Abstract This article analyses a debate published in the journal The Builder in 1851, in which subject field experts discussed nomenclatures for English medieval architecture. The debate illustrates how the principles of multidimensionality and terminologisation are instantiated in term formation, and describes the role played by the subjective motivations of experts in this process. Among these motivations were the authors’ personal aspirations to be recognised within their scientific community, the economic interests of publishers, and the authors’ idiosyncratic positions against possible updates to traditional terms. On a methodological level, the article highlights the importance of studying term formation as an evolving process in a diachronic perspective, focusing not just on terms as final products, as traditionally done in terminology theory, but on the dynamics which shaped them. Among the factors involved in this process, this study places particular emphasis on human agency, which played a decisive role in shaping its outcomes.
Abstract The article investigates the media-driven determinologization process of military terminology using a two-phase, corpus-assisted design: (1) analysis of Kazakh- and Russian-language online news reports (2019–2025), used to validate and classify the non-military senses that military terms acquire in public discourse; and (2) an experimental definition-task comparing the perception and interpretation of specifically selected military terms by students with and without military training backgrounds ( n = 40 and n = 33). We distinguish determinologization by counting a non-military interpretation as evidence only when it corresponds to a sense already attested for that term in the corpus. Responses were scored with a five-component semantic-decomposition rubric, with inter-rater reliability reported for both term classification (Cohen’s κ = 0.730) and response scoring (ICC = 0.97). Mann-Whitney U-tests indicate significant differences between the two focus groups for 13 of 20 military terms, with effect sizes aligning with training exposure. Experimental results and findings show consistent group asymmetries, with military-trained students favoring dictionary-fixed definitions for the terms and “civilian” specialty students privileging semantically broadened (determinologized) interpretations. Implications are suggested for programs training translation and interpreting specialists, media studies and journalism syllabuses, and media service personnel.
Abstract Generative AI is likely to transform terminology work by creating new opportunities for automation. At the same time, it raises concerns about the future of terminologists and terminological resources, as efficiency pressures may encourage excessive automation based on the perception that human expertise can be replaced by AI. However, large language models remain unreliable for terminological purposes due to errors, hallucinations, and various forms of bias, making terminologists indispensable for ensuring the accuracy and reliability of terminological data. This paper argues that human-centered AI, an approach that emphasizes that AI’s primary goal should be to contribute to human well-being, provides a framework for maximizing the benefits of generative AI while mitigating its risks. It contends that high levels of automation and meaningful human control are compatible and desirable, and that AI should enhance terminologists’ capabilities while preserving their agency and decision-making authority. The implications of AI-assisted terminology work are examined through three interrelated dimensions: the augmented terminologist, ethical AI, and human-centered design. In particular, the paper addresses how AI integration reshapes the role of the terminologist, affects professional values and working conditions, requires the management of AI-generated bias, and calls for the design of AI tools around the terminologist’s needs. The paper concludes that a human-centered orientation is necessary to ensure that AI strengthens, rather than undermines, the essential role of terminology work in supporting specialized communication and the accurate transmission of knowledge across languages and cultures.
Abstract Responding to the growing recognition that academic vocabulary is discipline-sensitive, this study develops and validates an Intercultural Communication Academic Word List (ICAWL). Owing to the lexical complexity and interdisciplinary scope of IC scholarship, general academic word lists may not adequately capture its vocabulary demands. A purpose-built corpus of 1,000 Scopus-indexed IC research articles published from 2025 onwards (9,046,688 tokens) was compiled. Word selection followed a multi-stage procedure incorporating frequency, range, dispersion, lexical profiling, keyword analysis against the British National Corpus, concordance screening, lemmatisation, and expert judgement. Two independent validation corpora — research articles (826,031 tokens) and books (1,557,040 tokens) — were used to assess coverage. The final ICAWL comprises 556 lemmas (704 word types) and achieves approximately 16 per cent coverage across corpora, substantially exceeding the Academic Word List (6–7%). The findings confirm the discipline-specific nature of IC vocabulary and provide a principled resource for curriculum design and corpus-informed pedagogy.
Abstract In academic and institutional contexts, the management and structuring of specialised knowledge are essential to support effective communication and internationalisation. Traditionally, ontology development and ontology-driven terminology work have relied on expert-based manual processes and corpus analysis techniques. However, recent advances in generative artificial intelligence (GenAI) and large language models (LLMs) have opened new possibilities for supporting knowledge representation tasks. In this context, this study explores the use of a customised GenAI-based chatbot for assisting ontology development in the domain of European higher education. Specifically, The Ontology Assistant, built on ChatGPT-5.2 and enriched with a domain-specific corpus, was designed to support the identification and structuring of ontological categories within the EuroScholar Ontology (EuSO). The model’s performance was evaluated through six experimental runs using a gold standard derived from a selected ontological category. Precision, recall, and F1-measure were used to assess category retrieval. The results show consistent behaviour across runs, characterised by high recall and low precision, reflecting the exploratory capacity of the chatbot to identify candidate ontological categories. These findings suggest that GenAI chatbots can function as useful support tools within ontology development workflows, provided that expert intervention is applied to verify and maintain alignment with the EuSO.
