Generative Artificial Intelligence as a Linguistic Crutch or Cognitive Scaffold: The Interplay of Self-Beliefs and General English Proficiency in English Medium Instruction

Barış Horzum M., SORUÇ A., YÜKSEL D., Pawlak M.

International Journal of Applied Linguistics (United Kingdom), 2026 (AHCI, SSCI, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1111/ijal.70153
  • Dergi Adı: International Journal of Applied Linguistics (United Kingdom)
  • Derginin Tarandığı İndeksler: Arts and Humanities Citation Index (AHCI), Social Sciences Citation Index (SSCI), Scopus, Educational research abstracts (ERA), INSPEC, Linguistic Bibliography, MLA - Modern Language Association Database
  • Anahtar Kelimeler: academic success, akademik başarı, bireysel farklılıklar, EMI, genel İngilizce yeterliliği, general English proficiency, generative artificial intelligence, individual differences, öğretim dili olarak İngilizce, Üretken Yapay Zeka
  • Kocaeli Üniversitesi Adresli: Evet

Özet

This study examines how self-efficacy, self-regulation, and generative artificial intelligence (GenAI) -related factors interact to shape academic success and general English proficiency (GEP) within an EMI context. It draws on data from 754 EMI students at a major public university in Turkey, focusing on the social sciences and engineering disciplines. The findings reveal that for social science students, besides GEP, a positive perception towards GenAI, self-efficacy, and self-regulation are key to academic achievement—self-belief and the ability to regulate one's learning are essential for mastering both language and content. For engineering students, however, success is primarily driven by GEP, GenAI competence, and a positive perception toward GenAI. When it comes to GEP, social science students benefit from GenAI competence to improve their language skills, while engineering students rely more on self-efficacy and self-regulation. Additionally, excessive time spent on GenAI platforms is correlated to poorer academic outcomes, highlighting the importance of quality over quantity in GenAI engagement. The findings are discussed, and implications for enhancing EMI strategies are presented.