Generative AI Prompt Engineering for Educators: Practical Strategies

Abstract

Generative AI, such as ChatGPT, produces personalized and contextually relevant content based on user prompts (inputs provided by users). These prompts act as the primary form of interaction between users and AI models, making their quality essential for generating the most relevant outputs. The process of writing, refining, and optimizing prompts, known as prompt engineering, is key to obtaining high-quality desired outputs from generative AI. For educators, proficiency in prompt engineering is crucial for effective interaction with AI as it enhances efficiency and produces the most relevant information. In this paper, we introduce practical strategies for prompt engineering for educators: (a) include essential components, including Persona, Aim, Recipients, Theme, and Structure (PARTS); (b) develop prompts using Concise, Logical, Explicit, Adaptive, and Restrictive (CLEAR) languages; (c) evaluate output and refine prompts: Rephrase key words, Experiment with context and examples, Feedback loop, Inquiry questions, Navigate by iterations, Evaluate and verify outputs (REFINE); and (d) apply with accountability. Examples for special educators and online resources are included.

Keywords

prompt engineering artificial intelligence generative AI

Get full access to this article

View all access options for this article.

References

Amatriain

(2024). Prompt design and engineering: Introduction and advanced methods. arXiv: 2401.14423. https://doi.org/10.48550/arXiv.2401.14423

Amershi

Cakmak

Knox

W. B.

Kulesza

(2014). Power to the people: The role of humans in interactive machine learning. AI Magazine, 35(4), 105–120. https://doi.org/10.1609/aimag.v35i4.2513

Cain

(2024). Prompting change: Exploring prompt engineering in large language model AI and its potential to transform education. TechTrends, 68(1), 47–57. https://doi.org/10.1007/s11528-023-00896-0

Celik

Dindar

Muukkonen

Järvelä

(2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y

Center for Digital Thriving . (2024). Teen and young Adult perspectives on generative AI patterns of use. excitements, and concerns. Harvard University. https://www.commonsensemedia.org/sites/default/files/research/report/teen-and-young-adult-perspectives-on-generative-ai.pdf

Chang

E. Y.

(2023). Prompting large language models with the socratic method. In 2023 IEEE 13th Annual computing and Communication Workshop and Conference (CCWC) pp. (351–360). https://doi.org/10.1109/CCWC57344.2023.10099179

Chen

Lin

(2020). Artificial intelligence in education: A review. IEEE Access, 8(1), 75264–75278. https://doi.org/10.1109/access.2020.2988510, https://ieeexplore.ieee.org/document/9069875

Council of the Great City Schools and Consortium for School Networking . (2023). K-12 generative AI readiness checklist. Council of the Great City Schools. https://www.cgcs.org/genaichecklist.

Coy

(2023). Opinion | A.I. could actually be a boon to education. The New York Times. Retrieved: https://www.nytimes.com/2023/05/03/opinion/chatgpt-ai-khan-academy.html (3 May 2023).

10.

Dwivedi

Y. K.

Kshetri

Hughes

Slade

E. L.

Jeyaraj

Kar

A. K.

Baabdullah

A. M.

Koohang

Raghavan

Ahuja

Albanna

Albashrawi

M. A.

Al-Busaidi

A. S.

Balakrishnan

Barlette

Basu

Bose

Brooks

Buhalis

Wright

(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(1), 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

11.

Eager

Brunton

(2023). Prompting higher education towards AI-augmented teaching and learning Practice. Journal of University Teaching and Learning Practice, 20(5), 1–19. https://doi.org/10.53761/1.20.5.02

12.

Ekin

(2023). Prompt engineering for ChatGPT: A quick guide to techniques, tips, and best practices. TechRxiv. https://doi.org/10.36227/techrxiv.22683919

13.

Future of Artificial Intelligence Innovation Act of 2024, S.4178, 118th Cong. (2024). https://www.govinfo.gov/app/details/BILLS-118s4178is.

14.

Garg

Sharma

(2020). Impact of artificial intelligence in special need education to promote inclusive pedagogy. International Journal of Information and Education Technology, 10(7), 523–527. https://doi.org/10.18178/ijiet.2020.10.7.1418

15.

Goldman

S. R.

Taylor

Carreon

Smith

S. J.

(2024). Using AI to support special education teacher workload. Journal of Special Education Technology, 39(3), 434–447. https://doi.org/10.1177/01626434241257240

16.

Google for Education . (2024a). Responsibility checklist. Mountain View, CA: Google LLC. https://drive.google.com/file/d/1wnp4wlmfi9S84-H3fgV60gsFdb9QSVjh/view.

17.

Google for Educators . (2024b). Generative. Mountain View, CA: AI for educators. Google LLC. https://skillshop.exceedlms.com/student/path/1176018.

18.

Guan

Mou

Jiang

(2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134–147. https://doi.org/10.1016/j.ijis.2020.09.001

19.

Harwell

(2023). Tech’s hottest new job: AI whisperer. No coding required. Washington Post. https://www.washingtonpost.com/technology/2023/02/25/prompt-engineers-techs-next-big-job/

20.

Hwang

G. J.

(2003). A conceptual map model for developing intelligent tutoring systems. Computers & Education, 40(3), 217–235. https://doi.org/10.1016/S0360-1315(02)00121-5

21.

Kohli

Phutela

Garg

Viner

(2021). Artificial intelligence technology to help students with disabilities: Promises and implications for teaching and learning. In Singh

Yeh

C. J.

Blanchard

Anunciação

(Eds.), Handbook of research on critical issues in special education for school rehabilitation practices (pp. 238–255). Information Science Reference/IGI Global. https://doi.org/10.4018/978-1-7998-7630-4.ch013

22.

L. S.

(2023a). The art and science of prompt engineering: A new literacy in the information age. Internet Reference Services Quarterly, 27(4), 203–210. https://doi.org/10.1080/10875301.2023.2227621

23.

L. S.

(2023b). 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

24.

Luckin

Cukurova

(2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824–2838. https://doi.org/10.1111/bjet.12861

25.

McCarthy

K. S.

Crossley

S. A.

Meyers

Boser

Allen

L. K.

Chaudhri

V. K.

Collins-Thompson

D’Mello

De Choudhury

Garg

Goel

Gosha

Heffernan

Hooper

M. A.

Hyman

Jarratt

D. C.

Khalil

Kizilcec

R. F.

Litman

Zampieri

(2022). Toward more effective and equitable learning: Identifying barriers and solutions for the future of online education. Technology, Mind, and Behavior, 3(1), 78. https://doi.org/10.1037/tmb0000063

26.

McIntosh

T. R.

Liu

Susnjak

Watters

Halgamuge

M. N.

(2023). A culturally sensitive test to evaluate nuanced GPT hallucination. IEEE Transactions on Artificial Intelligence, 5(6). https://doi.org/10.1109/TAI.2023.3332837

27.

Mercado

Varela

Mercado

(2024). OpenAI Statistics 2024: Funding, users, adoption, & impact. Skillademia. https://skillademia.com/statsitics/openai-statistics/.

28.

Next Move Strategy Consulting . (2023). Artificial Intelligence (AI) Market. NMSC. https://www.nextmsc.com/report/artificial-intelligence-market.

29.

Reynolds

McDonell

(2021). Prompt programming for large language models: Beyond the few-shot paradigm. arXiv:2102.07350. https://doi.org/10.48550/arXiv.2102.07350

30.

Ross

(1987). Intelligent tutoring systems. Journal of Computer Assisted Learning, 3(4), 194–203. https://doi.org/10.1111/j.1365-2729.1987.tb00331.x

31.

Sleeman

D. H.

Brown

J. S.

(1979). Editorial: Intelligent tutoring systems. International Journal of Man-Machine Studies, 11(1), 1–3. https://doi.org/10.1016/s0020-7373(79)80002-4

32.

United Nations Educational, Scientific and Cultural Organization . (2023). Guidance for generative AI in education and research. UNESCO. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research.

33.

U.S. Department of Education, Office of Educational Technology . (2023). Artificial Intelligence and Future of Teaching and Learning: Insights and Recommendations. U.S. Department of Education. https://www2.ed.gov/documents/ai-report/ai-report.pdf.

34.

Velásquez-Henao

J. D.

Franco-Cardona

C. J.

Cadavid-Higuita

(2023). Prompt engineering: A methodology for optimizing interactions with AI-language models in the field of engineering. Dyna, 90(230), 9–17. https://doi.org/10.15446/dyna.v90n230.111700

35.

Walter

(2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1), 15. https://doi.org/10.1186/s41239-024-00448-3

36.

Wang

Yang

Cai

Yin

(2024). Unleashing ChatGPT’s power: A case study on optimizing information retrieval in flipped classrooms via prompt engineering. IEEE Transactions on Learning Technologies, 17(1), 629–641. https://doi.org/10.1109/TLT.2023.3324714

37.

Waterfield

D. A.

Watson

Day

(2024). Applying artificial intelligence in special education: Exploring availability and functionality of AI platforms for special educators. Journal of Special Education Technology, 39(3), 448–454. https://doi.org/10.1177/01626434241257237

38.

Weizenbaum

(1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168

39.

White

Hays

Sandborn

Olea

Gilbert

Elnashar

Spencer-Smith

Schmidt

D. C.

(2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv: 2302.11382. https://doi.org/10.48550/arXiv.2302.11382

40.

Williams

D. L.

Boone

Kingsley

K. V.

(2004). Teacher beliefs about educational software: A Delphi study. Journal of Research on Technology in Education, 36(3), 213–229. https://doi.org/10.1080/15391523.2004.10782413

41.

Zhou

Muresanu

A. I.

Han

Paster

Pitis

Chan

(2023). Large language models are human-level prompt engineers. arXiv: 211.01910. https://doi.org/10.48550/arXiv.2211.01910