Assessing The Purpose Of Implementing Artificial Intelligence-Based Robots In An Educational Institution For The Purpose Of Educating Learners
Article Sidebar
Main Article Content
Abstract
Despite certain advancements, the incorporation of artificial intelligence into educational institutions is still inadequate. The demand for instructors will endure for a considerable period, yet the incorporation of AI-powered robots in schools has significantly diminished the importance of teachers. The aim of the current research was to assess the inclination of Jordanian institutions of higher learning to embrace artificial intelligence-based robots for educational objectives. Utilising the TAM, this research presents nine hypotheses to assess learners' inclination to embrace artificial intelligence-based robots in the field of education. The data of learners was gathered and examined employing PLS-SEM. The research's findings indicated that all hypotheses were confirmed. The findings suggest that learners are receptive to incorporating artificial intelligence-powered robots into their educational experience. Nevertheless, the results indicated that factors such as perceived ease of use, perceived utility, perceived risk, anxiety towards robots, self-efficacy in interacting with robots, and technology-related insecurity had no significant impact on the attitude towards artificial intelligence-based robots. The study's findings will offer valuable insights to university administrations on the importance of artificial intelligence-based robots in education. Furthermore, the results will assist developers of robots, policymakers, and university administrators in creating and executing artificial intelligence-driven robots that meet current educational requirements.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Ajzen, I. (1991). The theory of planned behavior. Organ Behav Hum Decis Process; 50 (2):179–211.
Altay, N., & Pal, R. (2022). Coping in supply chains: a conceptual framework for disruption management. Int. J. Logist. Manag. https://doi.org/10.1108/IJLM-05-2021-0305.
Al-Zoubi, A. (2023). An extension of the diffusion of innovation theory for business intelligence adoption: A maturity perspective on project management. Uncertain Supply Chain Management 11 2 465–472.
Alzoubi, A., & Alzoubi, M. (2020). Determinants of E-Learning Based on Cloud Computing adoption: Evidence from a Students' Perspective in Jordan; Vol. 29, No. 4, (2020), pp. 1361-1370,.
Al-Zoubi, e. a. (2023). The influence of soft and hard quality management practises on quality improvement and performance in UAE higher education. International Journal of Data Science 11 3.
Atman, U., Yavuz, G., & Koçak, Y. (2022). A systematic review study on educational robotics and robots. Interactive Learning Environments, 1–25. https://doi. org/10.1080/10494820.2021.2023890.
Battistoni, E., Gitto, S., Murgia, G., & Campisi, D. (2023). Adoption paths of digital transformation in manufacturing SME. Int. J. Prod. Econ. 255 (October 2022), 108675 https://doi.org/10.1016/j.ijpe.2022.108675.
Bentler, P., & Chou, C. (1987). Practical issues in structural modeling,’Sociol. Methods Res., vol. 16, no. 1, pp. 78–117, Aug.
Boddu, R., Karmakar, P., Bhaumik, A., Nassa, V., Vandana, W., & Bhattacharya, S. (2022). Analyzing the impact of machine learning and Artificial Intelligence and its effect on management of lung cancer detection in covid-19 pandemic. Materials Today: Proceedings 56, 2213–2216. https://doi.org/10.1016/j.matpr.2021.11.549.
Chandra, S., Dillenbourg, P., & Paiva, A. (2020). Children teach handwriting to a social robot with different learning competencies. International Journal of Social Robotics, 12(3), 721–748. https://doi.org/10.1007/s12369-019-00589-w.
Chang, C., Hwang, G., & Gau, M. (2022). Promoting students’ learning achievement and self-efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53(1), 171–188. https://doi.org/10.1111/ bjet.13158.
Chang, S., Hsu, T., & Jong, M. (2020). Integration of the peer assessment approach with a virtual reality design system for learning earth science. Computers & Education, 146, Article 103758. https://doi.org/10.1016/j.compedu.2019.103758.
Chen, B., Hwang, G., & Wang, S. (2021). Gender differences in cognitive load when applying game-based learning with intelligent robots. Educational Technology & Society, 24(3), 102 115. https://www.jstor.org/stable/27032859.
Chen, H., Park, H., & Breazeal, C. (2020). Teaching and learning with children: Impact of reciprocal peer learning with a social robot on children’s learning and emotive engagement. Computers & Education, 150, Article 103836. https://doi.org/ 10.1016/.
Cheung, G., Cooper-Thomas, H., Lau, R., & Wang, L. (2023). gent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pac. J. Manag. https://doi.org/10.1007/s10490-023-09871- y.
Chevalier, M., Giang, C., Piatti, A., & Mondada, F. (2020). Fostering computational thinking through educational robotics: A model for creative computational problem solving. International Journal of STEM Education, 7(1), 1–18. https://doi.org/ 10.1186/s4.
Chi, N. (2022). Transforming travel motivation into intention to pay for nature conservation in national parks: The role of chatbot e-services, J. Nature Conservation, vol. 68, Art. no. 126226, doi: 10.1016/J.JNC.2022.126226.
Conti, D., Cirasa, C., Nuovo, S., & Nuovo, A. (2020). Robot, tell me a tale!: A social robot as tool for teachers in kindergarten. Interaction Studies, 21(2), 220–242. https://doi.org/10.1075/is.18024.con.
Daoud, L., Marei, A., Al-Jabaly, S., & Aldaas, A. (2021). Moderating the role of top management commitment in usage of computer-assisted auditing techniques. Accounting 7 (2), 457–468. https://doi.org/10.5267/j.ac.2020.11.005.
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. J. Acad. Mark. Sci. 48 (1), 24–42. https://doi.org/ 10.1007/s11747-019-00696-0.
Davis , D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q;13(3):319–40. https://doi.org/10.2307/ 249008.
Deb, M., & David, E. (2014). An empirical examination of customers’ adoption of m-banking in India. Journal of Marketing Intelligence & Planning, 32(4), 475–494. https://doi.org/10.1108/MIP-07-2013-0119.
Dondapati, A., Sheoliha, N., Panduro-Ramirez, J., Bakhare, R., Sreejith, P., & Devi, V. (2022). An integrated artificial intelligence framework for knowledge production and B2B marketing rational analysis for enhancing business performance.Mater. Today.: Proc. 56, 2232–2235. https://doi.org/10.1016/j.
Evripidou, S., Georgiou, K., Doitsidis, L., Amanat, W., Zinonos, Z., & Chatzichristofis, A. (2020). Educational robotics: Platforms, competitions and expected learning outcomes. IEEE Access, 8, 219534–219562. https://doi.org/ 10.1109/ACCESS.202.
Fishbein, M., & Ajzen, J. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Reading: Addison-Wesley Publication Company.
Graham, S. (2022). Self-efficacy and language learning—What it is and what it isn’t, Lang. Learn. J., vol. 50, no. 2, pp. 186–207.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet, J. Marketing Theory Pract., vol. 19, no. 2, pp. 139–152, Apr.
Hooijdonk, ,. C. (2021). Chatbots in the tourism industry: The effects of communication style and brand familiarity on social presence and brand attitude, in Proc. Adjunct 29th ACM Conf. User Modeling, Adaptation Personalization, Jun. pp. 375_381, doi: 10.1145/3450614.3463599.
Huang, J., Saleh, S., & Liu, Y. (2021). A review on arti_cial intelligence in education, Academic J. Interdiscipl. Stud., vol. 10, no. 3, p. 206, doi: 10.36941/ajis-2021-0077.
Huang, M., & Rust, R. (2020). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172.
Hwang, G., Tu, Y., & Tang, K. (2022). AI in online-learning research: Visualizing and interpreting the journal publications from 1997 to 2019. International Review of Research in Open and Distance Learning, 23(1), 104–130. https://doi.org/10.1917.
Jalagat, R. (2016). The impact of change and change management in achieving corporate goals and objectives: organizational perspective. Int. J. Sci. Res. 5 (November), 1233–1239. https://doi.org/10.21275/ART20163105.
Jayashree, S., Reza, M., Malarvizhi, C., & Mohiuddin, M. (2021). Industry 4.0 implementation and triple bottom line sustainability: an empirical study on small and medium manufacturing firms. Heliyon 7 (8), e07753. https://doi.org/10.1016/ j.heliyon.
Lin, Q., Huang, C., & Ko, C. (2020). The impact of perceived enjoyment on team effectiveness and individual learning in a blended learning business course: The mediating effect of knowledge sharing, Australas. J. Educ. Technol., vol. 36, pp. 126–141, Jan.
Lin, V., Yeh, H., & Chen, N. (2022). A systematic review on oral interactions in robot-assisted language learning. Electronics, 11(2), 290. https://doi.org/10.3390/ electronics11020290.
Mohammed, A., Algerafi, M., Yueliang, Z., Hind, A., & Tommy, T. (2023). Understanding the Factors Influencing Higher Education Students’ Intention to Adopt Artificial Intelligence-Based Robots. IEEE , 11 (5), 99752-99764.
Nawaz, S., & Mohamed, R. (2020). Acceptance of mobile learning by higher educational institutions in Sri Lanka: an UTAUT2 approach, Journal of Critical Reviews, Vol. 7 No. 12, pp. 1036-1049, doi: 10.31838/jcr.07.12.183.
Ozturk, A., Bilgihan, A., & Nusair, D. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience,’’ Int. J. Inf. Manag., vol. 36, no. 6, pp. 135.
Pal, D., Arpnikanondt, D., & Funilkul, S. (2021). The adoption analysis of voice-based smart IoT products, IEEE Inter- net Things J., vol. 7, no. 11, pp. 10852_10867, Nov. doi: 10.1109/JIOT.2020.2991791.
Rita, R., Mohammad, D., Subhodeep, M., Debajyoti, P., & Suree, S. (2022). Evaluating the Intention for the Adoption of Artificial Intelligence-Based Robots in the University to Educate the Students. IEEE , 10 (11), 1-13.
Salimon, M., Goronduste, H., & Abdullah, H. (2018). ‘User adoption of smart homes technology in Malaysia: Integration TAM 3, TPB, UTAUT 2 and extension of their constructs for a better prediction,’’ J. Bus. Manag., vol. 20, no. 4, pp. 60–69.
Sheshadri, C., & Kalyan, K. (2020). Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling. Education and Information Technologies , 8 (12), 1-21.
Shih-Ting, C., Gwo-Jen, B., & Yun-Fang, C. (2022). Artificial intelligence-based robots in education: A systematic review of selected SSCI publications. Computers and Education: Artificial Intelligence , 19 (12), 1-9.
Skjuve, M., Følstad, A., Fostervold, I., & Brandtzaeg, B. (2021). My chatbot companion_A study of human-chatbot relationships, Int. J. Hum.-Comput. Stud., vol. 149, no. 102601, doi: 10.1016/J.IJHCS.2021.102601.
Suddin, L., Brahim, C., Mohd, R., & Noor, F. (2023). Determining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Open Innovation: Technology, Market, and Complexity , 9 (6), 1-10.
Suhaili, S., Salim, N., & Jambli, N. (2021). Service chatbots: A systematic review, Expert Syst. Appl., vol. 184, Art. no. 115461, doi: 10.1016/J.ESWA.2021.115461.
Wang, X., Pang, H., Wallace, M., Wang, Q., & Chen, W. (2022). Learners’ perceived AI presences in AI-supported language learning: A study of AI as a humanized agent from community of inquiry. Computer Assisted Language Learning.https://doi.org/.
Wang, Y., Lin, H., & Liao, E. (2012). Investigating the individual difference antecedents of perceived enjoyment in students’ use of blogging, Brit. J. Educ. Technol., vol. 43, no. 1, pp. 139–152, Jan.
Yu, H., & Nazir, S. (2021). , Role of 5G and arti_cial intelligence for research and transformation of english situational teaching in higher studies, Mobile Inf. Syst., vol. pp. 1_16, Nov. 2021, doi: 10.1155/2021/3773414.
Zhang, J., Lai, E., & Xu, W. (2020). Motivating students' learning in science and technology by using robots,' Int. J. Innov. Learn., vol. 27, no. 4, pp. 395_410, doi: 10.1504/IJIL.2020.107620.