Résumés
Abstract
This research study explored the perspectives of 20 educational technologists from four Saudi Arabian universities regarding the integration of AI-powered technology, particularly ChatGPT, into online higher education. The study used a qualitative research method that relied on the principles of theoretical sampling to select participants and conducted in-depth interviews to collect their insights. The approach taken for data analysis was thematic analysis, which uncovered a rich range of insights on both the challenges and opportunities associated with students’ use of AI-integrated technology in the context of online higher education. Ten significant challenges emerged that shed light on the complexities and intricacies of integrating AI-powered technology into educational environments. These challenges included issues related to technological infrastructure, pedagogical adaptation, and the need for comprehensive training programs to empower both teachers and learners. Additionally, eight threats were examined that highlighted concerns about data security, privacy, and potential risks associated with AI technology in educational institutions. This study not only provided a comprehensive overview of the current landscape of AI-integrated technology in Saudi Arabian higher education, but also provided valuable insights for education stakeholders, technologists, and policy makers. It underscored the necessity of proactive measures to mitigate challenges and threats while harnessing the opportunities presented by AI technology to enhance the quality and effectiveness of online higher education.
Keywords:
- AI-integrated technology,
- ChatGPT,
- higher education,
- online learning,
- threats,
- challenges,
- education technologists
Veuillez télécharger l’article en PDF pour le lire.
Télécharger
Parties annexes
Bibliography
- Ahmad, M. A., Teredesai, A., & Eckert, C. (2020). Fairness, accountability, transparency in AI at scale: Lessons from national programs. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 690–690). https://doi.org/10.1145/3351095.3375690
- Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-Tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted
- Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Huang, B. (2020, August). Factors influencing students’ behavioral intention to continue artificial intelligence learning. 2020 International Symposium on Educational Technology (pp. 147–150). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISET49818.2020.00040
- Chai, C. S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinesesecondary school students’ intention to learn. Artificial Intelligence: Mathematics, 8(11), 2089. https://doi.org/10.3390/math8112089
- Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
- De Graaf, M. M., & Allouch, S. B. (2013). Exploring influencing variables for the acceptance of social robots. Robotics and Autonomous Systems, 61(12). https://doi.org/10.1016/j.robot.2013.07.007
- Dennis, A. R., Fuller, R. M., & Valacich, J. S. (2008). Media, tasks, and communication processes: A theory of media synchronicity. Management Information Systems Quarterly,32(2), 575–600. https://doi.org/10.2307/25148857
- Dignum, V. (2021). The role and challenges of education for responsible AI. London Review of Education. 19(1), 1–11. https://doi.org/10.14324/LRE.19.1.01
- Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. Proceedings of FabLearn 2019 (pp. 104–111). https://doi.org/10.1145/3311890.3311904
- Fourtané, S. (2020, August 27). Ethics of AI: Benefits and risks of artificial intelligence systems. Interesting Engineering. https://interestingengineering.com/innovation/ethics-of-ai-benefits-and-risks-of-artificial-intelligence-systems
- Frenzel, A. C., Goetz, T., Lüdtke, O., Pekrun, R., & Sutton, R. E. (2009). Emotional transmission in the classroom: Exploring the relationship between teacher and student enjoyment. Journal of Educational Psychology, 101(3), 705–716. https://doi.org/10.1037/a0014695
- Ghallab, M. (2019). Responsible AI: Requirements and challenges. AI Perspectives, 1(1), 1–7. https://doi.org/10.1186/s42467-019-0003-z
- Gonzalez, C., & Kasper, G. M. (1997). Animation in user interfaces designed for decision support systems: The effects of image abstraction, transition, and interactivity on decision quality. Decision Sciences, 28(4), 793–823. https://doi.org/10.1111/j.1540-5915.1997.tb01332.x
- Holstein, K., McLaren, B. M., & Aleven, V. (2018). Student learning benefits of a mixed reality teacher awareness tool in AI-enhanced classrooms. International Conference on Artificial Intelligence in Education (pp. 154–168). Springer. https://doi.org/10.1007/978-3 319-93843-1_12
- Howlin, C., & Lynch, D. (2014, November). A framework for the delivery of personalized adaptive content. In 2014 International Conference on Web and Open Access to Learning (pp. 1–5). Institute of Electrical and Electronics Engineers. doi:10.1109/ICWOAL.2014.7009203
- Karnouskos, S. (2022). Symbiosis with artificial intelligence via the prism of law, robots, and society. Artificial Intelligence Law, 30(1), 93–115. https://doi.org/10.1007/s10506-021-09289-1
- Kasper, G. M. (1996). A theory of decision support system design for user calibration. Information Systems Research, 7(2), 215–232. https://doi.org/10.1287/isre.7.2.215
- Kentnor, H. E. (2015). Distance education and the evolution of online learning in the United States. Curriculum and Teaching Dialogue, 17(1), 21–34. https://digitalcommons.du.edu/law_facpub/24/
- Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: The Betty’s Brain system. International Journal of Artificial Intelligence in Education, 18(3), 181–208. https://content.iospress.com/articles/international-journal-of-artificial-intelligence-in-education/jai18-3-02
- López-Fernández, H., Fdez-Riverola, F., Reboiro-Jato, M., Glez-Peña, D., & Méndez, J. R. (2011). Using CBR as design methodology for developing adaptable decision support systems. In J. Chiang (Ed.), Efficient decision support systems: Practice and challenges from current to future (pp. 124–144). IntechOpen. https://doi.org/10.5772/16923
- Martin, F., Sun, T., & Westine, C. D. (2020). A systematic review of research on online teaching and learning from 2009 to 2018. Computers & Education, 159(4), 104009. https://doi.org/10.1016/j.compedu.2020.104009
- Mayer, R. E. (2014). Cognitive theory of multimedia learning. In The Cambridge handbook of multimedia learning (2nd ed.). Cambridge University Press.
- Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267(1), 1–38. https://doi.org/10.1016/j.artint.2018.07.007
- Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
- Mullikin, J. (2020). Focusing on feedback in distance learning. Edutopia. Retrieved August 29, 2022, from https://www.edutopia.org/article/focusing-feedback-distancelearning
- Neumann, M. M., & Herodotou, C. (2020). Evaluating YouTube videos for young children. Education and Information Technologies, 25(4), 4459–4475. https://doi.org/10.1007/s10639-020-10183-7
- Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2), 1-8. https://doi.org/10.7189/jogh.08.020303
- Pelaez, A., Jacobson, A., Trias, K., & Winston, E. (2022). The Turing teacher: Identifying core attributes for AI learning in K–12. Frontier Artificial Intelligence. 5(1), 1031450. https://doi.org/10.3389/frai.2022.1031450
- Phillips-Wren, G. (2012). AI tools in decision making support systems: A review. International Journal of Artificial Intelligence Tools, 21(2). https://doi.org/10.1142/S0218213012400052
- Reed, D. E., Kaplita, E. C., McKenzie, D. A., & Jones, R. A. (2022). Student experiences and changing science interest when transitioning from K–12 to college. Education Sciences, 12(7), 496. https://doi.org/10.3390/educsci12070496
- Roper, A. R. (2007). How students develop online learning skills. EDUCAUSE Quarterly, 30(1), 62–65. https://er.educause.edu/articles/2007/2/how-students-develop-online-learning-skills
- Samuel, J. (2021). A call for proactive policies for informatics and artificial intelligence technologies. Scholars Strategy Network. Retrieved October 2, 2022, from https://scholars.org/contribution/call-proactive-policies-informatics-and
- Samuel, J., Kashyap, R., Samuel, Y., & Pelaez, A. (2022). Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations. International Journal of Management, 65(4), 102505. https://doi.org/10.1016/j.ijinfomgt.2022.102505
- Samuel, J., Navazali, G. G., Rahman, M., Esawi, E., & Samuel, Y. (2020). COVID-19 public sentiment insights and machine learning for Tweets classification. Information, 11(6), 314–330. https://doi.org/10.3390/info11060314
- Sankar, C. S., Ford, F. N., & Bauer, M. (1995). A DSS user interface model to provide consistency and adaptability. Decision Support Systems,13(1), 93–104. https://doi.org/10.1016/0167-9236(93)E0033-A
- Turban, E. (1995). Decision support and expert systems management support systems. Prentice-Hall.
- Ulfert, A. S., Antoni, C. H., & Ellwart, T. (2022). The role of agent autonomy in using decision support systems at work. Computers in Human Behavior, 126, 106987. https://doi.org/10.1016/j.chb.2021.106987
- Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
- Wilson, L., & Gruzd, A. (2014). MOOCs: International information and education phenomenon? Bulletin of the American Society for Information Science and Technology, 40(5), 35–40. https://doi.org/10.1002/bult.2014.1720400510