Prompting Change: Exploring Prompt Engineering in Large Language Model AI and Its Potential to Transform Education
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Cain sets out how training in prompting helps students and educators transition from passive recipients to active co-creators of their learning experiences.
“Cain [70] sets out how training in prompting helps students and educators transition from passive recipients to active co-creators of their learning experiences.”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as Cain [70]
Cites
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · cited 1× · see citing works
“…generation, positioning it as a promising new tool for learning and instruction in educational contexts (Brown et al., 2020; Cain, 2023a; Cardona et al., 2023; Carvalho et al., 2022; Heaven, 2023; Kovanovic, 2023; Moore et al., 2022; OpenAI, 2023; Srinivasan, 2022; Zawacki-Richter et al., 2019).”
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 4× · see citing works
“The “language model” part of the name refers to their purpose: to model, or understand, human language in a way that allows them to generate coherent and contextually appropriate responses (Bender et al., 2021).”
InfluentialDwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584 · see citing works
A Unified Framework of Five Principles for AI in Society
Floridi, L., & Cowls, J. (2019) · Harvard Data Science Review · doi:10.1162/99608f92.8cd550d1 · Cited by 1,631 · cited 1× · see citing works
“…contexts with diverse learning and instructional applications, signifying a transformative but as yet ill-researched potential for traditional education practices (Abdous, 2023; Brown et al., 2020; Coy, 2023; Dwivedi et al., 2023; Floridi & Cowls, 2019; Thongprasit & Wanna-piroon, 2022).”
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Reynolds, L., & McDonell, K. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451760 · Cited by 952 · cited 1× · see citing works
“Yet, arriving at the ideal prompt often isn’t straightforward; it frequently requires an iterative approach, as supported by findings from Brown et al. (2020) and Reynolds and McDonell (2021).”
AI technologies for education: Recent research & future directions
Zhang, K., & Aslan, A. B. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100025 · Cited by 923 · see citing works
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023) · arXiv · doi:10.48550/arxiv.2302.11382 · Cited by 838 · see citing works
Kim, J., Lee, H., & Cho, Y. H. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10831-6 · Cited by 669 · cited 2× · see citing works
“Thus, LLM AI’s natural language capabilities can champion personalized learning dynamics, where students remain at the helm of their learning trajectory, maintaining steady engagement and pacing (Kim et al., 2022).”
Can artificial intelligence transform higher education?
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-00218-x · Cited by 490 · see citing works
Ji, H., Han, I., & Ko, Y. (2023) · Journal of Research on Technology in Education · doi:10.1080/15391523.2022.2142873 · Cited by 394 · see citing works
The CLEAR path: A framework for enhancing information literacy through prompt engineering
Lo, L. S. (2023) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2023.102720 · Cited by 347 · see citing works
Large Language Models Are Human-Level Prompt Engineers
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022) · arXiv · doi:10.48550/arxiv.2211.01910 · Cited by 330 · see citing works
Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices
Ekin, S. (2026) · ASEE Journal of Engineering Technology · doi:10.18260/jet.43.1.5 · Cited by 257 · see citing works
Short, C. E., & Short, J. C. (2023) · Journal of Business Venturing Insights · doi:10.1016/j.jbvi.2023.e00388 · Cited by 235 · see citing works
New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments
Mollick, E. R., & Mollick, L. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4300783 · Cited by 176 · cited 1× · see citing works
“An envisioned shift from conventional, didactic teaching methods to more interactive, student-centered learning experiences could result in more engaging teaching and the development of higher-order cognitive skills (Mollick & Mollick, 2022).”
How can we design for learning in an AI world?
Carvalho, L., Martinez-Maldonado, R., Tsai, Y.-S., Markauskaite, L., & De Laat, M. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100053 · Cited by 128 · see citing works
A Situation Awareness Perspective on Human-AI Interaction: Tensions and Opportunities
Jiang, J., Karran, A. J., Coursaris, C. K., Léger, P., & Beringer, J. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2093863 · Cited by 123 · see citing works
Machine Learning for Human Learners: Opportunities, Issues, Tensions and Threats
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Prompt Engineering For ChatGPT: A Quick Guide To Techniques, Tips, And Best PracticesPreprint
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AI & learning: A preferred future
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Framework of Artificial Intelligence Learning Platform for Education
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Handbook of Research in Online Learning
Martindale, T., Amankwatia, T. B., Cıfuentes, L., et al. (2024) · BRILL eBooks · doi:10.1163/9789004702813 · Cited by 12 · see citing works
The dawn of AI has come, and its implications for education couldn’t be more significant
Kovanović, V. (2022) · doi:10.64628/aa.5rhn6xpju · Cited by 11 · see citing works
Emergent Role of Artificial Intelligence in Higher Education
Nouhan, C., Scott, N., & Womack, J. E. (2021) · IEEE Technology Policy and Ethics · doi:10.1109/ntpe.2021.9778094 · Cited by 2 · see citing works
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Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295 · see citing works
“Cain [70] sets out how training in prompting helps students and educators transition from passive recipients to active co-creators of their learning experiences.”
Moorhouse, B. L., Wan, Y., Wu, C., Kohnke, L., Ho, T. Y., & Kwong, T. (2024) · System · doi:10.1016/j.system.2024.103399 · Cited by 228 · see citing works
Marzano, D. (2026) · Technology, Knowledge and Learning · doi:10.1007/s10758-025-09853-7 · Cited by 51 · see citing works
Large language models meet user interfaces: The case of provisioning feedback
Pozdniakov, S., Brazil, J., Abdi, S., Bakharia, A., Sadiq, S., Gašević, D., Denny, P., & Khosravi, H. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100289 · Cited by 43 · see citing works
Eutsler, L., Rivera, B., Barnes, M., & Cummings, J. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2025.2476589 · Cited by 16 · see citing works
The influence of generative AI with prompt engineering on creative design in architectural education
Lee, S., & Kang, S. (2026) · Journal of Asian Architecture and Building Engineering · doi:10.1080/13467581.2025.2552446 · Cited by 13 · see citing works
Li, X., Li, T., Wang, M., Tao, S., Zhou, X., Wei, X., & Guan, N. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3539104 · Cited by 8 · see citing works
- Authors
- Cain, W.
- Affiliations
- University of Wyoming (US)
- Corresponding author
- Cain, William
- ORCID
- Cain, William
- Venue
- TechTrends
- Published
- 2023-10-18
- Volume
- 68
- Issue
- 1
- Pages
- 47-57
- ISSN
- 1559-7075 (ISSN-L)
- 8756-3894
- OpenAlex
- W4387736592
- Semantic Scholar
- a0a3d3349497782861ecc436c3807bfe1a7d90a1
- Cited by
- 232
- References
- 25
- Citations by year
- 2026: 94 · 2025: 100 · 2024: 37 · 2023: 1
- FWCI
- 54.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- TechTrends (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- prompt engineering, large language models, artificial intelligence in education, critical thinking, information literacy, media literacy, digital citizenship, personalized learning, educational equity, AI ethics
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