İngiliz Dili ve Edebiyatı Bölümü Koleksiyonu

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https://dspace.mudanya.edu.tr/handle/20.500.14362/195

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Browsing İngiliz Dili ve Edebiyatı Bölümü Koleksiyonu by WoS Q Quality "Q1"

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    EFL instructors’ engagement with AI as digital media: A qualitative case study from Türkiye
    (Sage, 2026-05) Ocaktan Çeliktürk, Halenur; 414465
    Despite growing interest in integrating artificial intelligence (AI) into language education, research on how EFL instructors adopt, experience, and sustain the use of AI-driven tools over time remains limited. This qualitative study explores Turkish EFL instructors' perceptions and uses of AI-driven tools through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). The findings indicate that although most participants initially hesitated to integrate AI-driven tools, they gradually became active users, reflecting increased openness shaped by social influence and contextual factors consistent with the UTAUT framework. While instructors did not consistently adopt technological innovations, they demonstrated curiosity and eagerness to experiment with tools perceived as enhancing teaching performance. Performance expectancy and facilitating conditions, particularly access to technological resources, emerged as key factors influencing adoption. ChatGPT was identified as the most frequently used tool, followed by Grammarly and QuillBot, for material development, assessment, in-class activities, and feedback. At the same time, participants expressed concerns about overreliance and the lack of structured institutional training to support ethical and effective AI use. Overall, the study underscores the growing significance of AI integration in EFL education and highlights the need for sustained professional development to support responsible and pedagogically informed AI-driven practices.
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    Utilizing large language models for EFL essay grading: an examination of reliability and validity in rubric-based assessments
    (Wiley, 2025-01) Yavuz, Fatih; Çelik, Özgür; Yavaş Çelik, Gamze; 131069
    This study investigates the validity and reliability of generative large language models (LLMs), specifically ChatGPT and Google's Bard, in grading student essays in higher education based on an analytical grading rubric. A total of 15 experienced English as a foreign language (EFL) instructors and two LLMs were asked to evaluate three student essays of varying quality. The grading scale comprised five domains: grammar, content, organization, style & expression and mechanics. The results revealed that fine-tuned ChatGPT model demonstrated a very high level of reliability with an intraclass correlation (ICC) score of 0.972, Default ChatGPT model exhibited an ICC score of 0.947 and Bard showed a substantial level of reliability with an ICC score of 0.919. Additionally, a significant overlap was observed in certain domains when comparing the grades assigned by LLMs and human raters. In conclusion, the findings suggest that while LLMs demonstrated a notable consistency and potential for grading competency, further fine-tuning and adjustment are needed for a more nuanced understanding of non-objective essay criteria. The study not only offers insights into the potential use of LLMs in grading student essays but also highlights the need for continued development and research.