Prompt Engineering as a 21st Century Learning Competency: A Conceptual Framework for Science and Mathematics Education

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.