Abstracts
Abstract
Generative artificial intelligence (GenAI) has introduced a novel aspect to educational methodologies and sparked fresh dialogues regarding the creation and evaluation of instructional resources. This project seeks to investigate the impact of GenAI on the development and assessment of online course materials and learners’ engagement with these materials in the online learning environment. The study analyzed GenAI-generated multiple-choice questions, fill-in-the-blank exercises, and true-false activities during 3 weeks of a 14-week online course. Subject matter experts assessed these documents in regards to content, relevance, and clarity. Data was collected through an online form with open-ended questions. The interactions of learners with the GenAI-created learning activities were analyzed using log records of the learning management system and compared to the content provided by the course instructor regarding interaction levels. The study’s conclusions elucidate the capability of GenAI technologies to produce course-specific content and their efficacy in education. We stress that human specialists’ critical evaluations play a crucial part in improving the pedagogical validity of GenAI-powered learning materials. Further research into topics including the ethical dimension, the effect on academic achievement, and student motivation is recommended.
Keywords:
- Generative AI,
- online education,
- learner engagement,
- higher education,
- case study
Appendices
Bibliography
- Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in education and research: Opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965–2971. https://doi.org/10.1021/acs.jchemed.3c00323
- Ali, F., & OpenAI, Inc., C. (2023). Let the devil speak for itself: Should ChatGPT be allowed or banned in hospitality and tourism schools? Journal of Global Hospitality and Tourism, 2(1), 1–6. https://doi.org/10.5038/2771-5957.2.1.1016
- Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction. The International Review of Research in Open and Distributed Learning, 4(2). https://doi.org/10.19173/irrodl.v4i2.149
- Aydemir, H., & Seferoğlu, S. S. (2024). Investigation of users’ opinions, perceptions and attitudes towards the use of artificial intelligence in online learning environments. European Journal of Open, Distance and E-Learning, 26(1), 70–92. https://doi.org/10.2478/eurodl-2024-0007
- Bachiri, Y.-A., & Mouncif, H. (2023). Artificial intelligence system in aid of pedagogical engineering for knowledge assessment on MOOC platforms: Open EdX and Moodle. International Journal of Emerging Technologies in Learning (iJET), 18(05), 144–160. https://doi.org/10.3991/ijet.v18i05.36589
- Bachiri, Y.-A., Mouncif, H., & Bouikhalene, B. (2023). Artificial intelligence empowers gamification: Optimizing student engagement and learning outcomes in e-learning and MOOCs. International Journal of Engineering Pedagogy (iJEP), 13(8), 4–19. https://doi.org/10.3991/ijep.v13i8.40853
- Baidoo-anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500
- Bozkurt, A. (2023). ChatGPT, üretken yapay zekâ ve algoritmik paradigma değişikliği [ChatGPT, generative artificial intelligence and algorithmic paradigm shift]. Alanyazın 4(1), 63–72. https://doi.org/10.59320/alanyazin.1283282
- Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47. https://www.jstor.org/stable/48647028
- Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). Sage.
- DaCosta, B., & Kinsell, C. (2024). Investigating media selection through ChatGPT: An exploratory study on generative artificial intelligence in the aid of instructional design. Open Journal of Social Sciences, 12(4), 187–227. https://doi.org/10.4236/jss.2024.124014
- Ferrara, E. (2023). Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies. Sci, 6(1), Article 3. https://doi.org/10.3390/sci6010003
- Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6
- Guo, Z., Jing, T., Zhu, H., & Xu, X. (2023). Analysis of the impact of artificial intelligence on college students’ learning. The Frontiers of Society, Science and Technology, 5(13), 110–116. https://doi.org/10.25236/fsst.2023.051318
- Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2(4), Article 100089. https://doi.org/10.1016/j.tbench.2023.100089
- Huang, J. Shen, G., & Ren, X. (2021). Connotation analysis and paradigm shift of teaching design under artificial intelligence technology. International Journal of Emerging Technologies in Learning (iJET), 16(05), 73–86. https://doi.org/10.3991/ijet.v16i05.20287
- Jamilah, Jayadi, K., Yatim, H., Sahnir, N., Djirong, A., & Abduh, A. (2024). The integration of local cultural arts in the context of teaching materials on the implementation of the Merdeka Belajar curriculum. Journal of Education Research and Evaluation, 8(2), 404–413. https://doi.org/10.23887/jere.v8i2.78359
- Jouault, C., Seta, K., & Hayashi, Y. (2016). Content-dependent question generation using LOD for history learning in open learning space. New Generation Computing, 34(4), 367–394. https://doi.org/10.1007/s00354-016-0404-x
- Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
- Kır, Ş., & Şenocak, D. (2022). Açık ve uzaktan öğrenme sistemlerinde yapay zekânın öğrenen destek hizmeti bağlamında kullanımı [The use of artificial intelligence in the context of learner support services in open and distance learning systems]. Dijital Teknolojiler Ve Eğitim Dergisi, 1(1), 39–56. https://doi.org/10.5281/zenodo.6647642
- Kriangchaivech, K., & Wangperawong, A. (2019). Question generation by transformers. ArXiv. https://doi.org/10.48550/arxiv.1909.05017
- Lee , E. (2023). Is ChatGPT a false promise? Berkeley Blog. https://coilink.org/20.500.12592/hzdz6k
- Liang, Z., Zhao, Q., Zhou, Z., Yu, Q., Li, S., & Chen, S. (2020). The effect of “novelty input” and “novelty output” on boredom during home quarantine in the COVID-19 pandemic: The moderating effects of trait creativity. Frontiers in Psychology, 11, Article 601548. https://doi.org/10.3389/fpsyg.2020.601548
- Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), Article 100790. https://doi.org/10.1016/j.ijme.2023.100790
- Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39(1), 1–8. https://doi.org/10.14742/ajet.8695
- Nasution, N. E. A. (2023). Using artificial intelligence to create biology multiple choice questions for higher education. Agricultural and Environmental Education, 2(1), Article em002. https://doi.org/10.29333/agrenvedu/13071
- Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28, 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
- Sihite, M. R., Meisuri, M., & Sibarani, B. (2023). Examining the validity and reliability of ChatGPT 3.5-generated reading comprehension questions for academic texts. Randwick International of Education and Linguistics Science Journal, 4(4), 937–944. https://doi.org/10.47175/rielsj.v4i4.835
- Skanda, V. C., Jayaram, R., Bukitagar, V. C., & Kumar, N. S. (2020). Automatic questionnaire and interactive session generation from videos. In A. Chandrabose, U. Furbach, A. Ghosh, & M. A. Kumar (Eds.), IFIP advances in information and communication technology (Vol. 578; pp. 205–212). Springer. https://doi.org/10.1007/978-3-030-63467-4_16
- Sun, Z. (2010). Language teaching materials and learner motivation. Journal of Language Teaching and Research, 1(6), 889–892. https://doi.org/10.4304/jltr.1.6.889-892
- Sundari, W., Siahaan, S. M., & Ismet, I. (2023). Digital teaching materials based on attention relevance confidence satisfaction substance pressure material. Journal of Curriculum Indonesia, 6(1), 69–75. https://doi.org/10.46680/jci.v6i1.77
- Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
- Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), Article 15. https://doi.org/10.1186/s40561-023-00237-x
- Tran, S., Krishna, P., Pakuwal, I., Kafle, P., Singh, N., Lynch, J., & Drori, I. (2021). Solving machine learning problems. ArXiv. https://doi.org/10.48550/arxiv.2107.01238
- Wood, D., & Moss, S. H. (2024). Evaluating the impact of students’ generative AI use in educational contexts. Journal of Research in Innovative Teaching & Learning, 17(2), 152–167. https://doi.org/10.1108/jrit-06-2024-0151
- Xu, B., & Ismail, H. H. (2024). The impact of artificial intelligence-assisted learning applications on oral English ability: A literature review. International Journal of Academic Research in Progressive Education and Development, 13(4), 1118–1134. https://doi.org/10.6007/ijarped/v13-i4/23352
- Yin, R. K. (2014). Case study research design and methods. Sage Publications.
- Zabala-Vargas, S., García-Mora, L. H., Arciniegas-Hernández, E., Reina-Medrano, J. I., de Benito-Crosetti, B., & Darder-Mesquida, A. (2021). Strengthening motivation in the mathematical engineering teaching processes—A proposal from gamification and game-based learning. International Journal of Emerging Technologies in Learning (iJET), 16(06), 4–19. https://doi.org/10.3991/ijet.v16i06.16163
- 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, Article 39. https://doi.org/10.1186/s41239-019-0171-0