Prompt Engineering as a Key Competence in Education: Concept, Characteristics, and Assessment Approaches

In the context of the rapid development of Generative Artificial Intelligence (GenAI), prompt engineering is becoming a key competence for effective interaction with large language models in educational settings. However, the lack of a unified understanding of its nature, structure, and assessment tools complicates its integration into educational practice. The aim of this study is to systematize knowledge about prompt engineering as a key competence in education, identify its distinctive features, and explore approaches to its assessment. The study is based on a scoping review of more than 60 sources, including peer-reviewed journal articles, international conference proceedings, technology company documentation, and other materials. The search was conducted in databases such as Google Scholar, ERIC, СyberLeninka, and others for the period 2020–2025. The findings reveal that prompt engineering is an interdisciplinary competence that integrates knowledge, skills, and attitudes required for effective interaction with GenAI. The paper describes key prompt engineering techniques (Zero-shot, Few-shot, Chain-of-Thought, Tree-of-Thought, ReAct, Self-Consistency, etc.) and systematizes them along two dimensions: the level of prior information and the type of logical structure used in prompt construction. The evolution of prompt engineering is analyzed based on international and Russian research and practices. An operational model of the competence is proposed, grounded in the Bloom–Anderson taxonomy, with differentiation across six cognitive levels (remember, understand, apply, analyze, evaluate, and create) and three dimen-sions (knowledge, skills, attitudes).