Innovation in Language Learning
AI Competence Is Not Optional: Rethinking Teacher Education between Autonomy, Ethics, and Algorithms
Katharina McGrath, Friedrich-Schiller-University, Jena (Germany)
Abstract
Generative AI is rapidly transforming foreign language education by enabling personalized learning paths, adaptive feedback, simulation-based language practice, and assistive technologies (Son et al., 2023; García & Novak, 2024). While studies show significant gains in language proficiency and learner motivation when AI is purposefully integrated (Zhang et al., 2025; Wang, 2024), concerns around data privacy, authorship, algorithmic bias, and educational equity remain unresolved (Bender et al., 2021; Fleischmann, 2024).
This paper argues that effective and responsible AI integration critically depends on teacher education. It proposes a six-dimensional AI competence framework for pre-service and in-service foreign language teachers: (1) human-centered mindset, (2) AI literacy, (3) ethical and legal awareness, (4) AI pedagogy, (5) diagnostic and adaptive competence, and (6) learning agility.
Without these competences, AI-driven learning environments risk amplifying inequalities, reinforcing stereotypes, and marginalizing teachers. Drawing on empirical findings, policy frameworks (UNESCO, 2019; 2024), and practice-oriented scenarios, the paper provides actionable recommendations for embedding AI competences into teacher education. The goal is to empower teachers to critically evaluate, ethically integrate, and pedagogically harness AI while supporting learners in developing both linguistic proficiency and digital competence.
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Keywords |
Generative AI; Language Teacher Education; AI Competence; AI Pedagogy |
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REFERENCES |
[1] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623. https://doi.org/10.1145/3442188.3445922 [2] Fleischmann, M. (2024). Künstliche Intelligenz im Sprachunterricht: Chancen und Herausforderungen. Journal of Language Learning & AI, 12(3), 45-67. [3] García, P., & Novak, M. (2024). Adaptive AI in language education: A systematic review of empirical studies. Innovation in Language Learning and Teaching, 19(1), 12–34. https://doi.org/10.1080/17501229.2025.2509759 [4] Son, J.-B., Ružić, N. K., & Philpott, A. (2023). Artificial intelligence technologies and applications for language learning and teaching. Journal of China Computer-Assisted Language Learning. https://doi.org/10.1515/jccall-2023-0015 [5] UNESCO. (2019). Beijing Consensus on Artificial Intelligence and Education. Paris: UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000368303 [6] UNESCO. (2024). AI Competency Framework for Teachers. Paris: UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000383652 [7] Wang, D. (2024). Teacher- versus AI-generated (Poe application) corrective feedback and language learners’ writing anxiety, complexity, fluency, and accuracy. International Review of Research in Open and Distributed Learning, 25(3), 37–56. https://doi.org/10.19173/irrodl.v25i3.7646 [8] Zhang, L., Chen, Y., & Wu, J. (2025). The impact of AI-based blended learning on college students’ English proficiency and engagement: Evidence from the FLIT platform. Language Testing in Asia, 15(1), 37–56. https://doi.org/10.1186/s40468-025-00376-7 |
