Predicting the actual use of artificial intelligence features of Apple Vision Pro using PLS-SEM

Rana Saeed Al-Maroof; Ragad M. Tawafak; Waleed Mugahed Al-Rahmi; Khadijah Amru Alhashmi; Ibrahim Yaussef Alyoussef

doi:10.30935/cedtech/16208

Rana Saeed Al-Maroof ¹, Ragad M. Tawafak ², Waleed Mugahed Al-Rahmi ³ ^* , Khadijah Amru Alhashmi ⁴, Ibrahim Yaussef Alyoussef ⁵

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CONT ED TECHNOLOGY, Volume 17, Issue 3, Article No: ep580

https://doi.org/10.30935/cedtech/16208

Submitted: 20 November 2024, Published Online: 27 March 2025

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Abstract

Despite the spread of artificial intelligence (AI) tools and applications, the Apple Vision Pro (AVP) stands out for its innovative features compared to other types of wearable technology. Moreover, traditional glasses have been deficient in incorporating many AI innovations that could enhance user experiences and pose new challenges. In response to these innovative aspects, this study aims to develop a theoretical model by integrating constructs from the expectation confirmation model (ECM) (expectation confirmation and satisfaction [SAT]) and aspects from the Uses and Gratifications (U&G) theory. The perceived human likeness of AI mediates the model. This study focuses on the educational domain, aiming to assess how this technology enhances the academic environment and improves learning outcomes. The method used was a survey distributed among 134 participants from Al Buraimi University College, Oman, for two departments: English, linguistics, and information technology. The study consists of seven hypotheses to emphasize the conceptual model. The findings significantly impact predicting the actual use (AU) of AI features of AVP, indicating that users’ expectations and SAT play a pivotal role in technology adoption and are closely linked to the variable human likeness. Similarly, factors such as entertainment value, informativeness, and the lack of web irritations significantly influence technology adoption and are associated with the human likeness variable. However, Informativeness gratification failed to pass the proposal and showed a negative indicator for predicting the AU of AI. The implications drawn from these results suggest that educational institutions should tailor their courses and curricula to promote the effective use of AI.

Keywords: Apple Vision Pro, vision, ECM, U&G theory, human likeness

References

Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. In Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 373–383). Springer. https://doi.org/10.1007/978-3-030-49186-4_31
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Alfarsi, G. M., Jabbar, J., Tawafak, R. M., Iqbal, S., Alsidiri, A., Alsinani, M., & Sulaiman, H. (2020). Mobile application system supported BUC student’s services and learning. International Journal of Interactive Mobile Technologies, 14(9), 79–94. https://doi.org/10.3991/ijim.v14i09.12053
Alfarsi, G. M., Tawafak, R. M., ElDow, A., Malik, S. I., Jabbar, J., Al Sideiri, A., & Mathew, R. (2021). The general view about artificial intelligence technology in the education domain. In Proceedings of the International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies (pp. 120–127). https://doi.org/10.5220/0010304501200127
Al-Maroof, R. A. S., & Al-Emran, M. (2018). Students acceptance of Google Classroom: An exploratory study using PLS-SEM approach. International Journal of Emerging Technologies in Learning, 13(6), Article 112. https://doi.org/10.3991/ijet.v13i06.8275
Al-Maroof, R. A. S., & Salloum, S. A. (2020). An integrated model of continuous intention to use Google Classroom. In M. Al-Emran, K. Shaalan, & A. Ella Hassanien (Eds.), Recent advances in intelligent systems and innovative applications (pp. 311–335). Springer. https://doi.org/10.1007/978-3-030-47411-9_18
Al-Maroof, R. A. S., Alhumaid, K., Alshaafi, A., Akour, I., Bettayeb, A., Alfaisal, R., & Salloum, S. A. (2024). A comparative analysis of ChatGPT and Google in educational settings: Understanding the influence of mediators on learning platform adoption. In A. Al-Marzouqi, S. A. Salloum, M. Al-Saidat, A. Aburayya, & B. Gupta (Eds.), Artificial intelligence in education: The power and dangers of ChatGPT in the classroom. Studies in big data, vol 144 (pp. 365–386). Springer. https://doi.org/10.1007/978-3-031-52280-2_23
Al-Maroof, R. A. S., Alnazzawi, N., Akour, I., Ayoubi, K., Alhumaid, K., Nasser, N., Alaraimi, S., Albkushi, A., Thabit, S., Alfaisal, R., Aburayya, A., & Salloum, S. A. (2022). Students’ perception towards using electronic feedback after the pandemic: Post-acceptance study. International Journal of Data and Network Science, 6(4), 1233–1248. https://doi.org/10.5267/j.ijdns.2022.6.010
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A bright education perspective. Heliyon, 10(11), Article e31887. https://doi.org/10.1016/j.heliyon.2024.e31887
Al-Obaydi, L. H., Pikhart, M., & Tawafak, R. M. (2023). Online assessment in language teaching environment through essays, oral discussion, and multiple-choice questions. Computer-Assisted Language Learning Electronic Journal, 24(2), 175–197.
Al-Rahmi, W. M., Al-Adwan, A. S., Al-Maatouk, Q., Othman, M. S., Alsaud, A. R., Almogren, A. S., & Al-Rahmi, A. M. (2023). Integrating communication and task-technology fit theories: The adoption of digital media in learning. Sustainability, 15(10), Article 8144. https://doi.org/10.3390/su15108144
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Chen, Q., & Wells, W. D. (1999). Attitude toward the site. Journal of Advertising Research, 39(5), 27–38. https://doi.org/10.1037/t49047-000
Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Psychology Press. https://doi.org/10.4324/9781315827506
Dahri, N. A., Vighio, M. S., Alismaiel, O. A., & Al-Rahmi, W. M. (2022). Assessing the impact of mobile-based training on teachers’ achievement and usage attitude. International Journal of Interactive Mobile Technologies, 16(9), 107–129. https://doi.org/10.3991/ijim.v16i09.30519
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A., & Soomro, R. B. (2024). Extended TAM-based acceptance of AI-powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10(8), Article e29317. https://doi.org/10.1016/j.heliyon.2024.e29317
Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using Davis’s perceived usefulness and ease-of-use instruments for decision making: A confirmatory and multigroup invariance analysis. Decision Sciences, 29(4), 839–869. https://doi.org/10.1111/j.1540-5915.1998.tb00879.x
Ducoffe, R. H. (1996). Advertising value and advertising on the web. Journal of Advertising Research, 36(5), 21–35.
Ebadi, S., & Rahimi, R. (2024). A systematic review of technology-mediated feedback research: Insights and future directions. Journal of English Language Teaching and Learning, 16(34), 37–54. https://doi.org/10.22034/elt.2024.63638.2698
Egger, J., Gsaxner, C., Chen, X., Bian, J., Kleesiek, J., & Puladi, B. (2023). Apple Vision Pro for healthcare: “The ultimate display”? -- Entering the wonderland of precision medicine. arXiv. https://doi.org/10.48550/arXiv.2308.04313
Eighmey, J., & McCord, L. (1998). Adding value in the information age: Uses and gratifications of sites on the world wide web. Journal of Business Research, 41(3), 187–194. https://doi.org/10.1016/S0148-2963(97)00061-1
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
Gonzalez Nieto, L., Wallis, A., Clements, J., Miranda Sazo, M., Kahlke, C., Kon, T. M., & Robinson, T. L. (2023). Evaluation of computer vision systems and applications to estimate trunk cross-sectional area, flower cluster number, thinning efficacy and yield of apple. Horticulturae, 9(8), Article 880. https://doi.org/10.3390/horticulturae9080880
Ismail, A., Idris, M. Y. I., Ayub, M. N., & Por, L. Y. (2018). Vision-based apple classification for smart manufacturing. Sensors, 18(12), Article 4353. https://doi.org/10.3390/s18124353
Katawetawaraks, C., & Wang, C. (2011). Online shopper behavior: Influences of online shopping decision. Asian Journal of Business Research, 1(2). https://doi.org/10.14707/ajbr.110012
Keelson, S. A., Bruce, E., Egala, S. B., Amoah, J., & Bashiru Jibril, A. (2024). Driving forces of social media and its impact on tourists’ destination decisions: A uses and gratification theory. Cogent Social Sciences, 10(1), Article 2318878. https://doi.org/10.1080/23311886.2024.2318878
Kim, K. J., Park, E., Sundar, S. S., & Del Pobil, A. P. (2012). The effects of immersive tendency and need to belong on human-robot interaction. In Proceedings of the 7^th Annual ACM/IEEE International Conference on Human-Robot Interaction (pp. 207–208). ACM. https://doi.org/10.1145/2157689.2157758
Kumar, K. A., & Natarajan, S. (2020). An extension of the expectation confirmation model (ECM) to study continuance behavior in using e-health services. Innovative Marketing, 16(2), Article 15. https://doi.org/10.21511/im.16(2).2020.02
Lim, W. M. (2015). Antecedents and consequences of e-shopping: An integrated model. Internet Research, 25(2), 184–217. https://doi.org/10.1108/IntR-11-2013-0247
Lim, W. M., & Ting, D. H. (2012). E-shopping: An analysis of the uses and gratifications theory. Modern Applied Science, 6(5), Article 48. https://doi.org/10.5539/mas.v6n5p48
Lin, S. W., & Huang, C. D. (2024). Hooked on audio! Unveiling the secrets of podcast stickiness through social identity and uses and gratification theories. Technology in Society, 76, Article 102422. https://doi.org/10.1016/j.techsoc.2023.102422
Luo, X. (2002). Uses and gratifications theory and e-consumer behaviors: A structural equation modeling study. Journal of Interactive Advertising, 2(2), 34–41. https://doi.org/10.1080/15252019.2002.10722060
Malik, S. I., Mathew, R., Al-Sideiri, A., Jabbar, J., Al-Nuaimi, R., & Tawafak, R. M. (2022). Enhancing problem-solving skills of novice programmers in an introductory programming course. Computer Applications in Engineering Education, 30(1), 174–194. https://doi.org/10.1002/cae.22450
Malik, S. I., Tawafak, R. M., & Shakir, M. (2021). Aligning and assessing teaching approaches with solo taxonomy in a computer programming course. International Journal of Information and Communication Technology Education, 17(4), 1–15. https://doi.org/10.4018/IJICTE.20211001.oa5
Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350–363. https://doi.org/10.1016/j.im.2005.08.006
Obeid, A., Ibrahim, R., & Fadhil, A. (2024). Extended model of expectation confirmation model to examine users’ continuous intention toward the utilization of e-learning platforms. IEEE Access, 12, 40752–40764. https://doi.org/10.1109/ACCESS.2024.3373190
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.2307/3150499
Park, N., Jang, K., Cho, S., & Choi, J. (2021). Use of offensive language in human-artificial intelligence chatbot interaction: The effects of ethical ideology, social competence, and perceived human likeness. Computers in Human Behavior, 121, Article 106795. https://doi.org/10.1016/j.chb.2021.106795
Pikhart, M., Al-Obaydi, L. H., & Tawafak, R. M. (2022). The implementation of critical pedagogy in digital second language acquisition in higher education: Age and gender differences. Computer-Assisted Language Learning Electronic Journal, 23(2), 56–75.
Prasetya, F. H., Harnadi, B., Widiantoro, A. D., & Pamudji, A. K. (2022). Expectation-confirmation model (ECM) to see satisfaction and continued intention of e-learning (“cyber”). In Proceedings of the 6^th International Conference on Information Technology (pp. 303–308). IEEE. https://doi.org/10.1109/InCIT56086.2022.10067619
Rahi, S., Alghizzawi, M., & Ngah, A. H. (2023). Factors influence user’s intention to continue use of e-banking during COVID-19 pandemic: The nexus between self-determination and expectation confirmation model. EuroMed Journal of Business, 18(3), 380–396. https://doi.org/10.1108/EMJB-12-2021-0194
Rehman, U., & Cao, S. (2016). Augmented-reality-based indoor navigation: A comparative analysis of handheld devices versus Google Glass. IEEE Transactions on Human-Machine Systems, 47(1), 140–151. https://doi.org/10.1109/THMS.2016.2620106
Rubin, A. M. (2009). Uses and gratifications. In R. L. Nabi, & M. B. Oliver (Eds.), The SAGE handbook of media processes and effects (pp. 147–159). SAGE.
Ruijten, P. A. M., Midden, C. J. H., & Ham, J. (2015). Lonely and susceptible: The influence of social exclusion and gender on persuasion by an artificial agent. International Journal of Human-Computer Interaction, 31(11), 832–842. https://doi.org/10.1080/10447318.2015.1067480
Santos, E., & Castillo, V. (2024). Apple Vision Pro: Comments in healthcare. SSRN. https://doi.org/10.2139/ssrn.4694196
Tatnall, A. (2020). Editorial for EAIT issue 2, 2020. Education and Information Technologies, 25, 647–657. https://doi.org/10.1007/s10639-020-10135-1
Tawafak, R. M., Al-Obaydi, L. H., & Pikhart, M. (2023a). Competency categorization and roles of online teachers from the perspective of university students. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1009000
Tawafak, R. M., Al-Rahmi, W. M., Almogren, A. S., Al Adwan, M. N., Safori, A., Attar, R. W., & Habes, M. (2023b). Analysis of e-learning system use using combined TAM and ECT factors. Sustainability, 15(14), Article 11100. https://doi.org/10.3390/su151411100
Tawafak, R. M., Alyoussef, I. Y., & Al-Rahmi, W. M. (2023c). Essential factors to improve student performance using an e-learning model: Review study. International Journal of Interactive Mobile Technologies, 17(3), 160–176. https://doi.org/10.3991/ijim.v17i03.35727
Ullah, N., Al-Rahmi, W. M., Alblehai, F., Fernando, Y., Alharbi, Z. H., Zhanbayev, R., Al-Adwan, A. S., & Habes, M. (2024). Blockchain-powered grids: Paving the way for a sustainable and efficient future. Heliyon, 10(10), Article e31592. https://doi.org/10.1016/j.heliyon.2024.e31592
Waisberg, E., Ong, J., Masalkhi, M., Zaman, N., Sarker, P., Lee, A. G., & Tavakkoli, A. (2024). Apple Vision Pro and why extended reality will revolutionize the future of medicine. Irish Journal of Medical Science (1971-), 193(1), 531–532. https://doi.org/10.1007/s11845-023-03437-z
Walsh, G., & Yamin, M. (2005). Towards a conceptual model of consumer confusion. Advances in Consumer Research, 32(1), 143–150.
Wu, R., Wang, G., & Yan, L. (2020). The effects of online store informativeness and entertainment on consumers’ approach behaviors: Empirical evidence from China. Asia Pacific Journal of Marketing and Logistics, 32(6), 1327–1342. https://doi.org/10.1108/APJML-03-2019-0182
Yu, H. (2024). Why do people use Metaverse? A uses and gratification theory perspective. Telematics and Informatics, 89, Article 102110. https://doi.org/10.1016/j.tele.2024.102110

Citation

Al-Maroof, R. S., Tawafak, R. M., Al-Rahmi, W. M., Alhashmi, K. A., & Alyoussef, I. Y. (2025). Predicting the actual use of artificial intelligence features of Apple Vision Pro using PLS-SEM. Contemporary Educational Technology, 17(3), ep580. https://doi.org/10.30935/cedtech/16208

Al-Maroof, R. S., Tawafak, R. M., Al-Rahmi, W. M., Alhashmi, K. A., and Alyoussef, I. Y. (2025). Predicting the actual use of artificial intelligence features of Apple Vision Pro using PLS-SEM. Contemporary Educational Technology, 17(3), ep580. https://doi.org/10.30935/cedtech/16208

Al-Maroof RS, Tawafak RM, Al-Rahmi WM, Alhashmi KA, Alyoussef IY. Predicting the actual use of artificial intelligence features of Apple Vision Pro using PLS-SEM. CONT ED TECHNOLOGY. 2025;17(3), ep580. https://doi.org/10.30935/cedtech/16208

Al-Maroof, Rana Saeed, Ragad M. Tawafak, Waleed Mugahed Al-Rahmi, Khadijah Amru Alhashmi, and Ibrahim Yaussef Alyoussef. "Predicting the actual use of artificial intelligence features of Apple Vision Pro using PLS-SEM". Contemporary Educational Technology 2025 17 no. 3 (2025): ep580. https://doi.org/10.30935/cedtech/16208

Al-Maroof, Rana Saeed et al. "Predicting the actual use of artificial intelligence features of Apple Vision Pro using PLS-SEM". Contemporary Educational Technology, vol. 17, no. 3, 2025, ep580. https://doi.org/10.30935/cedtech/16208

Al-Maroof RS, Tawafak RM, Al-Rahmi WM, Alhashmi KA, Alyoussef IY. Predicting the actual use of artificial intelligence features of Apple Vision Pro using PLS-SEM. CONT ED TECHNOLOGY. 2025;17(3):ep580. https://doi.org/10.30935/cedtech/16208