Keywords
educational philosophy
learning
academic writing
Categories
How to Cite
Abstract
Rapid developments in the field of Generative AI have caused businesses, educators and politicians to consider how best to accommodate and utilise these new technologies. This article explores the implications of using AI, particularly Large Language Models (LLMs), in the writing process. While accepting that artificial intelligence has many valid and valuable uses in other spheres of human existence, this article argues that using LLMs at any stage of the academic writing process beyond grammar-checking is detrimental to student learning. This article explains the importance of students mastering each stage of the Basic Model of Learning to Write Well (learning, thinking, writing), identifies higher-order thinking as the key objective of education and reminds readers of why learning is one of the most joyful activities a human can experience.
References
An, J., Huang, D., Lin, C., & Tai, M. (2024). Measuring Gender and Racial Biases in Large Language Models (arXiv:2403.15281). arXiv. https://doi.org/10.48550/arXiv.2403.15281
Ash, I. K., Jee, B. D., & Wiley, J. (2012). Investigating insight as sudden learning. The Journal of Problem Solving, 4(2). https://digitalcommons.odu.edu/psychology_fac_pubs/63/
Bellovin, S. M. (2024). DeGenerative AI? IEEE Security & Privacy, 22(03), 88–88. https://doi.org/10.1109/MSEC.2024.3385549
Bennis, W. G., & Thomas, R. J. (2002). Crucibles of leadership. Harvard Business Review, 80.
Beretta, E., Cavaterra, C., Fornoni, M., Lorenzo, G., & Rocca, E. (2024). Iterative algorithms for the reconstruction of early states of prostate cancer growth (arXiv:2409.12844). arXiv. https://doi.org/10.48550/arXiv.2409.12844
Bloom, B., Englehart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York, NY: David McKay.
Bonde, J. P. E., Jensen, J. H., Smid, G. E., Flachs, E. M., Elklit, A., Mors, O., & Videbech, P. (2022). Time course of symptoms in posttraumatic stress disorder with delayed expression: A systematic review. Acta Psychiatrica Scandinavica, 145(2), 116–131. https://doi.org/10.1111/acps.13372
Chatfield, T. (2022). Critical Thinking: Your Guide to Effective Argument, Successful Analysis and Independent Study (2nd Edition). London: Sage
Ding, Y., & Shi, T. (2024). Sustainable LLM Serving: Environmental Implications, Challenges, and Opportunities?: Invited Paper. 2024 IEEE 15th International Green and Sustainable Computing Conference (IGSC), 37–38. https://doi.org/10.1109/IGSC64514.2024.00016
Dixon, S. (2023). Don’t panic (yet): The implications of ChatGPT for Education Studies in the UK. Educational Futures, 14(1), 96–116. https://educationstudies.org.uk/?p=20864
Dolmans, D. H. J. M., Loyens, S. M. M., Marcq, H., & Gijbels, D. (2016). Deep and surface learning in problem-based learning: A review of the literature. Advances in Health Sciences Education, 21(5), 1087–1112. https://doi.org/10.1007/s10459-015-9645-6
He, S., Leanse, L. G., & Feng, Y. (2021). Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases. Advanced Drug Delivery Reviews, 178, 113922. https://doi.org/10.1016/j.addr.2021.113922
Kirchhübel, C., & Brown, G. (2024). Intellectual property rights at the training, development and generation stages of Large Language Models. In I. Siegert & K. Choukri (Eds.), Proceedings of the Workshop on Legal and Ethical Issues in Human Language Technologies @ LREC-COLING 2024 (pp. 13–18). ELRA and ICCL. https://aclanthology.org/2024.legal-1.3/
Lee, H.-P., Sarkar, A., Tanklevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025, May 26). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. in CHI Conference on Human Factors in Computing Systems (CHI ’25), Yokohama, Japan. Retrieved February 10, 2025, from https://hankhplee.com/papers/genai_critical_thinking.pdf
Lee, W. J., Wu, H., Yun, H., Kim, H., Jun, M. B. G., & Sutherland, J. W. (2019). Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data. Procedia CIRP, 80, 506–511. https://doi.org/10.1016/j.procir.2018.12.019
Li, J., Consul, S., Zhou, E., Wong, J., Farooqui, N., Ye, Y., Manohar, N., Wei, Z., Wu, T., Echols, B., Zhou, S., & Diamos, G. (2024). Banishing LLM Hallucinations Requires Rethinking Generalization (arXiv:2406.17642). arXiv. https://doi.org/10.48550/arXiv.2406.17642
Linaza, M. T., Posada, J., Bund, J., Eisert, P., Quartulli, M., Döllner, J., Pagani, A., G. Olaizola, I., Barriguinha, A., Moysiadis, T., & Lucat, L. (2021). Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture. Agronomy, 11(6), Article 6. https://doi.org/10.3390/agronomy11061227
Paul, R., & Elder, L. (2019). The Thinker’s Guide to Socratic Questioning: Based on critical thinking concepts and tools. London: Rowman & Littlefield.
Plant, K. L., & Stanton, N. A. (2013). The explanatory power of Schema Theory: Theoretical foundations and future applications in Ergonomics. Ergonomics, 56(1), 1–15. https://doi.org/10.1080/00140139.2012.736542
Shi, X., Liu, J., Liu, Y., Cheng, Q., & Lu, W. (2025). Know where to go: Make LLM a relevant, responsible, and trustworthy searchers. Decision Support Systems, 188, 114354. https://doi.org/10.1016/j.dss.2024.114354
Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631(8022), 755–759. https://doi.org/10.1038/s41586-024-07566-y
Ward, B., Bhati, D., Neha, F., & Guercio, A. (2024). Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance (arXiv:2412.02166). arXiv. https://doi.org/10.48550/arXiv.2412.02166
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Ian Fellows