Revolutionizing education: Artificial intelligence empowered learning in higher education

References

• Abd Aziz, N. N., Aziz, M. A., & Abd Rahman, N. A. S. (2023). The mediating effects of student satisfaction on technostress–performance expectancy relationship in university students. Journal of Applied Research in Higher Education, 15(1), 113–24. https://doi.org/10.1108/JARHE-03-2021-0117
   Web of Science ®Google Scholar
• Abumandour, E.-S. T. (2022). Applying e-learning system for engineering education – challenges and obstacles. Journal of Research in Innovative Teaching & Learning, 15(2), 150–169. https://doi.org/10.1108/JRIT-06-2021-0048
   Google Scholar
• AlAjmi, M. K. (2022). The impact of digital leadership on teachers’ technology integration during the COVID-19 pandemic in Kuwait. International Journal of Educational Research, 112, 101928. https://doi.org/10.1016/j.ijer.2022.101928
   PubMed Web of Science ®Google Scholar
• Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, 100132. https://doi.org/10.1016/j.caeai.2023.100132
   Google Scholar
• Aldosari, S. A. M. (2020). The future of higher education in the light of artificial intelligence transformations. International Journal of Higher Education, 9(3), 145–151. https://doi.org/10.5430/ijhe.v9n3p145
   Google Scholar
• AL-Ghamdi, A. S., Ragab, M., & Sabir, M. F. S. (2022). Enhanced artificial intelligence-based cybersecurity intrusion detection for higher education institutions. Computers Materials & Continua, 72(2), 2895–2907. https://doi.org/10.32604/cmc.2022.026405
   Web of Science ®Google Scholar
• Al-Makhmari, L., Al-Bulushi, A., Al-Habsi, S., Al-Azri, O., & Tarhini, A.(2022). Determinants of consumers’ acceptance of Voice Assistance technology: Integrating the service robot acceptance model and unified theory of acceptance and use of technology: Research-in-progress. Proceedings of the International Conference on Information Systems and Intelligent Applications, Kuala Lumpur, Malaysia (pp. 603–612). Springer
   Google Scholar
• AL-Nuaimi, M. N., Al Sawafi, O. S., Malik, S. I., & Al-Maroof, R. S. (2022). Extending the unified theory of acceptance and use of technology to investigate determinants of acceptance and adoption of learning management systems in the post-pandemic era: A structural equation modeling approach. Interactive Learning Environments, 1–27. https://doi.org/10.1080/10494820.2022.2127777
   Web of Science ®Google Scholar
• Al-Takhayneh, S. K., Karaki, W., Hasan, R A., Chang, B-L., Shaikh, J M., Kanwal, W. (2022). Teachers’ psychological resistance to digital innovation in jordanian entrepreneurship and business schools: Moderation of teachers’ psychology and attitude toward educational technologies. Frontiers in Psychology, 13, 1004078. https://doi.org/10.3389/fpsyg.2022.1004078
   PubMed Web of Science ®Google Scholar
• Amornkitpinyo, T., Yoosomboon, S, Sopapradit, S, & Amornkitpinyo, P. (2021). The structural equation model of actual use of cloud learning for higher education students in the 21st century. Journal of E-Learning and Knowledge Society, 17(1), 72–80. https://doi.org/10.20368/1971-8829/1135300
   Web of Science ®Google Scholar
• Antonietti, C., Cattaneo, A., & Amenduni, F. (2022). Can teachers’ digital competence influence technology acceptance in vocational education? Computers in Human Behavior 132, 107266. https://doi.org/10.1016/j.chb.2022.107266
   Web of Science ®Google Scholar
• Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-020-00218-x
   Web of Science ®Google Scholar
• Beerkens, M. (2022). An evolution of performance data in higher education governance: A path towards a “big data” era? Quality in Higher Education, 28(1), 29–49. https://doi.org/10.1080/13538322.2021.1951451
   Web of Science ®Google Scholar
• Bisen, I. E., Arslan, E. A., Yildirim, K., & Yildirim, Y.(2021). Artificial intelligence and machine learning in higher education. In machine learning approaches for improvising modern learning systems (pp. 1–17). IGI Global. https://doi.org/10.4018/978-1-7998-5009-0.ch001
   Google Scholar
• Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2021). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust. Journal of Business Research, 131, 485–494. https://doi.org/10.1016/j.jbusres.2020.08.018
   PubMed Web of Science ®Google Scholar
• Bucea-Manea-Țoniş, R., Kuleto, V., Gudei, S. C. D., Lianu, C., Lianu, C., Ilić, M. P., & Păun, D. (2022). Artificial intelligence potential in higher education institutions enhanced learning environment in Romania and Serbia. Sustainability (Switzerland), 14(10), 5842. https://doi.org/10.3390/su14105842
   Web of Science ®Google Scholar
• Campillo-Ferrer, J. M., & Miralles-Martínez, P. (2021). Effectiveness of the flipped classroom model on students’ self-reported motivation and learning during the COVID-19 pandemic. Humanities and Social Sciences Communications, 8(1), 1–9. https://doi.org/10.1057/s41599-021-00860-4
   Google Scholar
• Carvalho, L., Martinez-Maldonado, R., Tsai, Y.-S., Markauskaite, L., & De Laat, M. (2022). How can we design for learning in an AI world? Computers and Education: Artificial Intelligence, 3, 100053. https://doi.org/10.1016/j.caeai.2022.100053
   Google Scholar
• Chaipongpati, J., Thawesaengskulthai, N., & Koiwanit, J. (2022). Development of a university innovation ecosystem assessment model for association of Southeast Asian Nations universities. Industry and Higher Education, 36(6), 846–860. https://doi.org/10.1177/09504222221084861
   Google Scholar
• Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3
   Web of Science ®Google Scholar
• Chang, Y., Lee, S., Wong, S. F., & Jeong, S.-P. (2022). AI-powered learning application use and gratification: An integrative model. Information Technology & People, 35(7), 2115–2139. https://doi.org/10.1108/ITP-09-2020-0632
   Web of Science ®Google Scholar
• Chatterjee, S., & Bhattacharjee, K. K. (2020a). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7
   Web of Science ®Google Scholar
• Chatterjee, S., & Bhattacharjee, K. K. (2020b). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7
   Web of Science ®Google Scholar
• Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P., & Sharma, A. (2021). Assessing organizational users’ intentions and behavior to AI integrated CRM systems: A meta-UTAUT approach. Information Systems Frontiers, 25(4), 1299–1313. https://doi.org/10.1007/s10796-021-10181-1
   Web of Science ®Google Scholar
• 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), 2210461. https://doi.org/10.1080/2331186X.2023.2210461
   Web of Science ®Google Scholar
• Chedrawi, C., & Howayeck, P. (2019). Artificial intelligence a disruptive innovation in higher education accreditation programs: Expert systems and AACSB. Lecture Notes in Information Systems and Organisation, 30, 115–129. https://doi.org/10.1007/978-3-030-10737-6_8
   Google Scholar
• Choi, K.-S. (2020). Opportunities for higher education of artificial intelligence in korea. International Journal of Engineering Research & Technology, 13(11), 3428–3430. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099496874&partnerID=40&md5=b17ea209ef4c14d7520981bc79ea5b22
   Google Scholar
• Cui, W., Xue, Z., & Thai, K.-P. (2019). Performance comparison of an AI-Based adaptive learning system in China. In 2018 Chinese Automation Congress, CAC 2018, pp. 3170–3175. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CAC.2018.8623327
   Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• Davis, F. D., Granić, A., & Marangunić, N. (2023). The technology acceptance model 30 years of TAM. Technology, 1(1), 1–150.
   Google Scholar
• De Roover, K., Vermunt, J. K., & Ceulemans, E. (2022). Mixture multigroup factor analysis for unraveling factor loading noninvariance across many groups. Psychological Methods, 27(3), 281. https://doi.org/10.1037/met0000355
   PubMed Web of Science ®Google Scholar
• De Simone, S., Cicotto, G., Pinna, R., & Giustiniano, L.(2016). Engaging public servants. Management Decision, 54(7), 1569–1594. https://doi.org/10.1108/MD-02-2016-0072
   Web of Science ®Google Scholar
• Dieguez, T., Loureiro, P., & Ferreira, I. (2021). Entrepreneurship and leadership in higher education to develop the needed 21st Century skills. In 17th European Conference on Management, Leadership and Governance, ECMLG 2021. Academic Conferences International Limited, pp. 143–151. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122925043&partnerID=40&md5=65810f73e090f33037e2729a253b6adf.
   Google Scholar
• Diep, N. A., Cocquyt, C., Zhu, C., & Vanwing, T. (2016). Predicting adult learners’ online participation: Effects of altruism, performance expectancy, and social capital. Computers & Education, 101, 84–101. https://doi.org/10.1016/j.compedu.2016.06.002
   Web of Science ®Google Scholar
• Dulle, F. W., & Minishi-Majanja, M. K. (2011). The suitability of the unified theory of acceptance and use of technology (utaut) model in open access adoption studies. Information Development, 27(1), 32–45. https://doi.org/10.1177/0266666910385375
   Web of Science ®Google Scholar
• Essien, A., Chukwukelu, G., & Essien, V. (2020). Opportunities and challenges of adopting artificial intelligence for learning and teaching in higher education. In Fostering communication and learning with underutilized technologies in higher education, pp. 67–78. IGI Global. https://doi.org/10.4018/978-1-7998-4846-2.ch005
   Google Scholar
• Franzoni, V., Milani, A., Mengoni, P., & Piccinato, F. (2020). Artificial intelligence visual metaphors in e-learning interfaces for learning analytics. Applied Sciences (Switzerland), 10(20), 1–25. https://doi.org/10.3390/app10207195
   Web of Science ®Google Scholar
• Ge, Z., & Hu, Y. (2020) Innovative application of artificial intelligence (AI) in the Management of higher education and teaching. In 2020 International Conference on Artificial Intelligence and Information Technology, ICAIIT 2020. Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1533/3/032089.
   Google Scholar
• Gupta, S., & Mathur, N. (2023). Virtual communication adoption by educational leaders: Moderating role of perceived risk and benefits. The International Journal of Information and Learning Technology, 40(3), 242–258. https://doi.org/10.1108/IJILT-03-2022-0044
   Web of Science ®Google Scholar
• Gürdür Broo, D., Kaynak, O., & Sait, S. M. (2022). Rethinking engineering education at the age of industry 5.0. Journal of Industrial Information Integration, 25, 100311. https://doi.org/10.1016/j.jii.2021.100311
   Web of Science ®Google Scholar
• Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial intelligence for assessment and feedback to enhance student success in higher education. Mathematical Problems in Engineering, 2022, 1–19. https://doi.org/10.1155/2022/5215722
   Google Scholar
• 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/10.1016/j.caeai.2020.100001
   Google Scholar
• Jiao, P., Ouyang, F., Zhang, Q., & Alavi, A. H. (2022). Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review, 55(8), 6321–6344. https://doi.org/10.1007/s10462-022-10155-y
   Web of Science ®Google Scholar
• Kang, D. Y., Hur, W.-M., & Shin, Y. (2023). Smart technology and service employees’ job crafting: Relationship between STARA awareness, performance pressure, receiving and giving help, and job crafting. Journal of Retailing and Consumer Services, 73, 103282. https://doi.org/10.1016/j.jretconser.2023.103282
   Web of Science ®Google Scholar
• Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2022). What factors contribute to acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Khan, N., Sarwar, A., Chen, T. B., & Khan, S.(2022). Connecting digital literacy in higher education to the 21st century workforce. Knowledge Management and E-Learning, 14(1), 46–61. https://doi.org/10.34105/j.kmel.2022.14.004
   Web of Science ®Google Scholar
• Khoza, S. B., & Mpungose, C. B. (2022). Digitalised curriculum to the rescue of a higher education institution. African Identities, 20(4), 310–330. https://doi.org/10.1080/14725843.2020.1815517
   Google Scholar
• Kistyanto, A., Rahman, M.F.W., Adhar Wisandiko, F., & Setyawati, E.E.P. (2022). Cultural intelligence increase student’s innovative behavior in higher education: The mediating role of interpersonal trust. International Journal of Educational Management, 36(4), 419–440. https://doi.org/10.1108/IJEM-11-2020-0510
   Web of Science ®Google Scholar
• Kocak, O., Coban, M., Aydin, A., & Cakmak, N. (2021). The mediating role of critical thinking and cooperativity in the 21st century skills of higher education students. Thinking Skills and Creativity 42, 100967. https://doi.org/10.1016/j.tsc.2021.100967
   Web of Science ®Google Scholar
• Kour, R., & Karim, R. (2020). Cybersecurity workforce in railway: Its maturity and awareness. Journal of Quality in Maintenance Engineering, 27(3), 453–464. https://doi.org/10.1108/JQME-07-2020-0059
   Web of Science ®Google Scholar
• Kuleto, V., Mihoreanu, L, Dinu, D. G., Ilić, M. P., & Păun, D. (2023). Artificial intelligence, Machine learning and extended reality: Potential problem solvers for higher education issues. In Springer series on cultural computing (pp. 123–136). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-27166-3_7
   Google Scholar
• Lei, M., Ian, M. C., Liu, H., & Bell, J.(2022). The acceptance of telepresence robots in higher education. International Journal of Social Robotics, 14(4), 1025–1042. https://doi.org/10.1007/s12369-021-00837-y
   PubMed Web of Science ®Google Scholar
• Li, J. J., Bonn, M. A., & Ye, B. H. (2019). Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172–181. https://doi.org/10.1016/j.tourman.2019.02.006
   Web of Science ®Google Scholar
• Li, H., Edwards, S. M., & Lee, J.-H. (2002). Measuring the intrusiveness of advertisements: Scale development and validation. Journal of Advertising, 31(2), 37–47. https://doi.org/10.1080/00913367.2002.10673665
   Web of Science ®Google Scholar
• Li, J., Li, J., Yang, Y., & Ren, Z. (2021). Design of higher education system based on artificial intelligence technology. Discrete Dynamics in Nature & Society, 2021, 1–11. https://doi.org/10.1155/2021/3303160
   Web of Science ®Google Scholar
• Liu, J., Gong, X., Weal, M., Dai, W., Hou, S., & Ma, J. (2023). Attitudes and associated factors of patients’ adoption of patient accessible electronic health records in China—A mixed methods study. Digital Health, 9, 20552076231174100. https://doi.org/10.1177/20552076231174101
   Web of Science ®Google Scholar
• Marcinkowski, F. (2020). Implications of AI (un-)fairness in higher education admissions: The effects of perceived AI (un-)fairness on exit, voice and organizational reputation. In 3rd ACM Conference on Fairness, Accountability, and Transparency, FAT* 2020. Association for Computing Machinery, Inc, pp. 122–130. https://doi.org/10.1145/3351095.3372867.
   Google Scholar
• Marks, B., & Thomas, J. (2022). Adoption of virtual reality technology in higher education: An evaluation of five teaching semesters in a purpose-designed laboratory. Education and Information Technologies, 27(1), 1287–1305. https://doi.org/10.1007/s10639-021-10653-6
   PubMed Web of Science ®Google Scholar
• Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education Sciences, 13(9), 856. https://doi.org/10.3390/educsci13090856
   Web of Science ®Google Scholar
• Miles, J. (2014). Tolerance and Variance inflation factor. Wiley StatsRef: Statistics Reference Online, https://doi.org/10.1002/9781118445112.stat06593
   Google Scholar
• Minkevics, V., & Kampars, J. (2021). Artificial intelligence and big data driven is security management solution with applications in higher education organizations. In P. Chemouil. (Ed.) 17th International Conference on Network and Service Management, CNSM 2021. Institute of Electrical and Electronics Engineers Inc. pp. 340–344. https://doi.org/10.23919/CNSM52442.2021.9615575.
   Google Scholar
• Mishra, R. (2019). Usage of data analytics and artificial intelligence in ensuring quality assurance at higher education institutions. In 2019 Amity International Conference on Artificial Intelligence, AICAI 2019. Institute of Electrical and Electronics Engineers Inc. pp. 1022–1025. https://doi.org/10.1109/AICAI.2019.8701392.
   Google Scholar
• Mohamed Zabri, S., Mohammad Abakar, Y., & Ahmad, K. (2023). Exploring the acceptance of online learning among students in technical and non-technical programmes at a higher education institution. Cogent Education, 10(2), 2284552. https://doi.org/10.1080/2331186X.2023.2284552
   Google Scholar
• Moreira-Fontán, E., García-Señorán, M., Conde-Rodríguez, Á., González, A. (2019). Teachers’ ICT-related self-efficacy, job resources, and positive emotions: Their structural relations with autonomous motivation and work engagement. Computers & Education 134, 63–77. https://doi.org/10.1016/j.compedu.2019.02.007
   Web of Science ®Google Scholar
• Moşteanu, N. R. (2022). Machine learning and robotic process automation take higher education One step Further. Romanian Journal of Information Science and Technology, 25(1), 92–99. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128311179&partnerID=40&md5=1f26e34db061fe50ed8b381520200072
   Web of Science ®Google Scholar
• Müller, W., & Leyer, M. (2023). Understanding intention and use of digital elements in higher education teaching. Education and Information Technologies, 28(12), 15571–15597. https://doi.org/10.1007/s10639-023-11798-2
   Google Scholar
• Naylor, D., & Nyanjom, J. (2021). Educators’ emotions involved in the transition to online teaching in higher education. Higher Education Research & Development, 40(6), 1236–1250. https://doi.org/10.1080/07294360.2020.1811645
   Web of Science ®Google Scholar
• Neumann, C. Stroud, K. M, Bailey, S, Allison, K, & Everts, S. S. 2021). 21st-century competencies in higher education: A practitioner’s guide. In Handbook of research on barriers for teaching 21st-Century competencies and the impact of digitalization. IGI Globalpp. 293–315. https://doi.org/10.4018/978-1-7998-6967-2.ch016.
   Google Scholar
• Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research & Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
   PubMed Web of Science ®Google Scholar
• 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
   Google Scholar
• Nikou, S., & Maslov, I. (2023). Finnish university students’ satisfaction with e-learning outcomes during the COVID-19 pandemic. International Journal of Educational Management, 37(1), 1–21. https://doi.org/10.1108/IJEM-04-2022-0166
   Web of Science ®Google Scholar
• Nsamba, A., & Chimbo, B. (2023). The use of modern technologies in postgraduate student support in an open learning institution: Are there new cultures emerging? Cogent Education, 10(2), 2270290. https://doi.org/10.1080/2331186X.2023.2270290
   Web of Science ®Google Scholar
• Nyathi, M., & Sibanda, E. (2022). E-learning: Substitutability of learner–learner, and learner–facilitator interactions to enhance learner satisfaction in higher education. Journal of Research in Innovative Teaching & Learning, 16(2), 210–225. ahead-of-print(ahead-of-print) https://doi.org/10.1108/JRIT-04-2022-0018
   Google Scholar
• Odhiambo, G. (2016). Higher education in Kenya: An assessment of current responses to the imperative of widening access. Journal of Higher Education Policy & Management, 38(2), 196–211. https://doi.org/10.1080/1360080X.2016.1150551
   Web of Science ®Google Scholar
• Olapade, D. T., Aluko, T. B., Adisa, A. L., & Abobarin, A. A. (2023). A framework for assessment of customary land delivery institutions: Instrument development, content validity and reliability testing. Property Management, 41(5), 729–752. https://doi.org/10.1108/PM-06-2022-0041
   Google Scholar
• Pedral Sampaio, R., Aguiar Costa, A., & Flores-Colen, I. (2023). A discussion of digital transition impact on facility management of hospital buildings. Facilities, 41(5/6), 389–406. https://doi.org/10.1108/F-07-2022-0092
   Web of Science ®Google Scholar
• Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1). https://doi.org/10.1186/s41039-017-0062-8
   PubMedGoogle Scholar
• Porter, W. W., & Graham, C. R. (2016). Institutional drivers and barriers to faculty adoption of blended learning in higher education. British Journal of Educational Technology, 47(4), 748–762. https://doi.org/10.1111/bjet.12269
   Web of Science ®Google Scholar
• Pradana, M., Elisa, H. P., & Syarifuddin, S. (2023). Discussing ChatGPT in education: A literature review and bibliometric analysis. Cogent Education, 10(2), 2243134. https://doi.org/10.1080/2331186X.2023.2243134
   Web of Science ®Google Scholar
• Qu, J., Zhao, Y., & Xie, Y. (2022). Artificial intelligence leads the reform of education models. Systems Research and Behavioral Science, 39(3), 581–588. https://doi.org/10.1002/sres.2864
   Web of Science ®Google Scholar
• Rahimi, A. R., & Tafazoli, D. (2022). The role of university teachers’ 21st-century digital competence in their attitudes toward ICT integration in higher education: Extending the theory of planned behavior. The JALT CALL Journal, 18(2), 238–263. https://doi.org/10.29140/jaltcall.v18n2.632
   Google Scholar
• Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management & Data Systems, 121(12), 2637–2650. https://doi.org/10.1108/IMDS-02-2021-0082
   Web of Science ®Google Scholar
• Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1). https://doi.org/10.37074/jalt.2023.6.1.9
   Google Scholar
• Sanusi, M. S. (2022). Action research to reassess the acceptance and use of technology in a blended learning approach amongst postgraduate business students. Cogent Education, 9(1), 2145813. https://doi.org/10.1080/2331186X.2022.2145813
   Web of Science ®Google Scholar
• Sarfraz, M., Khawaja, K. F., & Ivascu, L. (2022). Factors affecting business school students’ performance during the COVID-19 pandemic: A moderated and mediated model. The International Journal of Management Education, 20(2), 100630. https://doi.org/10.1016/j.ijme.2022.100630
   Web of Science ®Google Scholar
• Sayed Al Mnhrawi, D. N. T. A., & Alreshidi, H. A. (2022). A systemic approach for implementing AI methods in education during COVID-19 pandemic: Higher education in Saudi Arabia. World Journal of Engineering, 20(5), 808–814. https://doi.org/10.1108/WJE-11-2021-0623
   Web of Science ®Google Scholar
• Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
   Web of Science ®Google Scholar
• Sharma, A. K., Pareta, A, Meena, J., & Sharma, R. (2022). A long term impact of artificial intelligence and robotics on higher education. In 2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCAI53970.2022.9752633.
   Google Scholar
• Shin, S., Ha, M., & Lee, J.-K. (2017). High school students’ perception of artificial intelligence: Focusing on conceptual understanding, emotion and risk perception. Journal of Learner-Centered Curriculum & Instruction, 17(21), 289–312. https://doi.org/10.22251/jlcci.2017.17.21.289
   Google Scholar
• Silic, M., & Back, A. (2017). Impact of gamification on user’s knowledge-sharing practices: Relationships between work motivation, performance expectancy and work engagement. In T. X. Bui. and R. Sprague (Eds.), 50th annual Hawaii international conference on System Sciences, HICSS 2017 (pp. 1308–1317). IEEE Computer Society. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086568517&partnerID=40&md5=78947bf9868ebc7287223b8e2eb284b2
   Google Scholar
• Slimi, Z., & Carballido, B. V. (2023). Navigating the ethical challenges of artificial intelligence in higher education: An analysis of seven Global AI Ethics Policies. TEM Journal, 12(2), 590–602. https://doi.org/10.18421/TEM122-02
   Google Scholar
• Sohn, K., & Kwon, O. (2020a). Technology acceptance theories and factors influencing artificial intelligence-based intelligent products. Telematics and Informatics 47, 101324. https://doi.org/10.1016/j.tele.2019.101324
   Web of Science ®Google Scholar
• Sohn, K., & Kwon, O. (2020b). Technology acceptance theories and factors influencing artificial intelligence-based intelligent products. Telematics and Informatics 47, 101324. https://doi.org/10.1016/j.tele.2019.101324
   Web of Science ®Google Scholar
• Sun, H., Ni, W., & Farouk, A. (2022). Design and application of an AI-Based text content moderation system. Scientific Programming 2022, 1–9. https://doi.org/10.1155/2022/2576535
   Web of Science ®Google Scholar
• Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
   Web of Science ®Google Scholar
• Teng, Y., Zhang, J., & Sun, T. (2022). Data-driven decision-making model based on artificial intelligence in higher education system of colleges and universities. Expert Systems, 40(4), https://doi.org/10.1111/exsy.12820
   Web of Science ®Google Scholar
• van Twillert, A., Kreijns, K., Vermeulen, M., & Evers, A. (2020). Teachers’ beliefs to integrate Web 2.0 technology in their pedagogy and their influence on attitude, perceived norms, and perceived behavior control. International Journal of Educational Research Open 1, 100014. https://doi.org/10.1016/j.ijedro.2020.100014
   Google Scholar
• Venkatesh, V. (2003). User acceptance of Information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, J., Balaji, D., Thenmozhi, S., & Balasubramanie, P. (2012). Interactional behavior and relational impact of physicians in healthcare with emotional intelligence competencies. Life Science Journal, 9(3), 2169–2178.
   Google Scholar
• Wenge, M. (2021). Artificial intelligence-based real-time communication and ai-multimedia services in higher education. Journal of Multiple-Valued Logic and Soft Computing, 36(1), 231–248. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108330080&partnerID=40&md5=9d2751efd13ed63b082b01eb5c52a452
   Web of Science ®Google Scholar
• Williams, M. D., Dwivedi, Y. K., Lal, B., & Schwarz, A. (2009). Contemporary trends and issues in IT adoption and diffusion research. Journal of Information Technology, 24(1), 1–10. https://doi.org/10.1057/jit.2008.30
   Web of Science ®Google Scholar
• Wilson, M. L. (2023). The impact of technology integration courses on preservice teacher attitudes and beliefs: A meta-analysis of teacher education research from 2007–2017. Journal of Research on Technology in Education, 55(2), 252–280. https://doi.org/10.1080/15391523.2021.1950085
   Web of Science ®Google Scholar
• 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. https://doi.org/10.3389/fpsyg.2022.870777
   Web of Science ®Google Scholar
• Xue, Y., Wang, Y., & A Saeed, R. (2022). Artificial intelligence for education and teaching. Wireless Communications and Mobile Computing 2022, 2022, 1–10. https://doi.org/10.1155/2022/4750018
   Google Scholar
• Yamane, T. (1967). Elementary sampling theory. Englewood Cliffs. [Preprint]
   Google Scholar
• Yip, K. H. T., Lo, P., Ho, K. K. W., & Chiu, D. K. W. (2021). Adoption of mobile library apps as learning tools in higher education: A tale between Hong Kong and Japan. Online Information Review, 45(2), 389–405. https://doi.org/10.1108/OIR-07-2020-0287
   Web of Science ®Google Scholar
• Zang, G., Liu, M., Yu, B., & Khan, R. (2022). The application of 5g and artificial intelligence technology in the innovation and reform of college english education. Computational Intelligence and Neuroscience, 2022, 1–8. https://doi.org/10.1155/2022/9008270
   Google Scholar
• Zhu, Z., & Huang, W. (2023). A meta-analysis of mobile learning adoption using extended UTAUT. Information Development, 02666669231176428. https://doi.org/10.1177/02666669231176428
   Web of Science ®Google Scholar