Translation education (TE) demands significant effort from educators due to its labor-intensive nature. Developing computational tools powered by artificial intelligence (AI) can alleviate this burden by automating repetitive tasks, allowing instructors to focus on higher-level pedagogical aspects of translation. This integration of AI has the potential to significantly enhance the efficiency and effectiveness of translation education. The development of effective AI-based tools for TE is hampered by a lack of high-quality, comprehensive datasets tailored to this specific need, especially for Arabic. While the Saudi Learner Translation Corpus (SauLTC), a unidirectional English-to-Arabic parallel corpus, constitutes a valuable resource, its current format is inadequate for generating the parallel sentences required for a didactic translation corpus. This article proposes leveraging large language models like the Generative Pre-trained Transformer (GPT) to transform SauLTC into a parallel sentence corpus. Using cosine similarity and human evaluation, we assessed the quality of the generated parallel sentences, achieving promising results with an 85.2% similarity score using Language-agnostic BERT Sentence Embedding (LaBSE) in conjunction with GPT, outperforming other investigated embedding models. The results demonstrate the potential of AI to address critical dataset challenges in quest of effective data driven solutions to support translation education.

This article introduces the Saudi Learner Translation Corpus (SauLTC), an innovative multi-version English–Arabic parallel corpus featuring part-of-speech tagging. We describe the corpus parameters and compilation process and explain how textual processing and sentence alignment are conducted. The participants include 366 student translators, 48 instructors, and 23 alignment verifiers. The corpus provides access to two target versions of every ST to allow the detection of the changes in the translation and revision processes from the initial to the final draft. The translations were collected over three years, yielding 5,160,386 tokens. The metadata of 23 sentence alignment verifiers were added to the analysis as a unique variable to investigate individual differences in the manual verification process. This unidirectional corpus can be used to identify student translators’ strategies and errors in translation and analyze the efficacy of instructors’ feedback. Furthermore, it is accessible via an application and a website. It provides translation teachers and researchers with a database that can help develop corpus-based and corpus-driven teaching materials.