This study explores user perceptions and practical employment of ChatGPT Translation among students involved in English-Arabic translation tasks. For the quantitative part method, data were collected from a diverse assembly of 102 participants, comprising demographic queries and a constructed 20-item Likert scale questionnaire. The collected data were categorized and critically examined under five principal constructs: Efficiency, Accuracy, Ease of Use, Trustworthiness, and Overall Preference. The findings indicate a predominantly positive perception of ChatGPT among the participants. In addition, detailed analysis reveals novel insights, such as the considerable appreciation for ChatGPT's role in enhancing translation efficiency and the high level of trust expressed in its ability to maintain the confidentiality of translated work. Another significant finding is the tool's competitive edge, with participants favoring ChatGPT over other translation tools. Furthermore, the findings underscore the importance of extending the research landscape on AI-assisted translation tools. Thus, extending the research on A.I. Translation platforms fosters their effective integration into the industry and understanding of their potential impact on the future of translation pedagogy.

As streaming platforms adopt artificial intelligence (AI)-powered subtitle systems to satisfy global demand for instant localization, the cognitive impact of these automated translations on viewers remains largely unexplored. This study used a web-based eye-tracking protocol to compare the cognitive load that GPT-4o-generated Arabic subtitles impose with that of professional human translations among 82 native Arabic speakers who viewed a 10 min episode (“Syria”) from the BBC comedy drama series State of the Union. Participants were randomly assigned to view the same episode with either professionally produced Arabic subtitles (Amazon Prime’s human translations) or machine-generated GPT-4o Arabic subtitles. In a between-subjects design, with English proficiency entered as a moderator, we collected fixation count, mean fixation duration, gaze distribution, and attention concentration (K-coefficient) as indices of cognitive processing. GPT-4o subtitles raised cognitive load on every metric; viewers produced 48% more fixations in the subtitle area, recorded 56% longer fixation durations, and spent 81.5% more time reading the automated subtitles than the professional subtitles. The subtitle area K-coefficient tripled (0.10 to 0.30), a shift from ambient scanning to focal processing. Viewers with advanced English proficiency showed the largest disruptions, which indicates that higher linguistic competence increases sensitivity to subtle translation shortcomings. These results challenge claims that large language models (LLMs) lighten viewer burden; despite fluent surface quality, GPT-4o subtitles demand far more cognitive resources than expert human subtitles and therefore reinforce the need for human oversight in audiovisual translation (AVT) and media accessibility.

Translating humor is challenging as it is often tied to niche linguistic, interactional, and psychological contexts. This paper presents a new English-Arabic parallel corpus of stand-up comedy shows subtitles as a pedagogic tool for translating authentic examples of humor. Furthermore, the researchers experiment with the corpus by designing customized translator training tasks for checking the accuracy of translating verbal irony in a humor translation workshop. The tasks are designed based on bottom-up and top-down scaffolding of learning objectives based on a revised model of Bloom's taxonomy (2001). Moreover, the researchers propose Mateo's model of translating irony (1995) as a method of translating verbal irony in the context of stand-up comedy shows. Finally, the researchers recommend using parallel corpora as a pedagogic tool in humor translation training workshops and classrooms to bring the elements of trainees' creativity to overcome controversial translation issues.

The study employs eye-tracking measures and comprehension quizzes to investigate linguistic processing during subtitle reading and comprehension in Arabic and Spanish. Specifically, it examines the impact of native English (L1) audio, foreign Spanish or Arabic (L2) audio, or the absence of audio on subtitle reading strategies and the processing of embedded linguistic units in novice learners. The study utilizes a 2x3 within-subjects design, manipulating audio condition (L1 audio, L2 audio, no audio) and subtitle language (Spanish, Arabic). Global eye-tracking analyses reveal subtitle reading patterns, while local analyses focus on the processing of specific linguistic units. Comprehension quizzes assess learning outcomes after the viewing of the subtitled videos. The findings suggest that the absence of audio leads to increased fixation durations, indicating greater attentional demand. Spanish subtitles result in higher fixation counts and prolonged reading times compared to Arabic, reflecting deeper cognitive processing. The study highlights the complex interplay between audio presence, subtitle language, and individual proficiency in shaping attentional allocation and lexical processing during language acquisition through audio-visual media.

This study examines how morphological complexity affects cognitive effort in neural machine translation (NMT) post-editing across six languages. Analysis of the DivEMT dataset shows that morphologically richer target languages like Ukrainian and Turkish require more editing time, keystrokes, and frequent pauses, indicating higher cognitive demands. Vietnamese, despite simpler morphology, also showed high cognitive effort, suggesting other factors like syntax influence processing load. Mean Size of Paradigm (MSP) analysis confirmed Ukrainian and Turkish’s high morphological complexity compared to isolating languages like Vietnamese. Higher error rates in morphologically rich languages demonstrate increased editing needs. While user perceptions varied, the data reveals that greater linguistic distance correlates with higher cognitive effort in NMT post-editing, showing typological divergence impacts beyond morphology alone.