The Effect of Data-Driven Feedback, AI-Assisted Error Analysis, and Translation Knowledge on Trainee Translators’ Performance
DOI :
https://doi.org/10.36602/faj.2026.n21.19Keywords:
data-driven feedback, trainee students, performance, AI-empowered tools, METEOR assessmentAbstract
Feedback plays a pivotal role in enhancing learning outcomes in education; however, the integration of artificial intelligence-generated feedback, particularly that produced by advanced language models such as ChatGPT, remains largely underexplored within the field of translation teaching. This research investigates the impact of data-driven feedback strategies on the performance of 70 trainee translators recruited from administrative and economic workshops in The Translation Department at Tripoli University. Participants were chosen based on specific criteria, and all received theoretical knowledge and training on English-Arabic translation; moreover, all completed the pre-requisite translation workshop courses. It also explores the influence of AI tools and their support in analyzing translation. Data was collected by conducting pre- and post-tests and adopting a training program based on cooperative learning. ChatGPT-4 was used to analyze the translations and identify errors. Translation quality was evaluated using the automatic metric "METEOR. "The results of the tests show improvements in trainees' performance. The adopted strategies allow instructors to achieve the goals and track the trainees' progress by addressing the challenges when they appear and providing immediate feedback with reliance on cooperative learning, ChatGPT 4 text analysis, and automatic assessment. The approach has a positive impact on the evaluation and feedback process. Based on the test results, the tools can be adopted as a pedagogical resource to provide initial reflections on translation work and its effectiveness when combined with human reinforcement.
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