Prompt Engineering as a 21st Century Learning Competency: A Conceptual Framework for Science and Mathematics Education
* Corresponding author: aishatuabubakar@jossme.org.ng
Abstract
Artificial Intelligence (AI), particularly generative AI powered by Large Language Models (LLMs), is reshaping science and mathematics education. While prior studies have emphasized AI literacy, digital competence, and teacher preparedness, comparatively little attention has been given to the learner competencies required for effective interaction with AI through prompt engineering. This conceptual paper positions prompt engineering as a twenty-first-century learning competency that fosters inquiry, reasoning, critical thinking, communication, and self-regulated learning. Using a conceptual research design, the study synthesizes contemporary literature and educational theories to develop a learner-centred perspective on prompt engineering. Four complementary theories constructivist learning, self-regulated learning, cognitive load theory, and inquiry-based learning provide the foundation for two proposed frameworks. The Prompt Engineering Learning Competency (PELC) Framework identifies six domains essential for AI-supported learning, while the Prompt Engineering Competency Model (PECM) explains learners' progression from novice users to expert prompt engineers. The paper argues that prompt engineering should be recognized as an educational competency rather than merely a technical skill, with implications for curriculum design, classroom practice, teacher education, learner assessment, and future research in science and mathematics education.
Keywords
References
- Bruner, J. S. (1960). The process of education. Harvard University Press.
- Dede, C. (2010). Comparing frameworks for 21st-century skills. In J. Bellanca & R. Brandt (Eds.), 21st century skills: Rethinking how students learn (pp. 51–76). Solution Tree Press.
- Federiakin, D., Molerov, D., Zlatkin-Troitschanskaia, O., & Maur, A. (2024). Prompt engineering as a new 21st-century skill. Frontiers in Education, 9, Article 1366434. https://doi.org/10.3389/feduc.2024.1366434
- Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
- International Society for Technology in Education. (2024). Artificial intelligence in education: Educator guidance. ISTE.
- Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0
- Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeiffer, F., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
- Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall.
- Lee, D., & Palmer, E. (2025). Prompt engineering in higher education: A systematic review to help inform curricula. International Journal of Educational Technology in Higher Education, 22, Article 7. https://doi.org/10.1186/s41239-025-00503-7
- Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
- Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press.
- National Council of Teachers of Mathematics. (2023). Artificial intelligence and mathematics education. NCTM.
- National Science Teaching Association. (2024). Artificial intelligence for science education. NSTA.
- Organisation for Economic Co-operation and Development. (2024). OECD digital education outlook 2024: Towards equitable and responsible AI in education. OECD Publishing.
- Qian, Y. (2025). Prompt engineering in education: A systematic review of approaches and educational applications. Journal of Educational Computing Research, 63(7–8), 1782–1818. https://doi.org/10.1177/0735633125136518
- Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in Science Education, 36(1–2), 111–139. https://doi.org/10.1007/s11165-005-3917-8
- Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog12024
- UNESCO. (2024). Artificial intelligence competency frameworks for teachers and learners. UNESCO.
- Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
- Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press.
Abubakar, A. (2026). Prompt Engineering as a 21st Century Learning Competency: A Conceptual Framework for Science and Mathematics Education. Journal of Studies in Science and Mathematics Education, 6(1), 65–72. https://doi.org/10.67203/jossme.2026.blbmyc3j
A. Abubakar, "Prompt Engineering as a 21st Century Learning Competency: A Conceptual Framework for Science and Mathematics Education," Journal of Studies in Science and Mathematics Education, vol. 6, no. 1, pp. 65–72, August 2026. doi: 10.67203/jossme.2026.blbmyc3j