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All Journal Asshika: Journal of English Language Teaching and Learning
Mowafg Abrahem
University of Zawia
Education Social Sciences

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Journal : asshika journal of english language teaching and learning

 1 
Lexical, Syntactic, and Terminological Errors in Arabic-English Legal Translation among Undergraduate Students Mowafg Abrahem; Abdulrauf Atia; Laylay Hasan; Karima Elhaj; Entisar Alatrish; Safa Alrumayh; Zaynab Omar
Asshika: Journal of English Language Teaching and Learning Vol. 2 No. 2 (2025): April
Publisher : Saniya Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65190/asshika.v2i2.470

Abstract

This study examines the main error patterns found in Arabic-English legal translation among undergraduate students at the Faculty of Languages and Translation, University of Zawia, Libya. It seeks to identify the most frequent lexical, syntactic, and terminological errors in students’ translations and to explain these errors in relation to legal translation competence and teaching needs. The study used a corpus-based descriptive error-analysis design. Sixty undergraduate students enrolled in legal translation courses participated in the study. Each student translated a selected legal text from Arabic into English under controlled classroom conditions, and the translated scripts were compiled into a learner corpus for analysis. The findings showed a total of 538 errors across three main categories: terminological, syntactic, and lexical. Terminological errors were the most frequent, representing 45.2% of all errors, followed by syntactic errors at 31.8%, while lexical errors accounted for 23.0%. The results showed that students had the greatest difficulty in rendering specialized legal concepts accurately, maintaining terminological consistency, and distinguishing technical legal meanings from ordinary vocabulary. Syntactic difficulties also appeared in sentence structure, word order, passive voice, and the handling of complex legal clauses. The study concludes that students’ problems in legal translation may stem from limited exposure to authentic legal discourse, insufficient training in legal terminology, and overreliance on general translation strategies. The findings underline the need for more specialized, genre-based, and terminology-focused legal translation instruction in Libyan higher education.
The Effect of AI-Supported English Literary Instruction on Learning Archaic Vocabulary among EFL Students Abdulghani Abouzied; DafaAllah Ibrahim; Khuloud Alouzi; Mowafg Abrahem; Bushra Alfallah; Mohieddin Masoud
Asshika: Journal of English Language Teaching and Learning Vol. 2 No. 2 (2025): April
Publisher : Saniya Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65190/asshika.v2i2.471

Abstract

Learning archaic vocabulary remains a serious challenge for students studying English literature in an English-as-a-Foreign-Language (EFL) context. This study examines the effect of combining English literary texts with artificial intelligence (AI) tools on the learning of archaic vocabulary among undergraduate students at the Faculty of Languages and Translation, University of Zawia, Libya. The study used a quasi-experimental mixed-methods design with 60 EFL students, divided into an experimental group (n = 30) and a control group (n = 30). Both groups completed a pre-test and a post-test to measure their knowledge of archaic vocabulary in literary texts. The experimental group received AI-supported literary instruction, while the control group received conventional vocabulary instruction. The results showed that both groups improved, but the experimental group achieved much higher gains, rising from a pre-test mean of 9.83 to 16.27, while the control group increased from 9.71 to 12.08. An independent-samples t-test showed a significant difference between the two groups, t(58) = 7.65, p < .001. The questionnaire results also showed very positive student perceptions toward the AI-supported approach. The findings indicate that integrating AI tools into literature-based instruction can improve learning of archaic vocabulary and increase student engagement in EFL higher education.
Co-Authors Abdulghani Abouzied Abdulrauf Atia Bushra Alfallah DafaAllah Ibrahim Entisar Alatrish Karima Elhaj Khuloud Alouzi Laylay Hasan Mohieddin Masoud Safa Alrumayh Zaynab Omar