AI Application (ChatGPT) and Saudi Arabian Primary School Students’ Autonomy in Online Classes: Exploring Students and Teachers’ Perceptions
DOI:
https://doi.org/10.19173/irrodl.v25i3.7641Keywords:
AI-powered applications, ChatGPT, students' autonomy, online classes, students' perceptionsAbstract
In education, the integration of artificial intelligence (AI) has presented opportunities to transform the dynamics of online learning. This study investigated the impact of an AI-powered application, namely ChatGPT, on the autonomy of Saudi Arabian primary students participating in online classes. It also explored how the implementation of Chat GPT influenced Saudi Arabian primary students’ autonomy. In this mixed-methods study, a quasi-experimental design assessed the impact of ChatGPT on learner autonomy among 250 Saudi Arabian primary students from six primary schools in Riyadh, Saudi Arabia. The quantitative analysis employed descriptive statistics and t-tests, while the qualitative data underwent interpretative phenomenological analysis. To ensure coding reliability, 20% of the codes were independently reviewed by an external coder, with a 94% inter-coder agreement coefficient reached through consensus. Findings revealed that ChatGPT significantly affected the participants’ perceptions of autonomy and its different dimensions. Qualitative data showed that AI-powered applications contributed to the students’ autonomy in 10 different ways. Participants also mentioned that AI-powered apps might have some negative consequences. This study has theoretical implications for redefining learner autonomy in the digital age and calls for the exploration of many facets of autonomy. Practical applications from this study include strategic integration of AI into online education, data security, and the need for orientation programs.
References
Almusharraf, N. (2020). Teachers’ perspectives on promoting learner autonomy for vocabulary development: A case study. Cogent Education, 7(1), 1823154. https://doi.org/10.1080/2331186X.2020.1823154
Aslan, S., Alyuz, N., Tanriover, C., Mete, S. E., Okur, E., D’Mello, S. K., & Arslan Esme, A. (2019, May). Investigating the impact of a real-time, multimodal student engagement analytics technology in authentic classrooms. Proceedings of the 2019 Chi Conference on Human Factors in Computing Systems (pp. 1–12). https://dl.acm.org/doi/10.1145/3290605.3300534
Ayedoun, E., Hayashi, Y., & Seta, K. (2019). Adding communicative and affective strategies to an embodied conversational agent to enhance second language learners’ willingness to communicate. International Journal of Artificial Intelligence in Education, 29(1), 29–57. https://doi.org/10.1007/s40593-018-0171-6.
Bajaj, M. & Li, J. (2020, April 4). Students, faculty express concerns about online exam invigilation amidst COVID-19 outbreak. The Ubyssey. https://ubyssey.ca/news/Students-express-concerns-about-online-exams/
Beard, A. (2020, March 19). Can computers ever replace the classroom? The Guardian. https://www.theguardian.com/technology/2020/mar/19/can-computers-ever-replace the-classroom.
Benson, P. (2007). Autonomy in language teaching and learning. Language Teaching, 40(1), 21–40. https://doi.org/10.1017/ S0261444806003958
Bergmans, L., Bouali, N., Luttikhuis, M., & Rensink, A. (2021). On the efficacy of online Proctoring using Proctorio. In Proceedings of the 13th International Conference on Computer Supported Education - Volume 1. (pp. 279-290). https://doi.org/10.5220/0010399602790290
Borg, S., & Alshumaimeri, Y. (2019). Language learner autonomy in a tertiary context: Teachers’ beliefs and practices. Language Teaching Research, 23(1), 9–38. https://doi.org/10.1177/1362168817725759
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
Dang, T. T. (2010). Learner autonomy in EFL studies in Vietnam: A discussion from a sociocultural perspective. English Language Teaching, 3(2), 3–9. https://doi.org/10.5539/elt.v3n2p3
Dang, T. T. (2012). Learner autonomy: A synthesis of theory and practice. The Internet Journal of Language, Culture and Society, 35(1), 52–67. https://aaref.com.au/wp-content/uploads/2018/05/35-08.pdf
Dashtestani, R. (2020). Online English for academic purposes instruction in the context of Iran: Exploring the instructor element. Teaching English with Technology, 20(5), 23–37, http://cejsh.icm.edu.pl/cejsh/element/bwmeta1.element.desklight-f557ad7d-dbb6-4211-9307-1fa091f43998
Davis, N. L., Gough, M., & Taylor, L. L. (2019). Online teaching: Advantages, obstacles and tools for getting it right. Journal of Teaching in Travel & Tourism, 19(3), 256–263. https://doi.org/ 10.1080/15313220.2019.1612313
El Shazly, R. (2021). Effects of artificial intelligence on English speaking anxiety and speaking performance: A case study. Expert Systems, 38(3), e12667. https://doi.org/10.1111/exsy.12667
Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners. Computers in Human Behavior, 75, 461–468. https://doi.org/10.1016/j.chb.2017.05.045
Fryer, L. K., Coniam, D., & Lăpușneanu, D. (2018). Bots for language learning now: current and future directions. Language, Learning and Technology 24(2), 8-22. http://hdl.handle.net/10125/44719
Goel, A. K., & Polepeddi, L. (2018). Jill Watson: A Virtual Teaching Assistant for Online Education. In C. Dede, J. Richards, & B. Saxberg (Eds.), Learning engineering for online education (pp. 120–143). https://doi.org/10.4324/9781351186193-7
Heidicker, P., Langbehn, E., & Steinicke, F. (2017). Influence of avatar appearance on presence in social VR. 2017 IEEE Symposium on 3D User Interfaces (pp. 233–234). Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/3DUI.2017.7893357
Holstein, K., Hong, G., Tegene, M., McLaren, B. M., & Aleven, V. (2018). The classroom as a dashboard: Co-designing wearable cognitive augmentation for K-12 teachers. Proceedings of the Eighth International Conference on Learning Analytics and Knowledge (pp. 79–88). https://doi.org/10.1145/3170358.3170377
Huang, J.-X., Lee, K.-S., Kwon, O.-W., & Kim, Y.-K. (2017). A chatbot for a dialogue-based second language learning system. In K. Borthwick, L. Bradley & S. Thouësny (Eds.), CALL in a climate of change: Adapting to turbulent global conditions – short papers from EUROCALL 2017 (pp. 151–156). https://doi.org/10.14705/rpnet.2017.eurocall2017.705
Huang, J.-X., Kwon, O.-W., Lee, K.-S., & Kim, Y.-K. (2018). Improve the chatbot performance for the DB-CALL system using a hybrid method and a domain corpus. In P. Taalas, J. Jalkanen, L. Bradley & S. Thouësny (Eds.), Future-proof CALL: Language learning as exploration and encounters – short papers from EUROCALL 2018 (pp. 100–105). https://doi.org/10.14705/rpnet.2018.26.820
Hutapea, N. M. (2019). Improving senior high school students learning autonomy through generative learning. Journal of Educational Sciences, 3(1), 84–95. https://jes.ejournal.unri.ac.id/index.php/JES/article/view/6979/6184
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://www.sciencedirect.com/science/article/pii/S2666920X20300011
Kang, M., & Im, T. (2013). Factors of learner-instructor interaction which predict perceived learning outcomes in online learning environment. Journal of Computer Assisted Learning, 29(3), 292–301. https://doi.org/10.1111/jcal.12005
Khan, S., Zaman, S. I., & Rais, M. (2022). Measuring student satisfaction through overall quality at business schools: A structural equation modeling. South Asian Journal of Social Review 1(2), 34–55. https://doi.org/10.57044/sajsr.2022.1.2.2210
Kooli, C. (2023). Chatbots in education and research: A critical examination of ethical implications and solutions. Sustainability, 15(7), 5614. https://doi.org/10.3390/su15075614.
Lai, C. (2019). Learning beliefs and autonomous language learning with technology beyond the classroom, Language Awareness, 28(4), 1–19. https://doi.org/10.1080/09658416.2019.1675679
Little, D. (1996). Freedom to learn and compulsion to interact: Promoting learner autonomy through the use of information systems and information technologies. In R. Pemberton, S. L. Edward Li, W. F. Winnie, & H. D. Pierson (Eds.), Taking control: Autonomy in language learning (pp. 203-218). Hong Kong University Press.
Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behavior, 1(3), 1–28. https://doi.org/10.1038/s41562-016-0028
Murphy, K. P. (2019). Machine learning: A probabilistic perspective. The MIT Press. https://mitpress.mit.edu/9780262018029/machine-learning/
Perin, D., & Lauterbach, M. (2018). Assessing text-based writing of low-skilled college students. International Journal of Artificial Intelligence in Education, 28(1), 56–78. https://doi.org/10.1007/s40593-016-0122-z
Popenici, S. A., & 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), 22. https://doi.org/10.1186/s41039-017-0062-8
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
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(1), 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003
Richardson, J. W., Lingat, J. E. M., Hollis, E., College, R., & Pritchard, M. (2020). Shifting teaching and learning in online learning spaces: An investigation of a faculty online teaching and learning initiative. Online Learning Journal, 24(1), 67–91. https://doi.org/10. 24059/olj.v24i1.1629
Roll, I., Russell, D. M., & Gašević, D. (2018). Learning at scale. International Journal of Artificial Intelligence in Education, 28(4), 471–477. https://doi.org/10.1007/s40593-018-0170-7
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
Ross, B., Chase, A.-M., Robbie, D., Oates, G., & Absalom, Y. (2018). Adaptive quizzes to increase motivation, engagement and learning outcomes in a first year accounting unit. International Journal of Educational Technology in Higher Education, 15(1). https://doi.org/10.1186/s41239-018-0113-2
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
Rusmiyanto, R., Huriati, N., Fitriani, N., Kusumaning Tyas, N., Rofi’i, A., & Nurmalia Sari, M. (2023). The role of artificial intelligence (AI) in developing English language learner’s communication skills. Journal on Education, 6(1), 750–757. https://doi.org/10.31004/joe.v6i1.2990
Seo, K., Dodson, S., Harandi, N. M., Roberson, N., Fels, S., & Roll, I. (2021). Active learning with online video: The impact of learning context on engagement. Computers & Education, 165, 104–132. https://doi.org/10.1016/j.compedu.2021.104132
Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151(5), 103862. https://doi.org/10.1016/j.compedu.2020.103862
Tran, T. Q., & Duong, T. M. (2020). EFL learners’ perceptions of factors influencing learner autonomy development. Kasetsart Journal of Social Sciences, 41(1), 194–199. https://so04.tci-thaijo.org/index.php/kjss/article/view/231622
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
Wang, Y., Zhao, R., Li, Z., Wang, H., Yang, Q., & Yang, D. (2021). Building a dialogue agent for language learners. Proceedings of the 12th International Conference on Educational Data Mining (pp. 300–309).
Wogu, I. A. P., Misra, S., Olu-Owolabi, E. F., Assibong, P. A., Udoh, O. D., Ogiri, S. O., & Damasevicius, R. (2018). Artificial intelligence, artificial teachers and the fate of learners in the 21st century education sector: Implications for theory and practice. International Journal of Pure and Applied Mathematics, 119(16), 2245–2259. https://acadpubl.eu/hub/2018-119-16/2/232.pdf
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
Zhai, X. (2022, December 27). ChatGPT user experience: Implications for education. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4312418
Zhong, Q. M. (2018). The evolution of learner autonomy in online environments: A case study in a New Zealand context. Studies in Self-Access Learning Journal, 9, 71–85. https://doi.org/10.37237/090106
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