, Y., & Ibrahim, A. (2024). Validating the ChatGPT Usage Scale: psychometric properties and factor structures among postgraduate students. BMC psychology , 12 (1), 497.‏ https://doi.org/10.1186/s40359-024-01983-4 Neo, M. (2022). The merlin project: Malaysian students’ acceptance of an ai chatbot in their learning process. Turkish Online Journal of Distance Education , 23(3), 31–48. https://doi.org/10.17718/tojde.1137122. Parrales-Bravo, F., Caicedo-Quiroz, R., Barzola-Monteses, J., Guillén-Mirabá, J., & Guzmán-Bedor, O. (2024). CSM: A Chatbot solution to manage student questions about payments and enrollment in university. IEEE Access, 12, 74669–74680. https://doi.org/10.1109/ACCESS.2024.3404008 Pérez, J., Daradoumis, T., & Puig, J. (2020). Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education , 28(6), 1549–1565. https://doi.org/10.1002/cae.22326. Pernaa, J., Ikävalko, T., Takala, A., Vuorio, E., Pesonen, R., & Haatainen, O. (2024). Artificial Intelligence Chatbots in Chemical Information Seeking: Narrative Educational Insights via a SWOT Analysis. Informatics , 11(2), 20. https://doi.org/10.3390/informatics11020020. Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management , 32(10), 3199–3226. https://doi.org/10.1108/ijchm-04-2020-0259. Prakash, A., Joshi, A., Nim, S., & Das, S. (2023). Determinants and consequences of trust in AI-based customer service chatbots. The Service Industries Journal , 43(9–10), 642–675. https://doi.org/10.1080/02642069.2023.2166493. Rahim, N., Iahad, N., Yusof, A., & Al-Sharafi, M. (2022). AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach. Sustainability , 14 (19), 12726. https://doi.org/10.3390/su141912726. Ralhan, P. (2024). Artificial intelligence in higher studies: Use of AI-based tools by university students and its challenges. International Journal of Advanced Research , 12 , 1282–1285. https://doi.org/10.21474/ijar01/19563. Ramadan, F., Sari, P. K., & Murti, Y. R. (2024). Examining Students’ Motivation to Continue Using AI-Chatbot for Academic Assignment. Jurnal Sistem Informasi , 20 (2), 18–31. https://doi.org/10.21609/jsi.v20i2.1417. Romero-Rodríguez, J., Ramírez-Montoya, M., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at University as a Tool for Complex Thinking: Students' Perceived Usefulness. Journal of New Approaches in Educational Research , 12, 323–339. https://doi.org/10.7821/naer.2023.7.1458. Rudolph, J., Tan, S., & Tan, S. (2023). War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Ed-tech Reviews, 6 (1), 364–389. https://doi.org/10.37074/jalt.2023.6.1.23. Schei, O., Møgelvang, A., & Ludvigsen, K. (2024). Perceptions and Use of AI Chatbots among Students in Higher Education: A Scoping Review of Empirical Studies. Education Sciences , 14 (8), 922. https://doi.org/10.3390/educsci14080922. Shimichev, A. S., & Rotanova, M. B. (2023). Chatbot technology as an artificial intelligence tool in foreign language education. 2022 International Conference on Quality Management, Transport and Information Security, Information Technologies , 97–100. https://doi.org/10.1109/itqmtis58985.2023.10346566 Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments , 32 (9), 5142–5155. https://doi.org/10.1080/10494820.2023.2209881. Tao, W., Yang, J., & Qu, X. (2024). Utilization of, Perceptions on, and Intention to Use AI Chatbots Among Medical Students in China: National Cross-Sectional Study. JMIR medical education , 10, e57132. https://doi.org/10.2196/57132. Tian, W., Ge, J., Zhao, Y., & Zheng, X. (2024). AI Chatbots in Chinese higher education: adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models. Frontiers in Psychology , 15, 1268549. https://doi.org/10.3389/fpsyg.2024.1268549. Vanichvasin, P. (2021). Chatbot Development as a Digital Learning Tool to Increase Students’ Research Knowledge. International Education Studies . 14 (2), 44–53. https://doi.org/10.5539/IES.V14N2P44. Vázquez-Parra, J., Henao-Rodríguez, C., Lis-Gutiérrez, J., & Palomino-Gámez, S. (2024). Importance of University Students’ Perception of Adoption and Training in Artificial Intelligence Tools. Societies , 14 (8), 141. https://doi.org/10.3390/soc14080141. Wadhawan, I., Jain, T., & Galhotra, B. (2023). Usage and Adoption of Chatbot in Education Sector. 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS) , 1097–1103. https://doi.org/10.1109/ICICCS56967.2023.10142511. Yen, C., & Chiang, M. (2020). Trust me, if you can: a study on the factors that influence consumers’ purchase intention triggered by chatbots based on brain image evidence and self-reported assessments. Behaviour & Information Technology , 40(11), 1177–1194. https://doi.org/10.1080/0144929X.2020.1743362. 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. 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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-5953281","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444580408,"identity":"6d2d7400-740e-434a-b751-d15d2f1f2c6e","order_by":0,"name":"Ashraf Ibrahim","email":"data:image/png;base64,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","orcid":"","institution":"Al- Azhar University","correspondingAuthor":true,"prefix":"","firstName":"Ashraf","middleName":"","lastName":"Ibrahim","suffix":""},{"id":444580411,"identity":"3a48e5a9-ba1b-4a27-8fa7-9e015fb2288b","order_by":1,"name":"Mohamed Nemt-allah","email":"","orcid":"","institution":"Al- Azhar University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Nemt-allah","suffix":""}],"badges":[],"createdAt":"2025-02-03 18:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5953281/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5953281/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80895129,"identity":"d6e2ca95-990a-42d2-94e0-efdac2f33199","added_by":"auto","created_at":"2025-04-18 11:34:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStandardized CFA for the four-factor 27-Item AI Chatbots Usage Scale structure model\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5953281/v1/5422a966884467170d94eef3.jpeg"},{"id":83016181,"identity":"c250e47a-6599-42ca-9f5a-4bbadbd05862","added_by":"auto","created_at":"2025-05-19 06:23:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1021833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5953281/v1/3d62c343-bd36-4a5a-8294-fe67e6b7d542.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cyberpsychology: Validity of the AI Chatbots Usage Scale for University Students","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe integration of artificial intelligence (AI) chatbots in higher education has emerged as a transformative force, revolutionizing traditional teaching and learning paradigms across global academic institutions. Meta-analytic evidence suggests that chatbot technology yields medium-to-high effects on learning outcomes, particularly in areas of explicit reasoning and knowledge retention (Deng \u0026amp; Yu, 2023). While Lin and Yu (2023) identified diverse applications across educational contexts, Adiguzel et al. (2023) highlighted both the transformative potential and practical challenges of implementation. This technological evolution spans multiple domains, from language acquisition and cultural learning (Mageira et al., 2022; Shimichev \u0026amp; Rotanova, 2023) to personalized tutoring and automated educational services (Khidir \u0026amp; Sa'ari, 2022; P\u0026eacute;rez et al., 2020).\u003c/p\u003e \u003cp\u003eThe adoption landscape of chatbots in higher education reveals complex patterns of student engagement and institutional integration. Research indicates that students' adoption of chatbots is primarily influenced by perceived convenience, enhanced performance expectations, and habitual use patterns (Malik et al., 2021; Almahri et al., 2024). Empirical studies demonstrate that chatbots can function as effective educational agents, providing support comparable to human tutors (P\u0026eacute;rez et al., 2020) and significantly improving academic performance and student-teacher interactions (Chamorro-Atalaya et al., 2023). While Wadhawan et al. (2023) highlight the success of 24/7 information accessibility, students maintain some skepticism regarding chatbots' precision and reliability, particularly in specialized fields such as medical education (Al Kahf et al., 2023) and administrative support systems (Parrales-Bravo et al., 2024).\u003c/p\u003e \u003cp\u003eThe significance of understanding student interactions with AI tools has become a crucial focus in higher education research (Crawford et al., 2024; Fošner, 2024). Studies by Ezeoguine and Eteng-Uket (2024) and Romero-Rodr\u0026iacute;guez et al. (2023) indicate that while students increasingly recognize AI tools' efficiency and potential for personalized learning experiences, current data reveals that only 27.5% of university students demonstrate awareness and active usage of these technologies (Ezeanya et al., 2024). This limited adoption rate underscores the need for more effective integration strategies and better understanding of student engagement patterns. Baker et al. (2023) emphasized the psychological implications of classroom chatbots as intermediaries toward computational intelligence, while Pernaa et al. (2024) noted the significance of personal learning environments enabled by AI chatbots in shaping individual educational experiences.\u003c/p\u003e \u003cp\u003eCurrent research by Cheng (2023) indicates that chatbots positively influence students' motivation and problem-solving capabilities, suggesting the need for robust psychometric instruments to measure students' engagement with these technologies. The implementation of chatbots has proven especially beneficial in specific contexts, such as language learning, where studies have shown enhanced motivation and satisfaction levels among students (Klimova \u0026amp; Seraj, 2023; Vanichvasin, 2021). These findings are particularly significant given the increasing globalization of higher education and the growing demand for language learning support systems.\u003c/p\u003e \u003cp\u003eDespite these advancements, there remains a significant gap in the literature regarding validated scales specifically designed to measure chatbot usage patterns among university students (K\u0026ouml;hler \u0026amp; Hartig, 2024; Lin, 2024). While existing research has explored various aspects such as chatbot acceptance and implementation, comprehensive measurement tools that capture the multifaceted nature of student-chatbot interactions are notably lacking. Studies by Bubaš et al. (2023) and Hultberg et al. (2024) indicate that student perceptions and usage patterns vary significantly across academic disciplines and educational levels, highlighting the need for flexible and robust assessment frameworks.\u003c/p\u003e \u003cp\u003eRecent efforts to address this measurement gap include the development of usability scales like BUS-15 (Borsci et al., 2021) and personality inference metrics (Fan et al., 2023). Integration models such as UTAUT and ECM have identified key factors influencing adoption, including perceived usefulness, confirmation, and personal innovativeness (Tian et al., 2024). Notably, Nemt-allah et al. (2024) established a comprehensive ChatGPT Usage Scale revealing three key dimensions: Academic Writing Aid, Academic Task Support, and Reliance and Trust. These findings complement earlier research by Strzelecki (2023), who identified varying adoption patterns across academic disciplines and key motivational factors influencing student engagement with AI chatbots.\u003c/p\u003e \u003cp\u003eImplementation studies by Tian et al. (2024) and Rahim et al. (2022) have illuminated the crucial roles of trust, perceived confirmation, and performance expectancy in adoption, while innovative projects like MERLIN have demonstrated positive outcomes in students' learning processes (Neo, 2022). Halvankar (2024) emphasizes the sophisticated AI algorithms underlying these systems' ability to interpret and respond to user demands effectively, highlighting the technical complexity of creating reliable and responsive chatbot systems for educational contexts.\u003c/p\u003e \u003cp\u003eHowever, significant concerns persist regarding the impact of chatbots on academic integrity and social engagement (Amoozadeh et al., 2024; Ralhan, 2024). Research by Schei et al. (2024), Ramadan et al. (2024), and Rudolph et al. (2023) has raised important considerations about accuracy, critical thinking, and the potential for over-reliance on AI systems. While AI-driven systems can enhance student engagement through personalized assistance (Fenu et al., 2024; Fox et al., 2024), the potential negative effects on psychological well-being and social interaction necessitate careful consideration in implementation strategies (V\u0026aacute;zquez-Parra et al., 2024).\u003c/p\u003e \u003cp\u003eThe complexity of these challenges is further highlighted by the work of Hmoud et al. (2024) and Tao et al. (2024), who have contributed valuable insights into assessment frameworks and usage patterns across different academic contexts. Their research emphasizes the need for balanced approaches that maximize the benefits of chatbot technology while mitigating potential risks to academic integrity and student development. This growing body of evidence suggests that while chatbots offer significant potential for enhancing educational experiences, their implementation must be guided by robust empirical research and validated assessment tools.\u003c/p\u003e \u003cp\u003eThis study aims to address the critical need for a standardized measurement tool by developing and validating a comprehensive scale for assessing chatbot AI usage among university students. The research builds upon existing frameworks while incorporating new dimensions that reflect the evolving nature of AI-assisted learning in higher education. Through this investigation, we seek to provide educators, researchers, and administrators with a reliable instrument for understanding and optimizing chatbot interactions in educational settings. The development of such a tool is crucial for several reasons: it will enable more accurate assessment of implementation effectiveness, facilitate comparative studies across different educational contexts, and provide valuable insights for improving chatbot design and integration strategies. Ultimately, this research aims to contribute to the growing body of knowledge in educational technology and cyberpsychology, helping to shape the future of AI-enhanced learning in higher education.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eThe present study employed a quantitative research methodology to develop and validate a AI Chatbots Usage Scale for university students. The research was conducted during the 2023\u0026ndash;2024 academic year at the Faculty of Education at Tafhna Al-Ashraf, Dakahlia, Egypt. The study aimed to establish the psychometric properties of the scale while examining patterns of AI chatbot usage among undergraduate students.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe sample consisted of 374 male undergraduate students, which represented 62.33% of the total sample of 600, who were active users of AI chatbots. Participants ranged in age from 17 to 23 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18.52, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.23), with the majority (80.5%) being 18 years old. The sample distribution across academic years showed a concentration in the third year (83.2%), followed by first year (13.4%), fourth year (3.2%), and second year (0.3%). This distribution provided a robust representation of students with sufficient exposure to academic requirements and potential chatbot applications.\u003c/p\u003e \u003cp\u003eAcademic specializations within the sample reflected the diverse nature of the faculty's programs. Special Education represented the largest group (40.1%), followed by English (25.9%), Arabic (20.1%), and French (10.4%), with smaller representations from Psychology (1.6%) and History (1.9%). This distribution allowed for examination of chatbot usage patterns across different academic disciplines and learning contexts. The sample demonstrated varied academic performance levels, with 11.2% achieving excellent standing, 21.4% very good, 38% good, 17.6% acceptable, and 1.3% weak, while 10.4% were new students without established academic records.\u003c/p\u003e \u003cp\u003eThe sociodemographic profile of participants revealed important characteristics of the study population. A significant majority (73%) resided in rural areas, with the remainder (27%) from urban locations. Living arrangements varied, with 67.1% residing with family, 25.1% in independent housing, and 7.8% in university housing. Parental education levels showed considerable variation, with fathers' education distributed across primary (4%), preparatory (4.3%), secondary (17.9%), university (43%), and postgraduate (10.2%) levels, while mothers' education followed a similar pattern: primary (3.5%), preparatory (2.7%), secondary (28.3%), university (32.4%), and postgraduate (9.1%).\u003c/p\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cp\u003eThe research instrument consisted of an initial pool of 50 items designed to assess various dimensions of AI chatbot usage among university students. The scale development process involved comprehensive literature review, expert consultation, and pilot testing. Items were rated on a 5-point Likert scale ranging from 1 (Never) to 5 (Always), allowing for nuanced measurement of usage patterns and attitudes. The response options were clearly defined as Always (5), Often (4), Sometimes (3), Rarely (2), and Never (1) to ensure consistent interpretation across participants.\u003c/p\u003e\n\u003ch3\u003eData Collection Procedures\u003c/h3\u003e\n\u003cp\u003eData collection procedures were standardized to ensure reliability and validity of responses. All participants were active users of AI chatbots, which was established as a prerequisite for inclusion in the study. The research team administered the scale under controlled conditions, providing clear instructions and ensuring participant anonymity. The 100% response rate indicated successful engagement with the target population and effective data collection procedures.\u003c/p\u003e \u003cp\u003eThe demographic questionnaire accompanying the scale gathered detailed information about participants' academic and personal characteristics. This included age, academic year, specialization, academic performance, residence location, living arrangements, and parental education levels. These variables were coded systematically to facilitate comprehensive analysis of potential relationships with chatbot usage patterns.\u003c/p\u003e \u003cp\u003eStatistical analyses were conducted using SPSS version 27.0 and AMOS version 26.0 to examine the psychometric properties of the scale. The analysis plan included several stages: initial item analysis, factor analysis to establish construct validity, reliability assessment through internal consistency measures, and examination of relationships between demographic variables and scale scores. The significance level was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 for all statistical tests, following standard research practices in social sciences.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe psychometric analysis of the AI Chatbots Usage Scale yielded robust findings that support its validity and reliability for male university students in Egyptian higher education. The analysis process included several statistical procedures to establish the scale's psychometric properties, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and various reliability assessments.\u003c/p\u003e \u003cp\u003eInitial analysis of sampling adequacy yielded promising results. The Kaiser-Meyer-Olkin (KMO) measure produced a value of .941, indicating excellent sampling adequacy for factor analysis. Bartlett's Test of Sphericity was significant (χ\u0026sup2; = 5817.818, df\u0026thinsp;=\u0026thinsp;703, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), confirming the correlation matrix's suitability for factor analysis. These preliminary findings supported proceeding with the factor analysis.\u003c/p\u003e \u003cp\u003eThe exploratory factor analysis, conducted using principal component analysis with Varimax rotation, revealed a four-factor structure that collectively explained 47.360% of the total variance. The variance explanation was distributed across the factors as follows: Ease of Use (15.215%), Perceived Usefulness (13.072%), Trust (9.623%), and Accessibility (7.815%). This structure emerged as the most interpretable solution, with items demonstrating clear and meaningful loadings on their respective factors, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003e\u003cem\u003eFactor Loading Analysis of 27-Item AI Chatbots Usage Scale\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEase of Use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived Usefulness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccessibility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eItem total correlation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23. I find AI Chatbots adaptable across different academic subjects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.690\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28. Navigating between academic topics in AI Chatbots is straightforward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.636\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27. I can efficiently access AI Chatbots throughout my academic day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.624\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24. The AI system's responses are easily comprehensible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.623\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.53\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22. Formulating academic questions in AI Chatbots is straightforward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.616\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39. I can utilize AI Chatbots for academic purposes without complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.599\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33. I can effectively use AI Chatbots in various academic settings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.575\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.56\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42. I can effectively modify my queries to obtain more precise academic information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.557\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. The AI system aids in summarizing and reviewing lecture content comprehensively\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.695\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. AI Chatbots help me optimize my study schedule effectively\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.656\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.53\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. The AI Chatbots system improves the quality of my academic writing and reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.611\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.58\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. The AI system provides relevant examples that enhance my subject comprehension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.602\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. AI chatbots assist me in completing my university assignments efficiently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.589\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. AI Chatbots facilitate accurate translation of academic terminology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.574\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. The AI system enhances my preparation for examinations and presentations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.556\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. I feel secure sharing my academic queries with the AI system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. I feel secure sharing my academic queries with the AI system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.56\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16. I trust the AI system's recommendations regarding academic references\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.58\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17. I have confidence in the confidentiality of my academic interactions with AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.53\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. I rely on AI Chatbots to verify my academic information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.69\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15. I feel confident using AI Chatbots for university research purposes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.65\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46. The system is easily accessible in libraries and classrooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.46\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47. AI Chatbots are compatible with university laboratory equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.36\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45. I can use AI Chatbots even with limited internet connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.36\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50. Clear guidance for academic use of AI Chatbots is readily available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.40\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41. I can access AI Chatbots from anywhere within the university campus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.52\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30. Previously saved AI Chatbots are easily retrievable for review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.56\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003csup\u003e**\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, factor loadings for the final 27-item scale showed strong psychometric properties. The Ease of Use factor comprised eight items with loadings ranging from .557 to .690, reflecting students' perceptions of the straightforward nature of AI chatbot interactions. The Perceived Usefulness factor included seven items with loadings from .556 to .695, capturing the practical academic benefits of AI chatbot usage. The Trust factor contained six items with loadings between .519 and .691, addressing students' confidence in AI chatbot systems. The Accessibility factor encompassed six items with loadings from .557 to .729, focusing on the technical and physical availability of AI chatbots in academic settings.\u003c/p\u003e \u003cp\u003eThe confirmatory factor analysis provided strong support for the four-factor structure. The model demonstrated good fit to the data across multiple indices: CMIN/DF\u0026thinsp;=\u0026thinsp;1.622, GFI\u0026thinsp;=\u0026thinsp;.905, AGFI\u0026thinsp;=\u0026thinsp;.888, NFI\u0026thinsp;=\u0026thinsp;.858, CFI\u0026thinsp;=\u0026thinsp;.940, and RMSEA\u0026thinsp;=\u0026thinsp;.041 (90% CI [.034, .047]). These values indicate that the four-factor model provides an adequate representation of the scale's underlying structure for the target population. The standardized factor loadings from the CFA ranged from .495 to .744, with all loadings being statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), supporting the convergent validity of the scale. The structural relationships between factors and their respective items are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe reliability analysis revealed strong internal consistency for both the overall scale and its individual factors, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The overall scale demonstrated excellent reliability with McDonald's ω\u0026thinsp;=\u0026thinsp;.911 (95% CI [.898, .924]), Cronbach's α\u0026thinsp;=\u0026thinsp;.911 (95% CI [.897, .923]), and Guttman's λ2\u0026thinsp;=\u0026thinsp;.913. The average inter-item correlation of .279 indicated good internal consistency without redundancy among items.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eReliability Coefficients and Descriptive Statistics for the AI Chatbots Usage Scale\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMcDonald's ω\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCronbach's α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuttman's λ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGreatest Lower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEase of Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Usefulness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrust\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccessibility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.47\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\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, individual factors showed satisfactory reliability coefficients: Ease of Use (ω\u0026thinsp;=\u0026thinsp;.856, α\u0026thinsp;=\u0026thinsp;.855, λ2\u0026thinsp;=\u0026thinsp;.856), Perceived Usefulness (ω\u0026thinsp;=\u0026thinsp;.828, α\u0026thinsp;=\u0026thinsp;.827, λ2\u0026thinsp;=\u0026thinsp;.829), Trust (ω\u0026thinsp;=\u0026thinsp;.807, α\u0026thinsp;=\u0026thinsp;.806, λ2\u0026thinsp;=\u0026thinsp;.807), and Accessibility (ω\u0026thinsp;=\u0026thinsp;.754, α\u0026thinsp;=\u0026thinsp;.752, λ2\u0026thinsp;=\u0026thinsp;.754).\u003c/p\u003e \u003cp\u003eThe construct validity of the scale was further supported by the Average Variance Extracted (AVE) value of .625 and Composite Reliability (CR) of .866, both exceeding recommended thresholds. The Maximum Reliability coefficient (MaxR(H)) of .903 provided additional evidence of the scale's strong reliability. The Greatest Lower Bound (GLB) values for both the overall scale (.956) and individual factors (ranging from .788 to .885) suggested that the true reliability of the scale is likely to be high, even under conservative estimates.\u003c/p\u003e \u003cp\u003eItem-total correlations ranged from .36 to .69 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that all items contributed meaningfully to the overall scale score. The factor correlations ranged from moderate to strong (.558 to .899), demonstrating that the factors were related but sufficiently distinct to represent separate components of AI chatbot usage in academic settings. The highest correlation was observed between Trust and Perceived Usefulness (.899), suggesting a strong relationship between students' trust in AI chatbots and their perception of its academic utility.\u003c/p\u003e \u003cp\u003eMean scores across the factors revealed interesting patterns of AI chatbot usage among the sample. Ease of Use showed the highest mean score (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29.07, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.02), followed by Perceived Usefulness (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.07, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.16), Trust (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.50, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.59), and Accessibility (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18.55, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.72). These results suggest that while students generally find AI chatbots easy to use, there may be room for improvement in areas related to accessibility and trust.\u003c/p\u003e \u003cp\u003eThese results collectively suggest that the AI Chatbots Usage Scale demonstrates strong psychometric properties when applied to male university students in Egyptian higher education, with evidence supporting both its factorial validity and reliability. The four-factor structure provides a comprehensive framework for understanding AI chatbot usage patterns in academic settings, while the high reliability coefficients indicate that the scale provides consistent measurements of the construct.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study's development and validation of the AI Chatbots Usage Scale provides significant insights into how university students engage with AI chatbots in educational settings. The findings reveal a robust four-factor structure that both aligns with and extends existing theoretical frameworks, while offering practical implications for educational technology implementation.\u003c/p\u003e \u003cp\u003eThe four-factor structure of the AI Chatbots Usage Scale demonstrates strong theoretical alignment with established technology adoption frameworks, particularly the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM). The Ease of Use factor corresponds directly with UTAUT's effort expectancy construct, as evidenced by Hidayat-ur-Rehman and Ibrahim (2023), who found that educators' effort expectancy significantly influences their intention to adopt chatbots in educational settings. Similarly, Al-Sharafi et al. (2022) demonstrated that perceived usefulness, which aligns with UTAUT's performance expectancy, is among the most significant factors affecting the sustainable use of AI-based chatbots in education.\u003c/p\u003e \u003cp\u003eThe Trust factor emerges as a critical extension to traditional technology adoption models, particularly in the context of AI chatbots in education. Chen et al. (2024) identified trust as a significant influence on AI chatbot adoption among research scholars, while Rahim et al. (2022) emphasized the role of perceived trust in determining the effectiveness of chatbot adoption in higher education institutions. This alignment suggests that trust serves as a fundamental psychological prerequisite for technology acceptance, extending beyond the basic constructs of conventional adoption frameworks.\u003c/p\u003e \u003cp\u003eThe scale's strong reliability coefficients (McDonald's ω\u0026thinsp;=\u0026thinsp;.911, Cronbach's α\u0026thinsp;=\u0026thinsp;.911) align with and even exceed the reliability standards found in recent chatbot assessment tools, such as those developed by Nemt-allah et al. (2024) and Borsci et al. (2021). These values indicate exceptional internal consistency, making it a reliable tool for both research and practical applications in educational settings. The high Greatest Lower Bound value (.956) further supports the robustness of the scale, suggesting that its true reliability may be even higher than indicated by traditional reliability measures.\u003c/p\u003e \u003cp\u003eThe moderate to strong factor correlations (.558 to .899) demonstrate that while the four dimensions are related, they remain sufficiently distinct to capture different aspects of AI chatbot usage. This finding aligns with recent work by Tian et al. (2024), who identified similar patterns in their study of trust, perceived confirmation, and performance expectancy in chatbot adoption.\u003c/p\u003e \u003cp\u003eThe high mean score for Ease of Use (M\u0026thinsp;=\u0026thinsp;29.07) can be attributed to its fundamental role in shaping students' initial acceptance and continued usage of AI chatbots. As demonstrated by Liu et al. (2024) and Goli et al. (2023), perceived ease of use directly influences users' attitudes and behavioral intentions toward AI chatbot adoption. This finding is particularly relevant for university students who, as digital natives, have high expectations for seamless technological interactions. The prominence of ease of use as a leading factor aligns with Pillai and Sivathanu's (2020) research, which identifies it as a crucial predictor of adoption intention.\u003c/p\u003e \u003cp\u003eThe strong correlation between Trust and Perceived Usefulness (.899) reflects their reciprocal relationship in shaping user acceptance and behavior. Research by Prakash et al. (2023) emphasizes how conversational cues and perceived functional attributes contribute to building trust, ultimately determining behavioral intentions to use AI chatbots. This finding is further supported by Yen and Chiang (2020), who found that characteristics such as credibility, competence, and anthropomorphism positively influence trust, which in turn amplifies perceived usefulness.\u003c/p\u003e \u003cp\u003eThe relatively lower scores for Accessibility (M\u0026thinsp;=\u0026thinsp;18.55) may be attributed to several underlying factors related to user interface design and technical barriers in AI chatbot implementation. This finding aligns with Aditi et al. (2024), who emphasized that chatbot accessibility is heavily dependent on the quality of user interface design and adherence to inclusive digital environment standards. Additionally, Nadarzynski et al. (2019) found that while users may be generally receptive to chatbot technology, technical hesitancy and interface-related barriers can significantly compromise engagement levels.\u003c/p\u003e \u003cp\u003eThe predominantly male sample from Egyptian higher education provides important insights while raising considerations about generalizability. The cultural and educational context of Egyptian universities, particularly regarding technology adoption and implementation, may influence how students perceive and interact with AI chatbots. The diverse academic specializations represented in the sample (ranging from Special Education to French) suggest broad applicability across disciplines, though discipline-specific patterns of usage and acceptance may warrant further investigation.\u003c/p\u003e \u003cp\u003eBased on the factor scores, several recommendations emerge for improving AI chatbot implementation in educational settings. First, institutions should prioritize maintaining and enhancing the ease of use of AI chatbot interfaces, as this factor significantly influences adoption. Second, efforts should be made to address accessibility barriers through improved infrastructure and more seamless integration with existing educational platforms. Third, building trust through transparency about AI capabilities and limitations could enhance both trust and perceived usefulness simultaneously.\u003c/p\u003e \u003cp\u003eEducational technology developers can use these findings to guide feature prioritization and interface design. The strong relationship between trust and perceived usefulness suggests that developers should focus on features that demonstrate reliability and accuracy, while maintaining transparent communication about the system's capabilities and limitations.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting these results. The predominantly male sample from a single cultural context limits generalizability across gender and cultural groups. Future validation studies should include more diverse samples across different cultural and educational contexts. Longitudinal studies could help establish the scale's stability over time and its predictive validity for actual AI chatbot usage patterns.\u003c/p\u003e \u003cp\u003eThese findings have significant implications for institutional policies regarding AI chatbot integration in higher education. The scale can serve as a valuable tool for evaluating technology initiatives and guiding resource allocation decisions. Institutions should consider using this scale as part of their assessment strategy when implementing AI chatbot solutions, particularly focusing on accessibility improvements and trust-building measures. The scale's robust psychometric properties make it a valuable tool for measuring the effectiveness of AI chatbot implementations and informing evidence-based decision-making in educational technology investments. However, institutions should consider their specific contextual factors when applying these findings to policy decisions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe AI Chatbots Usage Scale represents a significant contribution to the field of educational technology assessment, providing a reliable and valid tool for measuring student engagement with AI chatbots. The four-factor structure offers a comprehensive framework for understanding and evaluating AI chatbot implementation in educational settings, while the specific factor findings provide practical guidance for improvement and optimization. Future research should focus on validating these findings across diverse populations and contexts, while educational institutions can use this scale to inform their technology integration strategies and policies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial Intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAGFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Goodness of Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Moment Structures\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAVE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAverage Variance Extracted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMIN/DF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChi-square/Degrees of Freedom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComposite Reliability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpectation Confirmation Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExploratory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGoodness of Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGreatest Lower Bound\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKMO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaiser-Meyer-Olkin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMaxR(H)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum Reliability coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormed Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTechnology Acceptance Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTAUT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnified Theory of Acceptance and Use of Technology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Research Ethics Committee of the Faculty of Education at Al-Azhar University, Dakahlia, Egypt (Ref. No. EDU-REC-2024-0310). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all student participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAI designed the research methodology and performed the statistical analyses, including factor analyses, reliability testing, and data visualization. MN conducted the literature review and developed the theoretical framework. AI implemented the software analysis using SPSS and AMOS, while MN prepared the initial scale items in English. AI validated the statistical procedures and interpreted the results, while MN refined the language and content of the scale items. Both authors contributed to data collection and interpretation. MN wrote the original manuscript draft, and AI provided critical revisions for statistical content. Both authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors express gratitude to the Faculty of Education at Tafhna Al-Ashraf, Dakahlia, undergraduate students, an expert committee, colleagues, Al-Azhar University, and anonymous reviewers for their support, participation, and contributions to the translation and adaptation of the Scale, as well as the support of their department.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdiguzel, T., Kaya, M., \u0026amp; Cansu, F. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. \u003cem\u003eContemporary Educational Technology\u003c/em\u003e, 15(3), ep429. https://doi.org/10.30935/cedtech/13152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAditi, A., Saxena, H., Prof, G., \u0026amp; Malik, A. (2024). 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Chatbot technology as an artificial intelligence tool in foreign language education. \u003cem\u003e2022 International Conference on Quality Management, Transport and Information Security, Information Technologies\u003c/em\u003e, 97\u0026ndash;100. https://doi.org/10.1109/itqmtis58985.2023.10346566\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students\u0026rsquo; acceptance and use of technology. \u003cem\u003eInteractive Learning Environments\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(9), 5142\u0026ndash;5155. https://doi.org/10.1080/10494820.2023.2209881.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao, W., Yang, J., \u0026amp; Qu, X. (2024). 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Chatbot Development as a Digital Learning Tool to Increase Students\u0026rsquo; Research Knowledge. \u003cem\u003eInternational Education Studies\u003c/em\u003e.\u003cem\u003e14\u003c/em\u003e(2), 44\u0026ndash;53. https://doi.org/10.5539/IES.V14N2P44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026aacute;zquez-Parra, J., Henao-Rodr\u0026iacute;guez, C., Lis-Guti\u0026eacute;rrez, J., \u0026amp; Palomino-G\u0026aacute;mez, S. (2024). Importance of University Students\u0026rsquo; Perception of Adoption and Training in Artificial Intelligence Tools. \u003cem\u003eSocieties\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(8), 141. https://doi.org/10.3390/soc14080141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWadhawan, I., Jain, T., \u0026amp; Galhotra, B. (2023). 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Trust me, if you can: a study on the factors that influence consumers\u0026rsquo; purchase intention triggered by chatbots based on brain image evidence and self-reported assessments. \u003cem\u003eBehaviour \u0026amp; Information Technology\u003c/em\u003e, 40(11), 1177\u0026ndash;1194. https://doi.org/10.1080/0144929X.2020.1743362.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI chatbots, educational technology, scale validation, higher education, psychometric analysis","lastPublishedDoi":"10.21203/rs.3.rs-5953281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5953281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study developed and validated the AI Chatbots Usage Scale for assessing university students' engagement with artificial intelligence chatbots in higher education. The research employed a quantitative methodology with 374 male undergraduate students from Al-Azhar university. Through rigorous psychometric analysis, including exploratory and confirmatory factor analyses, a four-factor structure emerged: Ease of Use, Perceived Usefulness, Trust, and Accessibility. The scale demonstrated excellent reliability (McDonald's ω\u0026thinsp;=\u0026thinsp;.911, Cronbach's α\u0026thinsp;=\u0026thinsp;.911) and strong construct validity, supported by good model fit indices (\u003cem\u003eCMIN/DF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.622, \u003cem\u003eCFI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.940, \u003cem\u003eRMSEA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.041). Factor analysis revealed that the four dimensions collectively explained 47.360% of the total variance, with factor loadings ranging from .519 to .729. The final 27-item scale showed robust internal consistency across all factors, with the highest mean scores observed in Ease of Use (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29.07, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.02) and the strongest correlation between Trust and Perceived Usefulness (.899). These findings provide educators and researchers with a validated instrument for measuring AI chatbot usage in academic settings, while offering insights for improving implementation strategies in higher education. The scale's psychometric properties support its utility for evaluating and enhancing AI chatbot integration in educational contexts.\u003c/p\u003e","manuscriptTitle":"Cyberpsychology: Validity of the AI Chatbots Usage Scale for University Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 11:34:40","doi":"10.21203/rs.3.rs-5953281/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":"6dee4e1c-688f-4281-97e6-1d393f0866ad","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T06:23:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-18 11:34:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5953281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5953281","identity":"rs-5953281","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}