Abstract
This paper explores the transformative potential of Large Language Models Artificial Intelligence (LLM AI) in educational contexts, particularly focusing on the innovative practice of prompt engineering. Prompt engineering, characterized by three essential components of content knowledge, critical thinking, and iterative design, emerges as a key mechanism to access the transformative capabilities of LLM AI in the learning process. This paper charts the evolving trajectory of LLM AI as a tool poised to reshape educational practices and assumptions. In particular, this paper breaks down the potential of prompt engineering practices to enhance learning by fostering personalized, engaging, and equitable educational experiences. The paper underscores how the natural language capabilities of LLM AI tools can help students and educators transition from passive recipients to active co-creators of their learning experiences. Critical thinking skills, particularly information literacy, media literacy, and digital citizenship, are identified as crucial for using LLM AI tools effectively and responsibly. Looking forward, the paper advocates for continued research to validate the benefits of prompt engineering practices across diverse learning contexts while simultaneously promoting potential defects, biases, and ethical concerns related to LLM AI use in education. It calls upon practitioners to explore and train educational stakeholders in best practices around prompt engineering for LLM AI, fostering progress towards a more engaging and equitable educational future.
Similar content being viewed by others
References
Abdous, M. (2023). How AI is shaping the future of higher ed. Retrieved April 4, 2023, from https://www.insidehighered.com/views/2023/03/22/how-ai-shaping-future-higher-ed-opinion
Attiah, K. (2023). Opinion | For writers, AI is like a performance-enhancing steroid. Retrieved January 13, 2023, from https://www.washingtonpost.com/opinions/2023/01/13/ai-writers-performance-enhancing-steroid/
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(1), 42, s41239–020-00218–x. https://doi.org/10.1186/s41239-020-00218-x
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922.
Bowman, E. (2022). A new AI chatbot might do your homework for you. But it’s still not an A+ student. NPR. Retrieved January 18, 2023, from https://www.npr.org/2022/12/19/1143912956/chatgpt-ai-chatbot-homework-academia
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. Retrieved May 31, 2023, from https://papers.nips.cc/paper_files/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
Cain, W. (2023a). GPTeammate: A design fiction on the use of variants of the GPT language model as cognitive partners for active learning in higher education. In Society for Information Technology & Teacher Education International Conference (pp. 1293–1298). Association for the Advancement of Computing in Education (AACE).
Cain, W. (2023b). Supporting AI-enhanced active online learning experiences: A framework for design and assessment. In T. Martindale, T. B. Amankwatia, L. D. Cifuentes, & A. A. Piña (Eds.), Handbook of research in online learning. Brill.
Cardona, M. A., Rodríguez, R. J., & Ishmael, K. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. Retrieved May 28, 2023, from U.S. Department of Education, Office of Educational Technology. https://www2.ed.gov/documents/ai-report/ai-report.pdf
Carvalho, L., Martinez-Maldonado, R., Tsai, Y.-S., Markauskaite, L., & De Laat, M. (2022). How can we design for learning in an AI world? Computers and Education: Artificial Intelligence, 3, 100053. https://doi.org/10.1016/j.caeai.2022.100053
Chomsky, N., Roberts, I., & Watumull, J. (2023). Opinion | Noam Chomsky: The false promise of ChatGPT. The New York Times. Retrieved April 21, 2023, from https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html
Coy, P. (2023). Opinion | A.I. could actually be a boon to education. The New York Times. Retrieved May 3, 2023, from https://www.nytimes.com/2023/05/03/opinion/chatgpt-ai-khan-academy.html
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642.
Ekin, S. (2023). Prompt engineering for ChatGPT: A quick guide to techniques, tips, And best practices TechRxiv. https://doi.org/10.36227/techrxiv.22683919.v2
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
Fourtané, S. (2022). Artificial intelligence in higher education: Benefits and ethics. Fierce Education. Retrieved June 3, 2023, from https://www.fierceeducation.com/technology/artificial-intelligence-higher-education-benefits-and-ethics
Harwell, D. (2023). Tech’s hottest new job: AI whisperer. No coding required. Washington Post. Retrieved February 26, 2023, from https://www.washingtonpost.com/technology/2023/02/25/prompt-engineers-techs-next-big-job/
Heaven, W. (2023). ChatGPT is going to change education, not destroy it. MIT Technology Review. Retrieved April 0, 2023, from https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2023/04/06/1071059/chatgpt-change-not-destroy-education-openai/amp/
Heilweil, R. (2022). AI is finally good at stuff. Now what? Vox. Retrieved June 3, 2023, from https://www.vox.com/recode/2022/12/7/23498694/ai-artificial-intelligence-chat-gpt-openai
Ji, H., Han, I., & Ko, Y. (2022). A systematic review of conversational AI in language education: Focusing on the collaboration with human teachers. Journal of Research on Technology in Education, 0(0), 1–16. https://doi.org/10.1080/15391523.2022.2142873
Jiang, J., Karran, A. J., Coursaris, C. K., Léger, P.-M., & Beringer, J. (2022). A situation awareness perspective on human-AI interaction: Tensions and opportunities. International Journal of Human–Computer Interaction, 1–18.
Kelley, K. J. (2023). Teaching actual student writing in an AI world. Inside Higher Ed. Retrieved January 19, 2023, from https://www.insidehighered.com/advice/2023/01/19/ways-prevent-students-using-ai-tools-their-classes-opinion
Kim, J., Lee, H., & Cho, Y. H. (2022). Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27(5), 6069–6104. https://doi.org/10.1007/s10639-021-10831-6
Kovanovic, V. (2023). The dawn of AI has come, and its implications for education couldn’t be more significant. The Conversation. Retrieved January 10, 2023, from http://theconversation.com/the-dawn-of-ai-has-come-and-its-implications-for-education-couldnt-be-more-significant-196383
Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship, 49(4), 102720. https://doi.org/10.1016/j.acalib.2023.102720
Mollick, E. (2023). How to... Use ChatGPT to boost your writing [Substack newsletter]. One Useful Thing. Retrieved June 3, 2023, from https://oneusefulthing.substack.com/p/how-to-use-chatgpt-to-boost-your
Mollick, E. R., & Mollick, L. (2022). New modes of learning enabled by AI chatbots: Three methods and assignments. (SSRN Scholarly Paper 4300783). https://doi.org/10.2139/ssrn.4300783
Moore, R. L., Jiang, S., & Abramowitz, B. (2022). What would the matrix do?: A systematic review of K-12 AI learning contexts and learner-interface interactions. Journal of Research on Technology in Education, 1–14. https://doi.org/10.1080/15391523.2022.2148785
Nouhan, C., Scott, N., & Womack, J. (2021). Emergent role of artificial intelligence in higher education. IEEE Technology Policy and Ethics, 6(3), 1–5. https://doi.org/10.1109/NTPE.2021.9778094
OpenAI. (2023). GPT-4 system card. OpenAI.
OpenAI. (n.d.). GPT Best Practices. OpenAI. Retrieved June 12, 2023, from https://platform.openai.com
Ortiz, S. (2023). This class requires ChatGPT usage, and the results are surprising. ZDNET. Retrieved February 21, 2023, from https://www.zdnet.com/article/this-class-requires-chatgpt-usage-the-results-are-surprising/
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer (arXiv:1910.10683). arXiv. Retrieved June 14, 2023, from http://arxiv.org/abs/1910.106833
Ramer, L. (2023). Adapt, evolve, elevate: ChatGPT is calling for interdisciplinary action. Times Higher Education. Retrieved February 22, 2023, from https://www.timeshighereducation.com/campus/adapt-evolve-elevate-chatgpt-calling-interdisciplinary-action
Reynolds, L., & McDonell, K. (2021). Prompt programming for large language models: Beyond the few-shot paradigm (arXiv:2102.07350). arXiv. Retrieved March 5, 2023, from http://arxiv.org/abs/2102.07350
Shapiro, L. (2023). Opinion | Why I’m not worried about my students using ChatGPT. Washington Post. Retrieved February 6, 2023, from https://www.washingtonpost.com/opinions/2023/02/06/college-students-professor-concerns-chatgpt/
Short, C. E., & Short, J. C. (2023). The artificially intelligent entrepreneur: ChatGPT, prompt engineering, and entrepreneurial rhetoric creation. Journal of Business Venturing Insights, 19, e00388. https://doi.org/10.1016/j.jbvi.2023.e00388
Srinivasan, V. (2022). AI & learning: A preferred future. Computers and Education: Artificial Intelligence, 3, 100062. https://doi.org/10.1016/j.caeai.2022.100062
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks (arXiv:1409.3215). arXiv. Retrieved June 14, 2023, from http://arxiv.org/abs/1409.3215
Terry, O. K. (2023). Opinion | I’m a student. You have no idea how much we’re using ChatGPT. The Chronicle of Higher Education. Retrieved May 17, 2023, from https://www.chronicle.com/article/im-a-student-you-have-no-idea-how-much-were-using-chatgpt
Thongprasit, J., & Wannapiroon, P. (2022). Framework of artificial intelligence learning platform for education. International Education Studies, 15(1), 76. https://doi.org/10.5539/ies.v15n1p76
Webb, M. E., Fluck, A., Magenheim, J., Malyn-Smith, J., Waters, J., Deschênes, M., & Zagami, J. (2021). Machine learning for human learners: Opportunities, issues, tensions and threats. Educational Technology Research and Development, 69, 2109–2130.
Weise, K., & Metz, C. (2023). When A.I. chatbots hallucinate. The New York Times. Retrieved May 3, 2023, from https://www.nytimes.com/2023/05/01/business/ai-chatbots-hallucinatation.html
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT (arXiv:2302.11382). arXiv. https://doi.org/10.48550/arXiv.2302.11382
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
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2023). Large language models are human-level prompt engineers (arXiv:2211.01910). arXiv. https://doi.org/10.48550/arXiv.2211.01910
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Research Involving Human Participants and/or Animals
This research did not involve either human participants and/or animals.
Informed Consent
This research did not contain any studies involving animal or human participants. No requirement for consent to publish was required.
Conflict of Interest
I declare I have no potential conflicts of interest in relation to the publication of the manuscript.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Cain, W. Prompting Change: Exploring Prompt Engineering in Large Language Model AI and Its Potential to Transform Education. TechTrends 68, 47–57 (2024). https://doi.org/10.1007/s11528-023-00896-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11528-023-00896-0