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All Journal Language, Technology, and Social Media Linguistic, Literature, and Translation Studies: LinguAmerta
Mohammed H. Alaqad
Universiti Malaya
Humanities Education Languange, Linguistic, Communication & Media Social Sciences

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Integrating Training Pills and Flipped Classroom Model to Enhance B2-Level Writing Skills in EFL Learners: A Quasi-Experimental Study Jose Maria Felix Martinez; Nadia Safeer; Lubna Choudhry; Mohammed H. Alaqad
Language, Technology, and Social Media Vol. 2 No. 2 (2024): December 2024 | Language, Technology, and Social Media
Publisher : WISE Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70211/ltsm.v2i2.68

Abstract

Writing proficiency, especially at the B2 level in English as a Foreign Language (EFL), poses challenges for learners, necessitating mastery of various competencies, including linguistic accuracy and content organization. This study investigates the effectiveness of integrating "training pills" into the Flipped Classroom model to enhance B2-level writing skills. The research adopts a quasi-experimental design conducted over two academic years (2020-2022) in Seville, Spain. The sample consists of 120 high school students, with the experimental group using training pills (short audiovisual lessons), while the control group follows traditional instruction. Pre- and post-tests measured the improvement in writing skills, supplemented by questionnaires assessing satisfaction and engagement. Quantitative data analysis revealed that the experimental group improved by 34.4%, significantly outperforming the control group (11.8%). Qualitative feedback highlighted the accessibility and engagement benefits of the training pills, particularly their role in improving students' organizational and communicative success in writing. The study concludes that training pills, when integrated into a Flipped Classroom model, effectively enhance B2 writing acquisition by providing flexible and self-paced learning opportunities. These findings have significant implications for technology-enhanced learning and offer practical insights for educators seeking to optimize writing instruction for EFL learners.
WILL ARTIFICIAL INTELLIGENCE REPLACE HUMAN TRANSLATORS AND INTERPRETERS BY 2036? A CONCEPTUAL FRAMEWORK FOR TASK SUBSTITUTION, COMMUNICATIVE RISK, AND HUMAN Mohammed H. Alaqad
LinguAmerta Vol. 3 No. 1 (2026): June 2026
Publisher : Politeknik Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/jla.v3i1.8795

Abstract

Rapid advances in neural machine translation, large language models, and speech-to-speech systems have renewed predictions that professional translators and interpreters will soon become obsolete. This conceptual paper examines whether artificial intelligence (AI) is likely to replace human language professionals between 2026 and 2036. It integrates research on translation technology, human-parity evaluation, interpreting, labour-market change, professional standards, and AI governance. The analysis separates occupation-level replacement from task-level automation and written translation from real-time interpreting. It develops a risk–routinization framework in which the suitability of AI-led, hybrid, or human-led provision depends on task predictability and the severity and reversibility of error. Four propositions follow: routine and measurable tasks are most susceptible to substitution; requirements for human oversight increase with communicative consequence; interpreting retains greater human dependence because of interactional complexity; and professional value shifts from text production toward verification, orchestration, and accountability. The paper argues that AI will absorb a substantial share of repetitive, low-risk, high-volume work and will intensify price and productivity pressures. However, it is unlikely to eliminate translation and interpreting as professions by 2036. Human expertise will remain most defensible in low-resource, culturally embedded, interactive, confidential, and high-consequence contexts. The decisive question is therefore not whether humans or machines translate better in the abstract, but how responsibility should be distributed within human-AI language-service systems.
Co-Authors Jose Maria Felix Martinez Lubna Choudhry Nadia Safeer