McTear, M. Conversational AI. https://doi.org/10.1007/978-3-031-02176-3 (Springer, 2021).
Arpaci, I. A. & Multianalytical, S. E. M. A. N. N. Approach to investigate the social sustainability of AI chatbots based on cybersecurity and protection motivation theory. IEEE Trans. Eng. Manag. 71, 1714–1725 (2024).
Google Scholar
Gibreel, O. & Arpaci, I. Development and validation of the prompt engineering competence scale (PECS). Inf. Dev. https://doi.org/10.1177/02666669251336455 (2025).
Google Scholar
Al-Sharafi, M. A. et al. Generation Z use of artificial intelligence products and its impact on environmental sustainability: A cross-cultural comparison. Comput. Hum. Behav. 143, 107708 (2023).
Google Scholar
Hansen, H. F., Lillesund, E., Mikalef, P. & Altwaijry, Ν. Understanding artificial intelligence diffusion through an AI capability maturity model. Inf. Syst. Front. 26, 2147–2163 (2024).
Google Scholar
Homolak, J. Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern promethean dilemma. Croat Med. J. 64, 1–3 (2023).
Google Scholar
Li, B., Bonk, C. J., Wang, C. & Kou, X. Reconceptualizing Self-Directed learning in the era of generative AI: an exploratory analysis of Language learning. IEEE Trans. Learn. Technol. 17, 1515–1529 (2024).
Google Scholar
Kim, J., Yu, S., Detrick, R. & Li, N. Exploring students’ perspectives on generative AI-assisted academic writing. Educ. Inf. Technol. https://doi.org/10.1007/s10639-024-12878-7 (2024).
Google Scholar
Stokel-Walker, C. & Van Noorden, R. What ChatGPT and generative AI mean for science. Nature 614, 214–216 (2023).
Google Scholar
Lund, B. D. & Wang, T. Chatting about chatgpt: how May AI and GPT impact academia and libraries? Libr. Hi Tech. News. 40, 26–29 (2023).
Google Scholar
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S. & Cheng, M. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Comput. Educ. Artif. Intell. 4, 100118 (2023).
Google Scholar
Shen, Y. et al. ChatGPT and other large Language models are Double-edged swords. Radiology 307, e230163 (2023).
Farrokhnia, M., Banihashem, S. K., Noroozi, O. & Wals, A. A SWOT analysis of chatgpt: Implications for educational practice and research. Innov. Educ. Teach. Int. 1–15. https://doi.org/10.1080/14703297.2023.2195846 (2023).
Chen, Y., Jensen, S., Albert, L. J., Gupta, S. & Lee, T. Artificial intelligence (AI) student assistants in the classroom: designing chatbots to support student success. Inf. Syst. Front. 25, 161–182 (2023).
Google Scholar
Labadze, L., Grigolia, M. & Machaidze, L. Role of AI chatbots in education: systematic literature review. Int. J. Educ. Technol. High. Educ. 20, 56 (2023).
Google Scholar
Wang, X., Lin, X. & Shao, B. Artificial intelligence changes the way we work: A close look at innovating with chatbots. J. Assoc. Inf. Sci. Technol. 74, 339–353 (2023).
Google Scholar
Arpaci, I., Baloglu, M., Kozan, H. I. Ö. & Kesici, S. Individual differences in the relationship between attachment and nomophobia among college students: The mediating role of mindfulness. J. Med. Internet Res. 19 (2017).
Arpaci, I., Abdeljawad, T., Baloğlu, M., Kesici, Ş. & Mahariq, I. Mediating effect of internet addiction on the relationship between individualism and cyberbullying: Cross-Sectional questionnaire study. J. Med. Internet Res. 22, 1–14 (2020).
Google Scholar
Arpaci, I. & Kocadag Unver, T. Moderating role of gender in the relationship between big five personality traits and smartphone addiction. Psychiatr Q. 91, 577–585 (2020).
Google Scholar
Özteke Kozan, H. İ., Baloğlu, M., Kesici, Ş. & Arpacı, İ. The role of personality and psychological needs on the problematic internet use and problematic social media use. Addicta Turkish J. Addict. 6, 203–219 (2019).
Google Scholar
Arpaci, I., Karatas, K., Kusci, I. & Al-Emran, M. Understanding the social sustainability of the metaverse by integrating UTAUT2 and big five personality traits: A hybrid SEM-ANN approach. Technol. Soc. 71, 102120 (2022).
Google Scholar
Chan, C. K. & Hu, W. Y. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. Int. J. Educ. Technol. High. Educ. 20 (2023).
An, Y., Ouyang, W. & Zhu, F. ChatGPT in higher education: design teaching model involving ChatGPT. Lect Notes Educ. Psychol. Public. Media. 24, 47–56 (2023).
Google Scholar
Al-Zahrani, A. M. The impact of generative AI tools on researchers and research: Implications for academia in higher education. Innov. Educ. Teach. Int. 1–15. https://doi.org/10.1080/14703297.2023.2271445 (2023).
Jauhiainen, J. S. & Guerra, A. G. Generative AI and ChatGPT in school children’s education: evidence from a school lesson. Sustainability 15, 14025 (2023).
Google Scholar
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I. & Pechenkina, E. Generative AI and the future of education: Ragnarök or reformation? A Paradoxical perspective from management educators. Int. J. Manag Educ. 21, 100790 (2023).
Wang, N., Wang, X. & Su, Y. S. Critical analysis of the technological affordances, challenges and future directions of generative AI in education: a systematic review. Asia Pac. J. Educ. 44, 139–155 (2024).
Google Scholar
Eysenck, H. J. The Structure of Human Personality (Methuen, 1970).
McCrae, R. R. & Costa, P. T. Reinterpreting the Myers-Briggs type indicator from the perspective of the Five‐Factor model of personality. J. Pers. 57, 17–40 (1989).
Google Scholar
Guadagno, R. E., Okdie, B. M. & Eno, C. A. Who blogs? Personality predictors of blogging. Comput. Hum. Behav. 24, 1993–2004 (2008).
Google Scholar
Goren-Bar, D., Graziola, I., Pianesi, F. & Zancanaro, M. The influence of personality factors on visitor attitudes towards adaptivity dimensions for mobile museum guides. User Model. User-adapt Interact. 16, 31–62 (2006).
Google Scholar
McCrae, R. R. & John, O. P. An introduction to the Five-Factor model and its applications. J. Pers. 60, 175–215 (1992).
Google Scholar
Poškus, M. S. & Žukauskienė, R. Predicting adolescents’ recycling behavior among different big five personality types. J. Environ. Psychol. 54, 57–64 (2017).
Google Scholar
Kaya, F. et al. The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. Int. J. Hum.–Comput. Interact. 1–18. https://doi.org/10.1080/10447318.2022.2151730 (2022).
Schepman, A. & Rodway, P. The general attitudes towards artificial intelligence scale (GAAIS): confirmatory validation and associations with personality, corporate distrust, and general trust. Int. J. Human–Computer Interact. 39, 2724–2741 (2023).
Google Scholar
Weinstein, T. A. R. & Capitanio, J. P. A nonhuman primate perspective on affiliation. Behav. Brain Sci. 28 (2005).
Devaraj, U. S., Easley, R. F. & Crant, M. How does personality matter? Relating the five-factor model to technology acceptance and use. Inf. Syst. Res. 19, 93–105 (2008).
Google Scholar
Barnett, T., Pearson, A. W., Pearson, R. & Kellermanns, F. W. Five-factor model personality traits as predictors of perceived and actual usage of technology. Eur. J. Inf. Syst. 24, 374–390 (2015).
Google Scholar
Lee, Y. K., Chang, C. T., Lin, Y. & Cheng, Z. H. The dark side of smartphone usage: psychological traits, compulsive behavior and technostress. Comput. Hum. Behav. 31, 373–383 (2014).
Google Scholar
Amichai-Hamburger, Y. & Vinitzky, G. Social network use and personality. Comput. Hum. Behav. 26, 1289–1295 (2010).
Google Scholar
Eysenck, H. J. & Eysenck, M. W. Personality and Individual Differences (Springer, 1985). https://doi.org/10.1007/978-1-4613-2413-3
Digman, J. M. Personality structure emergence of the five-factor model. Annu. Rev. Psychol. 41, 417–440 (1990).
Major, D. A., Turner, J. E. & Fletcher, T. D. Linking proactive personality and the big five to motivation to learn and development activity. J. Appl. Psychol. 91, 927–935 (2006).
Google Scholar
Payne, S. C., Youngcourt, S. S. & Beaubien, J. M. A meta-analytic examination of the goal orientation Nomological net. J. Appl. Psychol. 92, 128–150 (2007).
Google Scholar
Seibert, D., Godulla, A. & Wolf, C. Understanding how personality affects the acceptance of technology: A literature review. Media Commun. 1–24 (2021).
Barrick, M. R. & Mount, M. K. The big five personality dimensions and job performance: A meta-analysis. Pers. Psychol. 44, 1–26 (1991).
Google Scholar
Svendsen, G. B., Johnsen, J. A. K., Almås-Sørensen, L. & Vittersø, J. Personality and technology acceptance: the influence of personality factors on the core constructs of the technology acceptance model. Behav. Inf. Technol. 32, 323–334 (2013).
Google Scholar
David, D., Montgomery, G. H., Stan, R., DiLorenzo, T. & Erblich, J. Discrimination between hopes and expectancies for nonvolitional outcomes: psychological phenomenon or artifact? Pers. Individ Dif. 36, 1945–1952 (2004).
Google Scholar
Costa, P. T. & McCrae, R. R. Normal personality assessment in clinical practice: the NEO personality inventory. Psychol. Assess. 4, 5–13 (1992).
Google Scholar
Prasetya, W. Y. S., Shihab, M. R. & Sandhyaduhita, P. I. Exploring the roles of personality factors on knowledge management system acceptance. In 3rd International Conference on Information Communication Technology. 107–112 (IEEE, 2015). https://doi.org/10.1109/ICoICT.2015.7231406
Tanaka, J. S. How big is big enough? Sample size and goodness of fit in structural equation models with latent variables. Child. Dev. 58, 134 (1987).
Google Scholar
Hundleby, J. D. & Nunnally, J. Psychometric theory. Am. Educ. Res. J. 5, 431 (1968).
Benet-Martínez, V. & John, O. P. Los Cinco grandes across cultures and ethnic groups: Multitrait multimethod analyses of the big five in Spanish and english. J. Pers. Soc. Psychol. 75, 729–750 (1998).
Google Scholar
Sümer, N., Lajunen, T. & Özkan, T. Big five personality traits as the distal predictors of road accident involvement. Traffic Transp. Psychol. 215–227. https://doi.org/10.1016/b978-008044379-9/50173-4 (2005).
Arpaci, I. A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. Comput. Hum. Behav. 90, 181–187 (2019).
Google Scholar
Al-Sharafi, M. A. et al. Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interact. Learn. Environ. 31, 7491–7510 (2023).
Google Scholar
Podsakoff, P. M. & Organ, D. W. Self-Reports in organizational research: problems and prospects. J. Manage. 12, 531–544 (1986).
Henseler, J., Ringle, C. M. & Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43, 115–135 (2015).
Google Scholar
Mittal, U., Sai, S., Chamola, V. & Sangwan, D. A comprehensive review on generative AI for education. IEEE Access. 12, 142733–142759 (2024).
Google Scholar
Celik, I., Dindar, M., Muukkonen, H. & Järvelä, S. The promises and challenges of artificial intelligence for teachers: a systematic review of research. TechTrends 66, 616–630 (2022).
Google Scholar
Asad, M. M. & Ajaz, A. Impact of ChatGPT and generative AI on lifelong learning and upskilling learners in higher education: unveiling the challenges and opportunities globally. Int. J. Inf. Learn. Technol. https://doi.org/10.1108/IJILT-06-2024-0103 (2024).
Google Scholar
Capraro, V. et al. The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS Nexus 3 (2024).
Ali, I. M. & Arpaci, I. The role of moral intention and moral obligation in predicting attitudes toward avoiding aigiarism: A protection motivation theory perspective. Comput. Hum. Behav. Rep. 18, 100681 (2025).
Google Scholar
Muck, P. M. Review of the German version of the ‘revised NEO personality inventory by Costa and McCrae (NEO-PI-R)’ by F. Ostendorf and A. Angleitner. Z. Fur Arbeits- Und Organ. 48, 203–210 (2004).
De Raad, B. Personality traits in learning and education. Eur. J. Pers. 10, 185–200 (1996).
Google Scholar
Gobert, J. D., Pedro, S., Betts, C. G. & M. A. & An AI-Based teacher dashboard to support students’ inquiry: design principles, features, and technological specifications. Handb. Res. Sci. Educ. Vol. III 3, 1011–1044 (2023).
Google Scholar
Böckle, M., Yeboah-Antwi, K. & Kouris, I. Lecture Notes on Computer Science, LNAI. Vol. 12797. 3–20. (Springer, 2021).
Park, J. & Woo, S. E. Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence?. J. Psychol. 156, 68–94 (2022).
Google Scholar
Jang, J. & Kim, J. Exploring the Impact of Avatar Customization in Metaverse: The Role of the Class Mode on Task Engagement and Expectancy-Value Beliefs for Fashion Education. Mob. Inf. Syst. 1–13 (2023).
Zhou, T. & Lu, Y. The effects of personality traits on user acceptance of mobile commerce. Int. J. Hum. Comput. Interact. 27, 545–561 (2011).
Google Scholar
Graziano, W. G., Jensen-Campbell, L. A. & Hair, E. C. Perceiving interpersonal conflict and reacting to it: the case for agreeableness. J. Pers. Soc. Psychol. 70, 820–835 (1996).
Google Scholar
Mustafa, S., Zhang, W. & Why do I share? Participants’ personality traits and online participation. Int. J. Hum.–Comput. Interact. 40, 3763–3781 (2024).
Graziano, W. G. & Tobin, R. M. Handbook of Individual Differences in Social Behavior. 46–61 (The Guilford Press,2009).
Mou, Y. & Xu, K. The media inequality: comparing the initial human-human and human-AI social interactions. Comput. Hum. Behav. 72, 432–440 (2017).
Google Scholar
Munnukka, J., Talvitie-Lamberg, K. & Maity, D. Anthropomorphism and social presence in Human–Virtual service assistant interactions: the role of dialog length and attitudes. Comput. Hum. Behav. 135, 107343 (2022).
Google Scholar
Kurian, N. AI’s empathy gap: the risks of conversational artificial intelligence for young children’s well-being and key ethical considerations for early childhood education and care. Contemp. Issues Early Child. 26, 132–139 (2025).
Google Scholar
Okdie, B. M., Guadagno, R. E., Bernieri, F. J., Geers, A. L. & Mclarney-Vesotski, A. R. Getting to know you: Face-to-face versus online interactions. Comput. Hum. Behav. 27, 153–159 (2011).
Dong, W., Pan, D. & Kim, S. Exploring the integration of IoT and generative AI in english Language education: smart tools for personalized learning experiences. J. Comput. Sci. 82, 102397 (2024).
Google Scholar
Clarizia, F., Colace, F., Lombardi, M., Pascale, F. & Santaniello, D. Chatbot: An education support system for student. In Lecture Notes on Computer Science (including Subseries Lecture Notes on Artificial Intelligence Lecture Notes on Bioinformatics). LNCS . Vol. 11161. 291–302 (Springer, 2018).
Arpaci, İ. An investigation of the relationship between university students’ innovativeness profile and their academic success in the project development course. J. Entrep Innov. Manag. 7, 79–95 (2018).
Boun, K. & Makmee, P. Peera wongupparaj. Development of future skills assessment criteria for undergraduate students in cambodia: mixed methods research. J. Behav. Sci. 16, 85–100 (2021).
Makmee, P. Future skills of tertiary students required for industry in the Eastern special development zone of Thailand. J. Behav. Sci. 16, 85–100 (2021).
Chen, Y., Esmaeilzadeh, P. & Generative, A. I. Medical practice: In-Depth exploration of privacy and security challenges. J. Med. Internet Res. 26, e53008 (2024).
Google Scholar
Uddin, J., Feng, C. & Xu, J. Health communication on the internet: promoting public health and exploring disparities in the generative AI era. J. Med. Internet Res. 27, e66032 (2025).
Google Scholar
