Speakers
Description
Generative AI is often framed as a neutral mediator in language learning, but this poster argues that AI mediation is never neutral. Drawing on sociocultural perspectives, it shows how AI reshapes participation, redistributes effort, and alters task difficulty, emotional load, and learner risk. These shifts affect identity, investment, motivation, and affect, influencing how learners position themselves as language users. The poster foregrounds AI’s mediating role as a starting point for broader discussion.
Keywords
Generative AI, Sociocultural perspectives, Language learner identity, Mediation
Abstract
Generative AI is often framed as a neutral tool that mediates language learning by providing feedback, scaffolding, or access to linguistic resources. Drawing on sociocultural perspectives in applied linguistics, including work on mediation(a), communities of practice(b), speech communities(c), learner investment(d), and affect(e), this poster argues that mediation through AI is never neutral and reshapes the conditions under which learners engage with language. These sociocultural effects warrant closer conceptual and empirical attention.
Even when functioning primarily as a mediator, generative AI alters access to participation, redistributes effort, and changes how learners experience task difficulty, emotional load, and risk. Such shifts have implications for learner identity(f), investment(d), motivation(g), and affect(e), influencing how learners position themselves as legitimate speakers and future language users. AI-mediated environments may reduce anxiety and increase participation for some learners, while simultaneously obscuring developmental struggle or reinforcing dominant linguistic norms for others.
While generative AI ultimately operates as a hybrid force, acting as both mediator and participant, this poster deliberately foregrounds its mediating role in order to isolate how sociocultural effects emerge even in the absence of overt AI participation. Positioned as an entry point for broader discussion, the poster aims to clarify why AI-mediated learning should be examined not only in pedagogical terms, but also as a transformation of the sociocultural conditions under which language learners become social subjects.
References
A. Donato, R., & MacCormick, D. (1994). A sociocultural perspective on language learning strategies: The role of mediation. The modern language journal, 78(4), 453-464.
B. Wenger, E. (1999). Communities of practice: Learning, meaning, and identity. Cambridge university press.
C. Gumperz, J. J. (2009). The speech community. Linguistic anthropology: A reader, 1(66), 66-73.
D. Norton, B. (2016). Learner investment and language teacher identity. In Reflections on language teacher identity research (pp. 88-94). Routledge.
E. Scovel, T. (1978). The effect of affect on foreign language learning: A review of the anxiety research. Language learning, 28(1), 129-142.
F. Norton, B., & Toohey, K. (2011). Identity, language learning, and social change. Language teaching, 44(4), 412-446.
G. Dörnyei, Z. (2009). The L2 motivational self system. Motivation, language identity and the L2 self, 36(3), 9-11.
Special scheduling requests
I have two posters (one main, one co-presenter, just a heads up when scheduling)
Short summary
Generative AI is often treated as a neutral tool that mediates language learning through feedback, scaffolding, and access to linguistic resources. This poster examines how AI-mediated learning is never neutral and reshapes participation, effort, affect, and learner positioning. Focusing on AI as mediator, it highlights how sociocultural effects emerge even without overt AI participation, reframing AI use as a transformation of the conditions under which language learners become social subjects.
| Scheduling preference | Anytime on Saturday or Sunday |
|---|---|
| Title | When Mediation Is Not Neutral: Generative AI and Language Learners |