A qualitative exploration of pre-service English teachers’ integration of generative artificial intelligence in corpus-based language pedagogy

KURT TİFTİK G.

Computer Assisted Language Learning, 2025 (AHCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1080/09588221.2025.2552109
  • Dergi Adı: Computer Assisted Language Learning
  • Derginin Tarandığı İndeksler: Arts and Humanities Citation Index (AHCI), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Applied Science & Technology Source, Computer & Applied Sciences, EBSCO Education Source, Education Abstracts, Educational research abstracts (ERA), ERIC (Education Resources Information Center), INSPEC, Linguistics & Language Behavior Abstracts, MLA - Modern Language Association Database, Psycinfo, DIALNET
  • Anahtar Kelimeler: ChatGPT, Corpus, corpus-based language pedagogy, generative artificial intelligence, teacher education
  • Marmara Üniversitesi Adresli: Evet

Özet

Despite the longstanding application of corpus technology in data-driven learning (DDL), its integration into mainstream language teaching remains limited due to the complexity of corpus tools and the lack of pedagogical training. The emergence of generative artificial intelligence (GenAI) offers new possibilities to address these challenges, yet its role in corpus-based language pedagogy (CBLP) remains underexplored. This qualitative case study examines how Turkish pre-service English teachers (PTs) develop and implement GenAI-supported CBLP and the factors influencing their decisions when selecting GenAI and corpus tools to address student needs. Using Shulman’s pedagogical reasoning model as the analytical framework, the study focused on PTs’ CBLP development across lesson design and classroom implementation. The findings revealed PTs’ meaningful development of GenAI-supported CBLP in both theory and practice. Cognitive, pedagogical, and affective factors shaped their decisions regarding tool use. The study offers insights into the implementation of GenAI-supported CBLP and highlights implications for future research and teacher education.