Language Skills & Translation Tools for Adults

The Issue

Proficiency in one of Canada’s official languages (English and French) shapes access to education, employment, and community life. However, many adults, particularly immigrants and newcomers, face persistent barriers to language acquisition. In Quebec, French predominates as the primary language with 74.8% of the population speaking French as their mother tongue and 93.7% speaking French in some capacity. Yet there is a significant English-speaking population in the province as well: 7.6% of residents speak English as a mother tongue, and around half of the province (51.7%) can speak English. Additionally, nearly half of the province (46.4%) can speak both French and English (Statistics Canada, 2025). Given this linguistic diversity, this project addresses how machine translation tools (e.g., Google Translate, Bing Translator, DeepL, ChatGPT, etc.) can support French and English language acquisition among different language groups in Quebec.

What We Investigated

This project examines the use, trust, and perceptions of machine translation (MT) tools among adults in Quebec to support language learning. The project includes a literature review that provides foundational context on MT technologies, including neural machine translation systems and large language models, along with potential benefits and challenges. The project also examines the use of MT tools in Quebec by analyzing the results of an online survey of 1,000 people conducted by the Association for Canadian Studies in September 2023. The survey holds critical importance as it provides a major assessment of MT use among Quebec’s linguistically diverse population amid rapid AI advancements in language learning.

What We’re Learning

Overall, the results of the survey provide key insights about the use of machine translation (MT) among Canadian-born individuals and immigrants, and among Francophones, Anglophones, and Allophones:

• Respondents born outside of Canada were more likely to use translation devices than those born in Canada (60% vs. 51%).
• Respondents born outside of Canada were more likely than those born in Canada to believe that MT will reduce language barriers between language groups (65% vs. 51%).
• Overall, 80% of survey participants believed that MT improved their second language knowledge. All five groups surveyed indicated similar levels of belief (77-83%).
• The most common use of MT among all respondents was for assimilation of information (59%), followed by dissemination of information (41%), and translation-mediated interaction (24%). This order of preference was the same for respondents born in Canada and outside of Canada, Francophones, and Anglophones. However, Allophones were more likely to use MT for dissemination of information (48%) than assimilation of information (44%).
• All groups of respondents trusted humans more than MT devices to conduct translations. However, almost one-half (47%) of survey respondents trusted humans and MT devices equally.
• Respondents born outside of Canada were more likely than respondents born in Canada to trust MT for entertainment purposes (74% vs. 70%), in a workplace setting (75% vs. 64%), in a legal setting (49% vs. 37%), and in a healthcare setting (57% vs. 49%).
• Allophones were more likely than Francophones and Anglophones to trust MT for entertainment purposes, and in legal and healthcare settings. However, Anglophones were more likely to trust MT in the workplace. Francophones, Anglophones, and Allophones all had similar levels of trust in MT for educational purposes.
• Respondents born outside of Canada were much more likely than respondents born in Canada to use MT to translate from English to a language other than French, from a language other than French to English, and a language other than English to French. The same held true for Allophones compared to Francophones and Anglophones.
• Among immigrants, those reporting a good ability to speak English were more likely than those reporting a very good ability to speak English to use translation devices with automated machine learning (79% vs. 56%), to use an MT device for translation-mediated interaction (39% vs. 8%), and believe that an MT device can help improve second language knowledge (87% vs. 80%). However, immigrants reporting a very good ability to speak English were more likely to believe that MT will reduce language barriers between language groups (70% vs. 62%).

Why It Matters

Canada is investing heavily into language training programs through federally funded programs like Language Instruction for Newcomers to Canada (LINC) and Cours de langue pour les immigrants au Canada (CLIC). However, access to language training is often limited, particularly in rural and remote communities with underdeveloped service capacity and broadband infrastructure. Moreover, many individuals experience barriers to language learning related to high-speed internet connectivity, digital literacy, transportation, inflexible work schedules, and caregiving responsibilities. In this context, there are opportunities to improve the design and delivery of language training programs by leveraging technologies like machine translation (MT) and other AI-powered tools.

Survey findings have important implications for policy makers, funders, language training providers, settlement organizations, employers, and technology developers who are shaping how adults, particularly newcomers and linguistic minorities, access language training in Quebec and the rest of Canada. In current practice, survey results show that MT tools are deeply embedded in how Quebecers navigate work, education, healthcare, and legal systems, where bilingualism often intensifies linguistic demands.

State of Skills:
What Works for Newcomer Integration

Despite the overall success of Canada’s immigration system, a number of challenges persist. When compared to other nations, labour market mobility for newcomers in Canada is not as strong as other dimensions of migrant integration.

Read Thematic Report

MT must therefore not simply be viewed as an emerging technology but rather as a critical tool for many language learners in the province. This highlights the need for policies that support technology-enhanced language learning and incorporate clear guidance on the ethical and appropriate use of MT and other AI-powered tools. Organizations and governments that fail to recognize how language learners use technology risk designing services that do not reflect lived experiences. On the other hand, those that integrate evidence-based use of technology can improve language learning outcomes. Rapid advancements in MT and other AI-powered tools are reshaping language learning, and policy must evolve accordingly to reflect real-world language practices.

What’s Next

Based on the research and analysis presented in this report, we offer several recommendations for better incorporating machine translation (MT) into Canadian language training programs.

• Encourage integration of MT into language training programs, using it as a supplementary tool rather than a replacement for direct instruction or human translation.
• Develop targeted training for both instructors and learners on the effective and ethical use of MT tools, focusing on strengths, limitations, and risks.
• Educate users in workplace, legal, and healthcare settings about potential reliability concerns associated with MT.
• Support the development and adoption of MT that caters to the diverse linguistic backgrounds of Canadian immigrants, including Allophones.
• Encourage partnerships between government agencies, tech companies, and settlement services to share best practices and ensure MT solutions are responsive to learner needs.
• Conduct further research and regular program evaluations to monitor MT effectiveness, learner outcomes, and user satisfaction.