Integrating Artificial Intelligence in the Higher Education of Technical Writers and Technical Translators

Compared to translation and interpreting studies, which have already had a long-standing tradition, technical writing studies only emerged in the mid-1990s. At that time, so-called translator workbenches, which include several support functions for translators, became state-of-the-art tools. In addition, machine translation (MT) systems were put into operation and led to a change in translator’s daily activities. For example, additional tasks like pre-editing the original texts and post-editing the machine-translated versions have become routine. In addition to actual translation competence, knowledge about how to evaluate and optimise texts, etc., became necessary. Although the original texts to be translated by translators are normally written by technical writers and although the two professions share many common features (Schubert 2007: 103), translators often know very little about the actual work processes of technical writers and vice versa. Now, with the rapidly growing potential influence of generative artificial intelligence (AI) systems, which are mostly in the form of large language models (LLMs), both professions have to come to terms with the fact that many of their daily activities can now potentially be (semi-)automated through these technologies. These tasks include, among other things, information research, text analysis, text production, correction/editing, terminology work, and ofcourse translation. This article argues for the integration of generative AI in the higher education of both technical writers and technical translators, since it is highly likely that LLMs and other generative AI competences will not only be desired but explicitly requested by potential future employers and clients. Therefore, this article will discuss several examples of what and how universities could teach aspiring technical writers and translators so that they become experts in language-oriented AI.