Speaker
Description
This presentation examines the impact of artificial intelligence on post-secondary language teaching by contrasting two pedagogical models. While AI excels at standardized instruction, feedback, and assessment, many required language courses depend on human-led interaction, motivation, and classroom authority. The talk argues that AI’s influence on language education is determined less by technological capability than by pedagogical design, clarifying which teaching practices are automatable and which remain fundamentally human-centered.
Keywords
Artificial intelligence
Second language pedagogy
Classroom interaction
Required language courses
Abstract
At the end of 2025, Microsoft released a list of occupations identified as vulnerable to replacement by AI. Post-secondary language teaching was included among the roles expected to be affected by AI instructional systems. While AI has demonstrated capabilities in individualized practice, feedback, and assessment, such claims often overlook the pedagogical realities of required university language courses, where student motivation and participation cannot be assumed. This presentation argues that the question of whether AI can “replace” language teachers is not primarily technological, but pedagogical.
The talk contrasts two broad pedagogical approaches. The first is an instruction-centered, standardized model characterized by fixed lesson sequences, pre-determined scaffolding, formalized rubrics, and analytics-driven feedback. This model treats learning as something that improves through tighter sequencing, clearer instruction, and increasingly refined feedback, and is therefore highly amenable to automation by AI systems that outperform humans in consistency and scale. The second approach is an interaction-centered, human-driven model in which lessons are co-constructed with learners in real time. In this model, teachers adapt tasks dynamically, manage classroom interaction, regulate affect, and provide motivation through social presence, authority, and relational engagement.
Drawing on research from second language acquisition, task-based language teaching, and classroom interaction studies, the presentation argues that these human functions are central to learning in required courses and remain resistant to automation. The presentation concludes by re-framing AI not as a threat to language teaching, but as a force that clarifies which pedagogical practices are automatable and which depend fundamentally on human interaction and judgement.
Short summary
This presentation examines the impact of artificial intelligence on post-secondary language teaching by contrasting two pedagogical models. While AI excels at standardized instruction, feedback, and assessment, many required language courses depend on human-led interaction, motivation, and classroom authority. The talk argues that AI’s influence on language education is determined less by technological capability than by pedagogical design, clarifying which teaching practices are automatable and which remain fundamentally human-centered.
| Title | Human-Centered Language Teaching and the Limits of AI |
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