About the Conference

How do generative algorithmic environments affect processes of linguistic socialization, discursive agency, and identity formation in contemporary linguistic and cultural educational contexts? 

This conference focuses specifically on contexts of language education, including the teaching of first, second, or additional languages, academic literacy, and the didactics of language and discourse, in majority, minority, and marginalized contexts. It aims to examine how generative artificial environments are transforming language teaching and learning practices and processes, as well as the dynamics of agency, identity, and linguistic socialization among learners and instructors. 

The rapid proliferation of generative artificial intelligence systems in schools and universities is transforming language learning, discourse practices, modes of assessment, and the conditions under which knowledge is produced. While early debates focused primarily on normative and policy concerns (e.g., academic integrity, regulation, permitted uses), recent research has increasingly turned to the cognitive, identity-related, and sociopolitical implications of these technologies.

In applied linguistics and educational sciences, language learning has long been understood not merely as the acquisition of linguistic structures, but as embedded in processes of socialization, interaction, and identity formation (Maccoby, 2014; Maturana, 1975; Bandura, 2001; Loewen & Sato, 2017; Vygotsky et al., 1967; Gee, 2012; Norton, 2014; Norton & Toohey, 2011). These processes are shaped by situational and material conditions and are central to language and cultural education (Atkinson, 2011; van Lier, 2010; Long, 2016; Ellis et al., 2019). In second language contexts, they are reflected in proficiency frameworks ranging from basic communicative acts to complex argumentative discourse (Piccardo et al., 2019; Jezak, 2017).

With the emergence of large language models (LLMs), these dimensions are being reconfigured within digital and algorithmic environments. Generative AI affects cognition, emotion, linguistic formulation, argumentative structuring, and the authorization of knowledge (Defays, 2018; El Bahlouli, 2024; Chapelle, 2024; Kurt & Kurt, 2024; Huettig & Christiansen, 2024). While some studies highlight positive effects on vocabulary development, grammatical acquisition, and learner engagement (Xu, Yu & Liu, 2025; Chen et al., 2025; Dai & Wu, 2025; Levy, 2024), and even on phenomena of anthropomorphization (Chen & Yi, 2024), others point to risks related to cognitive delegation and transformations in forms of engagement (Barcaui, 2025; Zhai et al., 2024), as well as potential cognitive decline associated with prolonged use (MIT research; Kosmyna et al., 2025).

At the sociolinguistic level, human–AI interactions constitute spaces where identity, agency, and power are negotiated (Darvin, 2025; Lim & Darvin, 2026). Unequal access to digital resources shapes learners’ agency and their capacity for critical engagement, while prompts and system outputs may index specific ideologies and influence ways of thinking and acting. These processes are further shaped by learners’ psychological dispositions, family environments, and broader sociocultural contexts.
From a sociopolitical perspective, AI can be understood as a “registry of power” (Crawford, 2021), embedded in platform capitalism and contributing to the reproduction of social and symbolic inequalities (Kelly-Holmes, 2024; Wang, 2025; Buolamwini & Gebru, 2018; Gebru, 2019). These dynamics tend to privilege standardized and monolingual forms of communication and reinforce Anglicist and epistemic biases at semantic, syntactic, and pragmatic levels (Rastier, 2025; Schneider, 2022, 2024; Beacco et al., 2021; Dejica et al., 2016; Raus, 2022).

These issues are particularly salient in multilingual and Indigenous contexts. In Canada, where many Indigenous languages are critically endangered, language revitalization is inseparable from questions of epistemology, community sovereignty, and cultural continuity. Indigenous languages, as carriers of relational ontologies and epistemologies, challenge dominant AI paradigms based on standardization and data extraction, and call for community-led, ethically grounded approaches aligned with principles of Indigenous data sovereignty.

Taken together, this body of research shows that generative AI is not merely a pedagogical tool, but a socio-technical and cultural environment that reshapes the cognitive, linguistic, identity-related, and political conditions of language learning and knowledge production. It also highlights the need to rethink Critical Digital Literacy (CDL) in light of the probabilistic logics, infrastructures, and regimes of algorithmic authority associated with generative systems. At the same time, empirical research on AI-mediated socialization and identity formation in educational contexts remains limited.

This conference focuses on language education across first, second, and additional languages, academic literacy, and discourse didactics in majority, minority, and marginalized language contexts, including Indigenous languages. It seeks to foster dialogue across applied linguistics, critical language education, digital discourse studies, and AI research, and welcomes theoretical, empirical, and methodological contributions examining the role of generative AI in diverse linguistic, cultural, and institutional settings.