Automated scoring in the era of artificial intelligence: An empirical study with Turkish essays
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Authors
Automated scoring (AS) has gained significant attention as a tool to enhance the efficiency and reliability of assessment processes. Yet, its application in under-represented languages, such as Turkish, remains limited. This study addresses this gap by empirically evaluating AS for Turkish using a zero-shot approach with a rubric powered by OpenAI's GPT-4o. A dataset of 590 essays written by learners of Turkish as a second language was scored by professional human raters and an artificial intelligence (AI) model integrated via a custom-built interface. The scoring rubric, grounded in the Common European Framework of Reference for Languages, assessed six dimensions of writing quality. Results revealed a strong alignment between human and AI scores with a Quadratic Weighted Kappa of 0.72, Pearson correlation of 0.73, and an overlap measure of 83.5 %. Analysis of rater effects showed minimal influence on score discrepancies, though factors such as experience and gender exhibited modest effects. These findings demonstrate the potential of AI-driven scoring in Turkish, offering valuable insights for broader implementation in under-represented languages, such as the possible source of disagreements between human and AI scores. Conclusions from a specific writing task with a single human rater underscore the need for future research to explore diverse inputs and multiple raters.
Original language | English |
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Article number | 103784 |
Journal | System |
Volume | 133 |
Number of pages | 12 |
ISSN | 0346-251X |
DOIs | |
Publication status | Published - 10.2025 |
Bibliographical note
Publisher Copyright:
© 2025 The Authors
- Automated scoring, Large language models, Multilevel models, Rater reliability, Turkish essays, Zero-shot with rubric
- Educational science
Research areas
- Language and Linguistics
- Education
- Linguistics and Language