In contrast to word formation in stem-based languages, Arabic words are derived through a process of mapping a consonantal root into a vowel pattern. Ample evidence suggests that, during lexical processing of real words in Arabic, native speakers are successful in decomposing words into their roots and patterns such that there is priming between Arabic words that share the same root. In L2 acquisition, however, the question remains whether L2 learners adopt L2 morphological processing mechanisms especially when these are different from L1 processes. The current study explores this question by investigating L2 Arabic learners’ sensitivity to the presence of roots when processing auditory speech in Arabic. 38 Indonesian learners of Arabic as a second language and 38 native Arabic speakers rated the word-likeness of auditorily presented nonwords in Arabic. Results reveal that L2 Arabic learners, like native speakers, are sensitive to the presence of roots in the nonwords. These findings are discussed in light of lexical processing models.

This study investigated the growth language mindset of undergraduate students in Thailand, focusing on their beliefs about intelligence, aptitude, and age sensitivity in English language learning. A total of 1,174 students from diverse academic disciplines participated in the survey, which employed the adapted Language Mindset Inventory (LMI). Descriptive statistics, multivariate analyses of variance (MANOVA), and confirmatory factor analysis (CFA) were conducted to address four research questions: (1) the overall level of growth mindset in English learning, (2) variations of mindset across disciplines, (3) differences based on the urban–rural divide, and (4) the construct validity of the proposed three-factor model. Results revealed that students generally hold strong growth-oriented beliefs, with the highest mean score for age sensitivity. MANOVA results indicated significant disciplinary differences in intelligence beliefs and age sensitivity beliefs, with students in telecommunication engineering, polymer engineering, manufacturing automation and robotics engineering, and mechanical engineering reporting the strongest growth mindsets. In contrast, bioscience and environmental engineering students showed weaker orientations. A separate MANOVA showed significant urban–rural differences across all three constructs, with urban students consistently reporting higher growth beliefs, particularly in age sensitivity. The CFA provided strong support for the three-factor model, and fit indices confirmed an excellent model fit. These findings highlight that while growth-oriented beliefs about language learning are widespread among Thai undergraduates, disciplinary and geographical disparities remain. The validated three-factor model confirms that intelligence, aptitude, and age sensitivity beliefs are distinct but interrelated dimensions of language mindset. The study highlights the need for tailored interventions to foster growth-oriented language learning environments.

Despite the surge of research interest in generative AI and the rapid public adoption of large language models (LLMs), their role in translation remains unclear. The reliability of these systems and their limitations as machine translation tools continue to be a central concern for translation teachers and students. Systematic reviews that specifically examine LLMs in translation are still scarce. This systematic review aims to address this gap by synthesizing and interpreting recent empirical studies on the use of LLMs in translation across three areas: (1) LLMs’ translation quality, (2) LLM-generated translation feedback, and (3) the integration of LLMs into translation education. Drawing on 55 empirical studies, the findings show that LLMs—particularly GPT—consistently outperform conventional neural MT systems. For general, non-specialized texts, their output often approaches human quality, though human translators maintain a clear advantage in culturally dense, technical, or literary content. Evidence further indicates that LLMs can provide helpful and timely feedback that identifies common linguistic issues, which in turn can assist both teachers and students; however, teacher feedback remains superior in depth, contextual sensitivity, and clarity. As contemporary translation workplaces increasingly rely on MT and AI-supported tools, training students to work with LLMs has become essential for aligning classroom practice with professional expectations. At the same time, educators must balance LLM-assisted tasks with hands-on human translation to ensure that students continue to develop essential linguistic and problem-solving skills.

This research explores the Joyful, Experiential, Meaningful, Personalized, Integrated Technology, and Sustainability Oriented (JEMPITA) learning model as an innovative 21st-century pedagogical approach in Indonesia. This research is grounded in the observation that existing learning models have yet to fully accommodate 21st-century skills such as critical thinking, collaboration, communication, creativity, and technological integration. The JEMPITA model was designed by integrating deep learning theory to create engaging, meaningful, and contextually relevant learning experiences, and has received significant support from both teachers and educational experts. Employing a research and development (R&D) approach, this study adopts the 4D Model (Define, Design, Develop, Disseminate), though it is limited to the defining and designing phases. The research participants consist of Elementary School teachers in West Java, Indonesia. Data were collected through closed- and open-ended questionnaires, interviews, and focus group discussions (FGDs). A needs analysis involving 1,875 respondents indicated a strong demand for a learning model that enhances learning motivation, personalization, technological integration, and sustainability orientation. Among the model’s components, joyful and meaningful factors emerged as the most urgently needed, whereas the primary challenges identified were limited training and inadequate infrastructure. These findings suggest that the JEMPITA model holds strong potential as a transformative approach to primary education that is responsive to 21st-century challenges.

This study examines a critical deficiency in the literature regarding the effects of diminished government translation services on the assimilation of immigrants with limited English proficiency (LEP) into the U.S. labor market while simultaneously assessing the efficacy of alternative, scalable translation models. While previous studies have identified language barriers as impediments to labor market engagement, empirical information assessing both policy reductions and the relative efficacy of professional, AI-assisted, and hybrid translation methods is scarce. The study employs a mixed-methods design, incorporating surveys from 150 LEP immigrants alongside interviews with 50 employers and 20 translation service providers in various linguistically diverse U.S. cities. It integrates quantitative analysis using SPSS Version 28 and qualitative thematic coding via NVivo 14 to deliver a comprehensive understanding. Research demonstrates that professional and hybrid human–AI translation services significantly enhance employment alignment, retention, and workplace satisfaction, with hybrid models attaining up to 40% greater accuracy in job placement and 20% higher retention rates, thereby illustrating their superior effectiveness compared to conventional methods. Conversely, cuts to governmental translation services intensify employment gaps, dependence on informal translation, and systemic injustices. This research is innovative due to its comparative, multi-model evaluation inside a singular labor market scenario, providing strong empirical evidence that was previously inaccessible. The study theoretically extends workforce integration and social inclusion frameworks to include language access mechanisms and practically offers policymakers, workforce agencies, and employers culturally sensitive, scalable strategies that improve immigrant integration, foster equitable labor market participation, and reduce structural inequalities.

The low interest in reading and the uneven distribution of basic literacy skills among early grade elementary school students indicate the need for a more holistic, contextual, and developmentally responsive learning model. This study aims to develop a literacy model that integrates the principles of 6C, multisensory approaches, and growth mindset. The research employed a Design and Development (D&D) approach, which consisted of the following stages: problem identification, model design, development, trial implementation, and evaluation. The sample comprised 70 first- and second-grade students from four elementary schools located in West Java, DKI Jakarta, and Banten provinces. Data collection techniques included tests, classroom observations, and questionnaires. Quantitative data analysis was carried out through normality testing, t-tests, and descriptive analysis, while qualitative data were analyzed thematically by examining teachers’ observation notes and students’ responses to identify patterns in learning experiences, perceptions, and challenges in implementing the model. The findings reveal that the literacy model developed in this study significantly improved students’ reading and writing literacy skills, with the average gain categorized as moderate. Additional findings emphasize that students became more enthusiastic, willing to take risks, and felt supported through the use of multisensory learning materials. Teachers also reported that the approach facilitated differentiated instruction tailored to students’ needs. Furthermore, the integration of growth mindset encouraged motivation and perseverance in task completion. Overall, the integrated 6C, multisensory, and growth mindset literacy model not only produced significant improvements in learning outcomes but also fostered positive, adaptive, and developmentally appropriate learning experiences for early grade elementary students.

This research investigates how university students perceive learning general English vocabulary using social media. With the rapid shift in the social and educational landscapes towards the seamless use of social media and its expanding role as an educational tool, its importance as an informal, interactive vocabulary acquisition tool continues to grow. The research investigates how students perceive social media as an additional resource for learning English vocabulary, focusing on Instagram, Twitter, and YouTube. The study employs an attitude research framework, using surveys and interviews, and focuses on the educational use of these platforms, the types of content, and the purported enhancement of English language abilities. The results indicate that most university students believe social media platforms assist with learning general English vocabulary. In addition, social media platforms offer informal, interactive, multimodal, and context-rich opportunities to learn vocabulary by engaging with posts, videos, voice notes, comments, and other users. The research emphasizes the use of social media as an educational tool to foster vocabulary acquisition, outlining ways to use it to facilitate language learning.

The language used by foreign language teachers in the classroom serves a dual role: as a means of communication and as a source of rich, meaningful input for learners. From a Conversation Analysis (CA) perspective, EFL teachers’ classroom language actively shapes interaction with learners, as it can either facilitate or hinder their oral performance and may discourage or encourage their initiation to use the language. This study explores the classroom language functions of English for Specific Purposes (ESP) teachers in Vietnamese universities. Sixteen hours of classroom time, drawn from English for Transportation and English for Tourism and Hospitality courses taught by eight ESP teachers, were recorded, transcribed and analyzed using CA approach. The data analysis showed three main functions of teachers’ classroom language: classroom management, pedagogical function, and feedback provision. Quantitative results indicated that classroom management accounted for only a small proportion of classroom discourse, while pedagogical function and feedback provision were almost equally dominant. Qualitative findings further highlighted specific sub-functions within each category. For classroom management, ESP teachers primarily used discourse to monitor time and regulate learning pace. Within pedagogical function, delivering instructions emerged as the most common sub-function, whereas providing corrective feedback was most frequent in the feedback provision category. The analysis also revealed how teachers integrated different sub-functions and functions of their discourse to facilitate students’ speaking. These findings underscore the crucial role of ESP teachers’ classroom language and point to the need for training in classroom discourse for both EFL and ESP teachers.

This study looks at changes in the rhetorical structure and features of L2 university student writing after receiving English for Academic Purposes (EAP) instruction. Previous research suggests that the way ideas are presented in text can vary across different cultures. This potentially means that L2 students, who study in English Medium Instruction (EMI) universities, may have to learn how to organize and arrange their ideas in writing to align with what their content area instructors expect to see. The EAP courses that L2 students take, to prepare them for the challenges of studying in an EMI university, primarily focus on language use, but these also may draw students’ attention to possible differences in written discourse in L1 and L2 and help learners to produce the type of writing that is expected by their EMI instructors. To examine this, the present study looked at changes in the rhetorical features and structure of L2 student writing produced before and after learners had completed a 17-week EAP course. After the completion of writing, semi-structured interviews were also carried out with 10 randomly selected participants to gauge their opinions about possible rhetorical differences in written discourse in their first and second languages. Findings revealed that there were significant changes in the rhetorical structure and features of L2 student text after learners had completed EAP instruction. These results suggest that EAP instruction may help students to produce writing that aligns with the expectations of their instructors.

The purpose of this study is to explore the attitudes of Saudi EFL university learners regarding the use of AI-generated games in the process of learning English vocabulary. This mixed-methods study includes 150 responses to the questionnaire and 10 responses to a structured interview. The results of the mixed research show that most students use AI tools in learning vocabulary or playing games such as Duolingo, Quizlet, Kahoot, and WordUp. Although most respondents claim to be satisfied with AI-based games, which are interactive, amusing, and effective for retaining vocabulary, the questionnaire analysis indicates that AI games enhance vocabulary size, contextual understanding, and language abilities. At the same time, the students face multiple problems, such as technical difficulties, lack of access to the internet, lack of assistance, and an inappropriate level of gaming content. As far as gaming is concerned, the proposed solutions are increased teacher involvement, training workshops, a better selection of feedback tools, and Arabic translation to help comprehend the message. The interview data partially supports this, with results showing that AI use generally has a positive effect in stimulating interest and autonomy and reducing anxiety. In total, the findings seem to support the creation of authentic and engaging contextual vocabulary learning environments with the help of AI games. However, the positive outcomes will only be instrumental once the instructional, technical, and contextual problems are addressed by means of high-quality training and adjustments in the level of difficulty and integration.