Prompt engineering as a new component of information and communication competence for prospective translators and engineers

Abstract

This paper examines prompt engineering - the deliberate design of textual inputs to elicit targeted, high-quality outputs large language model (LLM)-based systems - as an emergent and structurally significant component of the information and communication competence (ICC) profile required of prospective translators and engineers. Drawing on a review of Scopus- and Web of Science-indexed scholarship and the DigComp framework, the study argues that the capacity to construct, iterate, and critically evaluate prompts constitutes a distinct digital competence that intersects with, yet exceeds, established information literacy and AI literacy frameworks. For translation students, prompt engineering is directly relevant to quality-assured engagement with neural machine translation systems and LLM-assisted terminology work; for engineering students, it underpins AI-assisted technical problem-solving, code generation, and documentation automation. The paper maps the domain-specific dimensions of this competence and proposes its integration into curricula.