This study presents a quantitative descriptive analysis comparing the performance of human simultaneous interpreters (H-SI) and AI-driven interpreting systems (AI-SI) using deep learning architectures specifically Transformer-based models (e.g., mBART, Wav2Vec 2.0 + NMT pipelines) in real-time multilingual contexts. Drawing on a corpus of 1,248 interpreted segments extracted from TED Talks (English–Spanish, English–Mandarin), we evaluate output quality across six empirically derived metrics: lexical accuracy, syntactic fluency, temporal alignment, semantic fidelity, discourse cohesion, and error density. Data were collected from 30 professional human interpreters and three state-of-the-art AI systems (Google Translate Live, DeepL Pro, and a fine-tuned mBART-50 model). Statistical analyses (descriptive statistics, Mann-Whitney U tests, and Kruskal-Wallis H tests) reveal that while AI-SI outperforms H-SI in lexical accuracy (M = 92.4%, SD = 3.1) and temporal alignment (M = 0.87s lag, SD = 0.21), human interpreters demonstrate significantly superior performance in semantic fidelity (M = 86.7% as well as. 71.3%, p < .001) and discourse cohesion (M = 84.1% as well as. 63.9%, p < .001). Error density was significantly lower in human output (Mean Rank = 47.3 as well as. 29.1, p = .002). Findings suggest that current AI systems, despite algorithmic advancements, remain deficient in pragmatic and contextual adaptation core competencies rooted in human cognitive-linguistic processing. The study contributes a validated metric framework for evaluating AI interpreting performance and calls for hybrid human-AI paradigms in professional settings. Implications for interpreter training curricula and machine translation pedagogy are discussed

Keywords: Simultaneous Interpreting، Artificial Intelligence، Deep Learning، Quantitative Descriptive Research، Machine Translation، Human-Machine Comparison، Interpreter Performance، Transformer Model

-This qualitative case study investigates the effectiveness of AI-driven translation technologies in mediating cultural understanding within English Language Teaching (ELT) contexts in Libyan higher education. Drawing on data from semi-structured interviews with 55 ELT instructors, 24 classroom observations across six universities, and document analysis of student translation outputs and institutional curricula, the study reveals a dualistic role of artificial intelligence in language pedagogy. While AI-powered tools such as Google Translate and Microsoft Translator significantly enhance lexical accessibility and reduce language anxiety among learners, their capacity to convey cultural nuance remains critically limited. Findings indicate that algorithmic biases, rooted in Anglo-American linguistic corpora, result in the systematic flattening of cultural meaning and misrepresentation of idioms, humor, politeness strategies, and socio-pragmatic cues, thereby reinforcing a form of digital linguistic imperialism. Students’ uncritical reliance on AI outputs has fostered a “copy-paste culture,” undermining opportunities for intercultural reflection and eroding pragmatic awareness. However, the study also identifies transformative potential when AI tools are pedagogically repurposed through “teaching against the machine” strategies. A subset of instructors successfully used AI-generated mistranslations as pedagogical moments to foster critical cultural awareness, metacognitive reflection, and intercultural dialogue. Despite this potential, widespread implementation is hindered by systemic barriers, including the absence of institutional policies, lack of professional development, curricular gaps, and infrastructural challenges. The research concludes that AI technologies are not inherently facilitative or obstructive to cultural understanding; rather, their effectiveness is contingent upon intentional pedagogical mediation, critical digital literacy, and context-sensitive integration. The study calls for curriculum reforms, ethical AI integration frameworks, and teacher training programs that

position AI as a scaffold, not a substitute for intercultural communicative competence in ELT contexts

KEYWORDS: AI-driven translation, cultural understanding,, English language teaching, higher education, Libya, intercultural competence, technology integration

driven translation technologies in mediating his qualitative case study investigates the effectiveness of AI

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This research investigated the challenges medical university students encounter in learning

technical vocabulary. A quantitative research approach was adopted. 61 first-year medical

students at the University of Gharyan participated in the study. A structured questionnaire was

used to gather the data. The findings revealed that medical university students encounter

several challenges in medical vocabulary learning, including pronunciation, spelling, meaning and use. The results also indicated that medical university students struggle to recall some

medical terms, which could be attributed to cognitive overload. The researchers suggest

revaluating the current English medical programs at the Faculties of Medicine in Libya.

Teaching vocabulary in context, highlighting the grammatical categories of words, drawing

distinctions between synonyms through examples, and training students on using medical

words through interactive activities could enhance the current English medical programs.

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