Abstract

Conclusion. The study provides evidence-based insights for educators, curriculum designers, and policymakers seeking to leverage GenAI as part of equitable, student-centered, and cognitively enriching AI-enabled learning environments.

Keywords: Generative Artificial Intelligence (GenAI), Sustainable higher education, Student engagement, Cognitive development, Bloom’s Taxonomy

Abdalla, S. Z. S. (2025). Understanding ChatGPT adoption for data analytics learning: A UTAUT perspective among social science students in Oman. Social Sciences & Humanities Open, 11, 101310. https://doi.org/10.1016/j.ssaho.2025.101310

Acosta-Enriquez, B. G., Ramos Farroñan, E. V., Villena Zapata, L. I., Mogollon Garcia, F. S., Rabanal-León, H. C., Morales Angaspilco, J. E., & Saldaña Bocanegra, J. C. (2024). Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory. Heliyon, 10(19), e38315. https://doi.org/10.1016/j.heliyon.2024.e38315

Adewale, M. D., Azeta, A., Abayomi-Alli, A., & Sambo-Magaji, A. (2024). Impact of artificial intelligence adoption on students' academic performance in open and distance learning: A systematic literature review. Heliyon, 10(22), e40025. https://doi.org/10.1016/j.heliyon.2024.e40025

Aideed, H., Salem, I. E., Magdy, A., AlAmri, T. K., Alzubaidi, A. S., & Elbaz, A. M. (2025). Beyond reality: Harnessing the metaverse for transformative education through UTAUT-2 and task-technology synergy. The International Journal of Management Education, 23(2), 101169. https://doi.org/10.1016/j.ijme.2025.101169

Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2024). The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199, 123076. https://doi.org/10.1016/j.techfore.2023.123076

Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon, 10(11), e31887. https://doi.org/10.1016/j.heliyon.2024.e31887

Al Salti, A. (2025). Technology-driven innovation in social work education: A UTAUT perspective. Journal of Social Work, 85(3), 140–172. Egyptian Association of Social Workers. https://doi.org/10.21608/egjsw.2025.401915.1504

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411. https://doi.org/10.1037/0033-2909.103.3.411

Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Longman.

Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168. https://doi.org/10.1016/j.im.2019.05.003

Bozkurt, A., Xiao, F., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C., Romero-Hall, E., & Jandrić, P. (2023). Speculative futures on ChatGPT and generative Artificial Intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53–130. https://doi.org/10.5281/zenodo.7636568

Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399–426. https://doi.org/10.2307/25148690

Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic Concepts, Applications, and Programming (2nd ed.). Routledge.

Cabrera-Sánchez, J.-P., Villarejo-Ramos, Á. F., Liébana-Cabanillas, F., & Shaikh, A. A. (2021). Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables. Telematics and Informatics, 58, 101529. https://doi.org/10.1016/j.tele.2020.101529

Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616–630. https://doi.org/10.1007/s11528-022-00715-y

Changalima, I. A., Amani, D., & Ismail, I. J. (2024). Social influence and information quality on generative AI use among business students. The International Journal of Management Education, 22(3), 101063. https://doi.org/10.1016/j.ijme.2024.101063

Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023). Time to Revisit Existing Student’s Performance Evaluation Approach in Higher Education Sector in a New Era of ChatGPT — A Case Study. Cogent Education, 10(1). https://doi.org/10.1080/2331186X.2023.2210461

Chávez Herting, D., Cladellas Pros, R., & Castelló Tarrida, A. (2020). Habit and social influence as determinants of PowerPoint use in higher education: A study from a technology acceptance approach. Interactive Learning Environments, 31(1), 497–513. https://doi.org/10.1080/10494820.2020.1799021

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer. https://doi.org/10.1007/978-1-4899-2271-7

Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024). ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial Intelligence, 6, 100198. https://doi.org/10.1016/j.caeai.2023.100198

Faqih, K. M. S., & Jaradat, M.-I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67, 101787. https://doi.org/10.1016/j.techsoc.2021.101787

Faraon, M., Rönkkö, K., Milrad, M., Hemberg, J., & Fominykh, M. (2025). International perspectives on artificial intelligence in higher education: An explorative study of students’ intention to use ChatGPT across the Nordic countries and the USA. Education and Information Technologies. 30, 17835–17880. https://doi.org/10.1007/s10639-025-13492-x

Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F. F., Al-shami, S. A., & Slamene, R. (2023). Analyzing the students' views, concerns, and perceived ethics about ChatGPT usage. Computers and Education: Artificial Intelligence, 5, 100180. https://doi.org/10.1016/j.caeai.2023.100180

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

G. C., S. B., Bhandari, P., Gurung, S. K., Srivastava, E., & Ojha, D. (2024). Examining the role of social influence, learning value and habit on students’ intention to use ChatGPT: The moderating effect of information accuracy in the UTAUT2 model. Cogent Education, 11(1), 2403287. https://doi.org/10.1080/2331186X.2024.2403287

García-López, I. M., González González, C. S., Ramírez-Montoya, M.-S., & Molina-Espinosa, J.-M. (2025). Challenges of implementing ChatGPT on education: Systematic literature review. International Journal of Educational Research Open, 8, 100401. https://doi.org/10.1016/j.ijedro.2024.100401

Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023). Adaptive learning using artificial intelligence in e-learning: A literature review. Education Sciences, 13(12). https://doi.org/10.3390/educsci13121216

Gonsalves, C. (2024). Generative AI’s Impact on Critical Thinking: Revisiting Bloom’s Taxonomy. Journal of Marketing Education. 1-16. https://doi.org/10.1177/02734753241305980

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864

Grassini, S., Aasen, M. L., & Møgelvang, A. (2024). Understanding University Students’ Acceptance of ChatGPT: Insights from the UTAUT2 Model. Applied Artificial Intelligence, 38(1). https://doi.org/10.1080/08839514.2024.2371168

Habiballa, H., Kotyrba, M., Volna, E., Bradac, V., & Dusek, M. (2025). Artificial Intelligence (ChatGPT) and Bloom’s Taxonomy in Theoretical Computer Science Education. Applied Sciences, 15(2), 581. https://doi.org/10.3390/app15020581

Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, 100190. https://doi.org/10.1016/j.caeai.2023.100190

Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

Hair, J. F., Jr., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069

Hameed, A. G. A.-R., & Sumari, P. B. (2024). Adoption and continued usage of mobile learning of virtual platforms in Iraqi higher education: An unstable environment. International Journal of Information Management Data Insights, 4(2), 100242. https://doi.org/10.1016/j.jjimei.2024.100242

Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal, 25(1), 76–81. https://doi.org/10.1016/j.ausmj.2017.02.001

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Herrmann-Werner, A., Festl-Wietek, T., Holderried, F., Herschbach, L., Griewatz, J., Masters, K., Zipfel, S., & Mahling, M. (2024). Assessing ChatGPT’s mastery of bloom’s taxonomy using psychosomatic medicine exam questions: mixed-methods study. Journal of Medical Internet Research, 26(13), e52113. https://doi.org/10.2196/52113

Herzallah, A. M., & Makaldy, R. (2025). Technological self-efficacy and sense of coherence: Key drivers in teachers' AI acceptance and adoption. Computers and Education: Artificial Intelligence, 8, 100377. https://doi.org/10.1016/j.caeai.2025.100377

Hoi, V. N. (2020). Understanding higher education learners' acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761. https://doi.org/10.1016/j.compedu.2019.103761

Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.

Hsu, W.-L., & Silalahi, A. D. K. (2024). Exploring the paradoxical use of ChatGPT in education: Analyzing benefits, risks, and coping strategies through integrated UTAUT and PMT theories using a hybrid approach of SEM and fsQCA. Computers and Education: Artificial Intelligence, 7, 100329. https://doi.org/10.1016/j.caeai.2024.100329

Huang, C.-Y., & Kao, Y.-S. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, Article ID: 603747. https://doi.org/10.1155/2015/603747

Huang, T. (2023). Expanding the UTAUT2 framework to determine the drivers of mobile shopping behaviour among older adults. PLoS ONE, 18(12), e0295581. https://doi.org/10.1155/2015/603747

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1371/journal.pone.0295581

Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour. Technology in Society, 77, 102521. https://doi.org/10.1016/j.techsoc.2024.102521

Kabra, G., Ramesh, A., Akhtar, P., & Dash, M. K. (2017). Understanding behavioural intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250–1261. https://doi.org/10.1016/j.tele.2017.05.010

Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8, 100348. https://doi.org/10.1016/j.caeai.2024.100348

Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. https://doi.org/10.1016/j.caeai.2021.100017

Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925

Khan, I. U. (2022). How does culture influence digital banking? A comparative study based on the unified model. Technology in Society, 68, 101822. https://doi.org/10.1016/j.techsoc.2021.101822

Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research note—Two competing perspectives on automatic use: A theoretical and empirical comparison. Information Systems Research, 16(4), 418–432. https://doi.org/10.1287/isre.1050.0070

Kuhlmann, S. L., Plumley, R., Evans, Z., Bernacki, M. L., Greene, J. A., Hogan, K. A., Berro, M., Gates, K., & Panter, A. (2024). Students’ active cognitive engagement with instructional videos predicts STEM learning. Computers & Education, 216, 105050. https://doi.org/10.1016/j.compedu.2024.105050

Larsen, T. M., Endo, B. H., Yee, A. T., Do, T., & Lo, S. M. (2022). Probing internal assumptions of the revised Bloom's taxonomy. CBE—Life Sciences Education, 21(1), 1–12. https://doi.org/10.1187/cbe.20-08-0170

Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., & Karacapilidis, N. (2024). Determinants of Humanities and Social Sciences Students’ Intentions to Use Artificial Intelligence Applications for Academic Purposes. Information, 15(6), 314. https://doi.org/10.3390/info15060314

Lei, M., & Lomax, R. G. (2005). The Effect of Varying Degrees of Nonnormality in Structural Equation Modeling . Structural Equation Modeling: A Multidisciplinary Journal, 12(1), 1–27. https://doi.org/10.1207/s15328007sem1201_1

Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31, 705–737. https://doi.org/10.2307/25148817

Li, Q., & Qin, Y. (2023). AI in medical education: Medical student perception, curriculum recommendations and design suggestions. BMC Medical Education, 23(1), Article ID: 852. https://doi.org/10.1186/s12909-023-04700-8

Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410

Lubbe, A., Marais, E., & Kruger, D. (2025). Cultivating independent thinkers: The triad of artificial intelligence, Bloom’s taxonomy and critical thinking in assessment pedagogy. Education and Information Technologies, 30, 17589–17622. https://doi.org/10.1007/s10639-025-13476-x.

Mandai, K., Tan, M. J. H., Padhi, S., & Pang, K. T. (2024). A cross-era discourse on ChatGPT’s influence in higher education through the lens of John Dewey and Benjamin Bloom. Education Sciences, 14(6), 614. https://doi.org/10.3390/educsci14060614

Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s findings. Structural Equation Modeling, 11(3), 320–341. https://doi.org/10.1207/s15328007sem1103_2

Memarian, B., & Doleck, T. (2023). ChatGPT in education: Methods, potentials, and limitations. Computers in Human Behavior: Artificial Humans, 1(2), 100022. https://doi.org/10.1016/j.chbah.2023.100022

Merhi, M., Hone, K., & Tarhini, A. (2019). A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust. Technology in Society, 59, 101151. https://doi.org/10.1016/j.techsoc.2019.101151

Mienye, I. D., & Swart, T. G. (2025). ChatGPT in education: A review of ethical challenges and approaches to enhancing transparency and privacy. Procedia Computer Science, 254, 181–190. https://doi.org/10.1016/j.procs.2025.02.077

Montello, M. B., Bezerra de Moura, S. A., & Abreu, B. J. (2025). Bloom's taxonomy as a tool for educational objectives applied to human anatomy. Morphologie, 109(364), 100943. https://doi.org/10.1016/j.morpho.2024.100943

Nazaretsky, T., Mejia-Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2025). The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions. Computers and Education: Artificial Intelligence, 8, 100368. https://doi.org/10.1016/j.caeai.2025.100368

Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2, 100041. https://doi.org/10.1016/j.caeo.2021.100041

O’Connor, S. (2022). Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice, 66, 103537–103537. https://doi.org/10.1016/j.nepr.2022.103537

Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020

Pradana, M., Elisa, H. P., & Syarifuddin, S. (2023). Discussing ChatGPT in education: A literature review and bibliometric analysis. Cogent Education, 10(2). https://doi.org/10.1080/2331186X.2023.2243134

Popenici, S., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(22). https://doi.org/10.1186/s41039-017-0062-8

Rahiman, H. U., & Kodikal, R. (2023). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2023.2293431

Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R. (2024). Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework. Heliyon, 10(18), e37569. https://doi.org/10.1016/j.heliyon.2024.e37569

Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003

Retscher, G. (2025). Exploring the intersection of artificial intelligence and higher education: Opportunities and challenges in the context of geomatics education. Applied Geomatics, 17, 49–61. https://doi.org/10.1007/s12518-024-00602-6

Romero-Rodríguez, J. M., Ramírez-Montoya, M. S., 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(2), 323–339. https://doi.org/10.7821/naer.2023.7.1458

Rudhumbu, N. (2022). Applying the UTAUT2 to predict the acceptance of blended learning by university students. Asian Association of Open Universities Journal, 17(1), 15–36. https://doi.org/10.1108/AAOUJ-08-2021-0084

Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. The Guilford Press.

Saha, P., Hossain, M. S., Roy, N. C., Al Masud, A., & Amin, R. (2025). Unlocking the power of AI in education: Students’ intentions and AI tool use driving learning success in an emerging economy. On the Horizon, 33(1), 126–144. https://doi.org/10.1108/OTH-10-2024-0066

Schmitz, A., Dı´az-Martı´n, A. M., & Yagüe Guillén,, M. J. (2022). Modifying UTAUT2 for a cross-country comparison of telemedicine adoption. Computers in Human Behavior, 130, 107183.

Seo, K. W., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1), Article 13. https://doi.org/10.1186/s41239-021-00292-9

Shaqrah, A., & Almars, A. (2022). Examining the internet of educational things adoption using an extended unified theory of acceptance and use of technology. Internet of Things, 19, 100558. https://doi.org/10.1016/j.iot.2022.100558

Sitar-Taut, D. A. (2021). Mobile learning acceptance in social distancing during the COVID-19 outbreak: The mediation effect of hedonic motivation. Human Behavior and Emerging Technologies, 3(3), 366-378.

Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127

Stanny, C. (2016). Reevaluating Bloom's taxonomy: What measurable verbs can and cannot say about student learning. Education Sciences, 6(4), 37. https://doi.org/10.3390/educsci6040037

Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2024). AI‐driven adaptive learning for sustainable educational transformation. Sustainable Development, 1-27. https://doi.org/10.1002/sd.3221

Strzelecki, A. (2024). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovations in Higher Education, 49, 223–245. https://doi.org/10.1007/s10755-023-09686-1

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

Suhail, F., Adel, M., Al-Emran, M., & AlQudah, A. A. (2024). Are students ready for robots in higher education? Examining the adoption of robots by integrating UTAUT2 and TTF using a hybrid SEM-ANN approach. Technology in Society, 77, 102524. https://doi.org/10.1016/j.techsoc.2024.102524

Supianto, R. W., Wulandari, F., Zainudin, M., Athiyallah, A., & Rizqa, M. (2024). Exploring the factors affecting ChatGPT acceptance among university students. Multidisciplinary Science Journal, 6(12). https://doi.org/10.31893/multiscience.2024273

Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, 102269. https://doi.org/10.1016/j.ijinfomgt.2020.102269

Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the Moderating Effect of Gender and Age on E-Learning Acceptance in England: A Structural Equation Modeling Approach for An Extended Technology Acceptance Model. Journal of Educational Computing Research, 51(2), 163-184. https://doi.org/10.2190/EC.51.2.b

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x

Tseng, T. H., Lin, S., Wang, Y.-S., & Liu, H.-X. (2022). Investigating teachers’ adoption of MOOCs: The perspective of UTAUT2. Interactive Learning Environments, 30(4), 635–650. https://doi.org/10.1080/10494820.2019.1674888

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252(Part A), 124167. https://doi.org/10.1016/j.eswa.2024.124167

West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56–75). Sage.

Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring factors of the willingness to accept AI-assisted learning environments: An empirical investigation based on the UTAUT model and perceived risk theory. Frontiers in Psychology, 13, 870777. https://doi.org/10.3389/fpsyg.2022.870777

Xia, Q., Chiu, T. K. F., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022). A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education. Computers & Education, 189, 104582. https://doi.org/10.1016/j.compedu.2022.104582

Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory of Acceptance and Use of Technology (UTAUT) in Higher Education: A Systematic Review. SAGE Open, 14(1). https://doi.org/10.1177/21582440241229570

Xu, X., Su, Y., Zhang, Y., Wu, Y., & Xu, X. (2024). Understanding learners’ perceptions of ChatGPT: A thematic analysis of peer interviews among undergraduates and postgraduates in China. Heliyon, 10(4), e26239. https://doi.org/10.1016/j.heliyon.2024.e26239

Yusuf, A., Bello, S., Pervin, N., & Tukur, A. K. (2024). Implementing a proposed framework for enhancing critical thinking skills in synthesizing AI-generated texts. Thinking Skills and Creativity, 53, 101619. https://doi.org/10.1016/j.tsc.2024.101619

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0