Creating AI Applications for EFL Teaching: Perceptions and Challenges in Initial Teacher Education
DOI:
https://doi.org/10.17398/1695-288X.25.2.87Keywords:
Artificial Intelligence; English as Foreign Language; Pre-service Teachers; Educational Technology; Technology uses in Education., Artificial Intelligence, English as Foreign Language, Pre-service teachers, Educational Technology, Technology uses in educationAbstract
The dimension of "creating" with artificial intelligence (AI), proposed by UNESCO as an advanced level of AI literacy, remains underexplored in the field of initial teacher education (ITE), particularly in English as a foreign language (EFL) teaching. In this context, the present study aimed to analyze pre-service English teachers' perceptions of the pedagogical possibilities and difficulties associated with creating educational applications using generative AI (GenAI) through Gemini 3.0. The participants were 51 English Pedagogy students from three Chilean public universities. The study adopted a qualitative approach through an online survey, and the data were analyzed using inductive content analysis based on Krippendorff's model, with the assistance of two large language models and human supervision. The results were organized into five categories: the pedagogical potential of AI for English language learning, the creation of educational applications, the new role of the pre-service teacher, new competencies and challenges in the use of AI, and the appropriation of the experience. However, an important finding was the identification of specific training gaps: prompt formulation emerged as a didactic competence rather than a technical one, and the greatest difficulty lay in translating pedagogical intentions into clear instructions. The experience also transformed the pre-service teachers' self-perception, as they came to see themselves as creators of educational technology. It is concluded that initial teacher education must shift the focus from the tool to the pedagogical problem, preparing teachers as learning sequence designers who create, adapt, and critically evaluate AI-powered resources.
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