AI Mistakes in the Classroom
DOI:
https://doi.org/10.58886/jfi.v23i2.10179Keywords:
Artificial Intelligence, Connectivism, PedagogyAbstract
Large language models and their rapid, sweeping impact on education have only just begun to be documented in the teaching and learning literature. In quick succession, many publications on the acceptance and use of artificial intelligence in the classroom have appeared. The literature primary fell into two categories: (1) how-to guides for incorporating AI into the classroom (e.g. Mollick & Mollick, 2023; Noy & Zhang, 2023; Winkler & Ross, 2019) and (2) concerns about how it will be used for cheating (e.g. Amani, et al., 2023; Barros et al., 2023; Michel-Villarreal et al., 2023; Okaiyeto et al., 2023). While not explicitly stated in most articles, an underlying learning theory that strongly supports the inclusion of AI in the classroom is Connectivism. This theory can explain why AI in the classroom supports learning. However, what is currently missing from the literature are documented failures caused by incorporating AI into assignments and examples of how such inclusion has hindered learning. This paper attempts to begin to fill that gap. The following is a literature review showing how inclusion of AI supports the Connectivism view of learning, followed by three examples of mistakes when building AI based assignments and how they violated the Connectivist view of learning, leading to frustration by students and professors.
References
Alam, A. (2023). Connectivism learning theory and connectivist approach in teaching and learning: a review of literature. Bhartiyam International Journal Of Education & Research, 12(2).
Amani, S., White, L., Balart, T., Arora, L., Shryock, D. K. J., Brumbelow, D. K., & Watson, D. K. L. (2023). Generative AI Perceptions: A Survey to Measure the Perceptions of Faculty, Staff, and Students on Generative AI Tools in Academia (arXiv: 2304.14415). arXiv. URL: https://doi.org/10.48550/arXiv.2304.14415
Aryan, P. (2024). LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial Search for Adaptive Arguments. arXiv preprint arXiv:2412.06229. DOI: https://doi.org/10.1109/ICEI64305.2024.10912343
Barros, A., Prasad, A., & Śliwa, M. (2023). Generative artificial intelligence and academia: Implication for research, teaching and service. Management Learning, 54(5), 597-604. https://doi.org/10.1177/13505076231201445 DOI: https://doi.org/10.1177/13505076231201445
Bhatt, V., Gupta, D. B., & Chandra, G. (2024, September). Optimizing Classroom Teaching with AI-Based Adaptive Learning Tools. In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) (pp. 1-5). IEEE. https://doi.org/10.1109/acroset62108.2024.10743688 DOI: https://doi.org/10.1109/ACROSET62108.2024.10743688
Bray, R. L. (2024). A Tutorial on Teaching Data Analytics with Generative AI. arXiv preprint arXiv:2411.07244.
Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161-182. https://doi.org/10.1007/s10796-022-10291-4 DOI: https://doi.org/10.1007/s10796-022-10291-4
Correia, A., Água, P., & Conceição, V. (2024). AI in Education: A comparative study of rhizomatic and connectivism pedagogical theories. In INTED2024 Proceedings (pp. 4548-4555). IATED. https://doi.org/10.21125/inted.2024.1179 DOI: https://doi.org/10.21125/inted.2024.1179
Davvodi, A. (2025). Crafting innovative paths in non-linear professional learning for bilingual education: the role of connectivism in the age of AI. Professional development in education, 51(3), 434-450. https://doi.org/10.1080/19415257.2024.2421492 DOI: https://doi.org/10.1080/19415257.2024.2421492
De Jager, E., & Bitzer, E. (2013). First-year students’ participation and performance in a financial accounting support group. https://doi.org/10.19030/iber.v12i4.7739 DOI: https://doi.org/10.19030/iber.v12i4.7739
Downes, S. (2008). An introduction to connective knowledge. Media, Knowledge & Education, 77-102
Holtham, C. (2023). Deploying generative AI to draft a roleplay simulation of difficult conversations about inclusivity. Irish Journal of Technology Enhanced Learning, 7(2), 146-157. https://doi.org/10.22554/ijtel.v7i2.127 DOI: https://doi.org/10.22554/ijtel.v7i2.127
Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education Sciences, 13(9), 856. https://doi.org/10.3390/educsci13090856 DOI: https://doi.org/10.3390/educsci13090856
Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: Five strategies, including prompts. The Wharton School Research Paper. https://doi.org/10.2139/ssrn.4391243 DOI: https://doi.org/10.2139/ssrn.4391243
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. https://doi.org/10.2139/ssrn.4375283 DOI: https://doi.org/10.1126/science.adh2586
Okaiyeto, S. A., Bai, J., & Xiao, H. (2023). Generative AI in education: To embrace it or not?. International Journal of Agricultural and Biological Engineering, 16(3), 285-286. https://doi.org/10.25165/j.ijabe.20231603.8486 DOI: https://doi.org/10.25165/j.ijabe.20231603.8486
Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Nature Machine Intelligence, 3(12), 1013-1022. https://doi.org/10.1038/s42256-021-00417-9 DOI: https://doi.org/10.1038/s42256-021-00417-9
Pratama, M. P., Sampelolo, R., & Lura, H. (2023). Revolutionizing education: harnessing the power of artificial intelligence for personalized learning. Klasikal: Journal of education, language teaching and science, 5(2), 350-357. https://doi.org/10.52208/klasikal.v5i2.877 DOI: https://doi.org/10.52208/klasikal.v5i2.877
Roumeliotis, K. I., & Tselikas, N. D. (2023). Chatgpt and open-ai models: A preliminary review. Future Internet, 15(6), 192. https://doi.org/10.3390/fi15060192 DOI: https://doi.org/10.3390/fi15060192
Shyr, C., Grout, R. W., Kennedy, N., Akdas, Y., Tischbein, M., Milford, J., Tan, J., Quarles, K., Edwards, T., Novak, L., White, Jules., Wilkins, C. H., & Harris, P. A. (2024). Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency. Journal of the American Medical Informatics Association, 31(10), 2294-2303. https://doi.org/10.1093/jamia/ocae186 DOI: https://doi.org/10.1093/jamia/ocae186
Siemens, G. (2005). Connectivism: Learning as network-creation. ASTD Learning News, 10(1), 1-28.
Siemens, G. (2017). Connectivism. Foundations of learning and instructional design technology.
Upadhyay, A., Farahmand, E., Muñoz, I., Akber Khan, M., & Witte, N. (2024). Influence of LLMs on Learning and Teaching in Higher Education. Available at SSRN 4716855. https://doi.org/10.2139/ssrn.4716855 DOI: https://doi.org/10.2139/ssrn.4716855
Winkler, R., & Roos, J. (2019). Bringing AI into the classroom: Designing smart personal assistants as learning tutors.
Yang, Y., Shin, A., Kang, M., Kang, J., & Song, J. Y. (2024). Can We Delegate Learning to Automation?: A Comparative Study of LLM Chatbots, Search Engines, and Books. arXiv preprint arXiv:2410.01396.
