Integration of AI-Oriented Writing Concepts into the Teaching of Technical Writing to University Students

Abstract

The article investigates the ongoing transformation of technical writing practices driven by the rapid adoption of intelligent digital technologies that significantly reshape the creation, organization, and application of documentation. The expansion of generative language systems has redefined the role of technical texts, which are increasingly perceived not only as user-oriented instructional resources but also as structured inputs for automated analysis and algorithmic learning. This shift highlights the growing demand for specialists capable of operating at the intersection of linguistics, technical communication, and artificial intelligence, thereby underscoring the relevance of revising higher education curricula in technical writing. The purpose of the study is to provide a theoretical justification for integrating AI-oriented writing principles into university-level technical writing courses and to propose an updated instructional model tailored to contemporary technological contexts. To achieve this objective, the research employs a combination of methods, including analytical review and synthesis of scholarly literature on technical communication and education, comparative analysis of conventional and emerging pedagogical approaches, generalization of professional documentation practices, and structural-functional analysis of curriculum design and course components. The findings of the study involve the systematization of core requirements for producing content suitable for intelligent system interpretation, taking into account textual structure, semantic clarity, terminological consistency, and contextual completeness. The research further substantiates the inclusion of prompt engineering within technical writing education as a methodological tool for managing and optimizing interactions with artificial intelligence in both academic and professional settings. As a result, an enhanced course framework is proposed that integrates traditional documentation workflows with principles relevant to the preparation of texts for machine learning environments. The conclusions emphasize that embedding AI-focused writing methodologies into technical communication education is a necessary condition for preparing adaptable and competitive professionals. Such an approach enables graduates to function effectively in digitally mediated environments and to contribute to the development of accurate, reliable, and ethically grounded AI-driven knowledge infrastructures.