Conference Agenda

Canada is globally recognized for its multicultural ethos, rooted in policies that embrace linguistic and cultural diversity. The nation’s unique language framework, including official bilingualism and the accommodation of Indigenous and immigrant languages, plays a pivotal role in shaping intercultural communication. These policies attempt to foster inclusion, bridge cultural gaps, and promote mutual respect, while also presenting challenges in ensuring equitable communication across diverse communities. Canada’s bilingual framework is enshrined in the Official Languages Act of 1969, which guarantees English and French as official languages, mandating their equal status in government and public services. This policy reflects the historical duality of its founding nations while promoting the coexistence of both linguistic groups. Additionally, Canada’s Multiculturalism Act (1988) underscores its commitment to preserving and enhancing the cultural and linguistic heritage of all citizens. Indigenous languages hold a special place in Canada’s linguistic landscape. The Indigenous Languages Act of 2019 seeks to revitalize and protect these languages, recognizing their central role in Indigenous cultures and their connection to reconciliation efforts.

Terminology plays a vital role in specialised communication, serving as a bridge between languages and cultures to facilitate mutual understanding and effective knowledge exchange. In today’s data-driven realities, terminology studies benefit from new methodological possibilities, enabling the integration of data-driven learning (DDL) approaches.

This study presents the rationale for using DDL approaches in terminology studies, along with the methodology behind them. The key benefit of the DDL approach is the enhancement of students’ autonomous learning skills. By exploring and managing language data, students become researchers independently generating knowledge through the systematisation of data and discovering trends and patterns across languages.

The proposed methodology of data-driven terminology studies includes a range of methods and software tools that help students develop various skills. Corpus-building methods enable students to deepen their knowledge of the domain and publicly available representative texts. Data extraction methods allow for the exploration of data from various perspectives, such as frequency, distribution, and collocational patterns. Establishing equivalent terms across languages, along with their conceptual structuring and definition, is key to fostering an understanding of knowledge structures within the domain and the impact of cultural context on their perception, which is essential in navigating linguistic and cultural differences across different languages. Finally, the compilation of a termbase promotes the principles of data structuring.

The proposed methodology has proven to enhance not only students’ terminology research skills but also deepen their domain knowledge and intercultural awareness, as well as develop data management skills that can be applied to other subjects.

The increasing global demand for homecare services, fueled by aging populations, chronic health conditions, and a preference for receiving care in the familiar comfort of home, underscores the vital role of non-professional caregivers (NP-C)—typically family members, friends, or informal support networks. Although these individuals lack formal healthcare training, they provide the majority of care in home environments, offering essential support with daily activities, medication management, personal care, and emotional assistance.

This research aims to explore and analyze the language utilized by NP-C in homecare settings, focusing on how they articulate their needs, challenges, and experiences.

A qualitative study was conducted involving 10 informants who have been providing care for an elderly individual. The interview data were transcribed and analyzed using NVivo software. The analysis of language expression is divided into four subcategories: degree of everyday language, degree of scientific terminology, imagery, and mood of language. Nursing content is examined through the following subcategories: identified problems, management of patient expectations, and the need for additional services.

The study demonstrated that the language used in communication can serve as a valuable approach to understanding the context of home care. It revealed that family members of patients who refrain from using scientific terminology tend to be more passive in their caregiving and are less motivated to explore diverse solutions to enhance their care. Conversely, caregivers who are acquainted with scientific language are generally better equipped to handle challenges, are more proactive in seeking external assistance, and are often more successful in finding help.

The hype around ChatGPT, and the speed at which it was adopted, put Generative AIs in the spotlight, with descriptions ranging from a fleeting mania or a technological marvel, to a looming threat and even a paradigm shift. In some cases, AIs were even framed in dystopian terms as “the vampire at our door” (Heckman 2023). Yet, this overlooks the fact that machines have long been integral to professional and scientific communication. From Computer Assisted Translation Tools (CAT tools) to machine translation, we have all been writing with machines for years. Machines have become an integral part of professional and scientific communication. The real question is not whether AIs should be used in scientific writing, but rather how to best leverage their potential. This paper explores the disruptions AIs bring to Language for Specific Purposes (LSP), particularly in the context of intercultural professional communication. It seeks to critically examine how these tools can reshape professional language practices and explores whether they have the potential to foster multicultural communication in global professional environments. It also aims to spark a broader conversation on the use of AIs in LSP education.