Search Results (38)

Deaf children who acquire a signed language at home and at school prior to learning to read are a unique group of developing bilinguals. They are first-language users of a signed language (e.g., American Sign Language; ASL) and second-language users of the written form of the ambient spoken language (e.g., English). Because signed languages do not possess orthographies that are widely used, deaf readers typically read in their second language. This profile stands in contrast to hearing bilingual readers, who read in their first language and, sometimes, their second language. In studies of child readers, vocabulary knowledge is a strong predictor of reading comprehension for both monolinguals and bilinguals. We combine the unique linguistic backgrounds of young signing deaf readers with what is known about vocabulary knowledge and reading, guided by the following research question: Does signed (L1) and spoken (L2) vocabulary knowledge predict reading (eye-gaze) behaviors of deaf children when reading in their L2? We present results from an exploratory pilot eye-tracking study of nine deaf ASL-English bilingual children ages 9–11, examining whether ASL and English vocabulary knowledge predict different aspects of the reading paradigm. Results suggest that ASL vocabulary predicts where-decisions and measures of oculomotor control such as skipping and regressions, while English vocabulary predicts when-decisions and first-pass durations. We suggest these results from this small-scale study provide initial support for the language interdependence hypothesis because deaf child signers’ first-language vocabulary knowledge predicts some of the variance in second-language reading performance. Full article

Background/Objectives: In mainstream educational settings, deaf and hard-of-hearing (DHH) students may have limited or no access to the spoken lectures and discussions that are central to the hearing majority classroom. Yet, engagement in these educational and social exchanges is fundamental to their learning and inclusion. Two primary visual accessibility solutions can support this need: real-time speech-to-text transcriptions (i.e., captioning) and high-quality sign language interpreting. Their combined use (or coexistence), however, raises concerns of competition between concurrent streams of visual information. This article examines the empirical evidence concerning the effectiveness of using both captioning and sign language simultaneously in educational settings. Specifically, it investigates whether this combined approach leads to better or worse content learning for DHH students, when compared to using either visual accessibility solution in isolation. Methods: A review of all English language studies in peer-reviewed journals until August 2025 was performed. Eligible studies used an experimental design to compare content learning when using sign language and captions together, versus using sign language or captions on their own. Databases Reviewed: EMBASE, PubMed/MEDLINE, and PsycInfo. Results: A total of four studies met the criteria for inclusion. This limited evidence is insufficient to decide on the coexistence of captioning and sign language. Yet, it underscores the potential of captions for content access in education for DHH, even when sign language is available. Conclusions: The present article reveals the lack of evidence in favor or against its coexistence with sign language. With the aim to be constructive for future research, the discussion offers considerations on the attentional demands of simultaneous visual accessibility resources, the diversity of DHH learners, and the impact of current and forthcoming technological advancements. Full article

English is the first foreign language most Taiwanese have encountered, yet few have achieved proficient speaking skills. This paper presents a generative AI-based English speaking training system designed to enhance oral proficiency through interactive AI agents. The system employs ChatGPT version 5.2 to generate diverse and tailored conversational scenarios, enabling learners to practice in contextually relevant situations. Spoken responses are captured via speech recognition and analyzed by a large language model, which provides intelligent scoring and personalized feedback to guide improvement. Learners can automatically generate scenario-based scripts according to their learning needs. The D-ID AI system then produces a virtual character of the AI agent, whose lip movements are synchronized with the conversation, thereby creating realistic video interactions. Learning with an AI agent, the system maintains controlled emotional expression, reduces communication anxiety, and helps learners adapt to non-native interaction, fostering more natural and confident speech production. Accordingly, the proposed system supports compelling, immersive, and personalized language learning. The experimental results indicate that repeated practice with the proposed system substantially improves English speaking proficiency. Full article

This study reexamines Learner Englishes (LEs)–New Englishes (NEs) continuum by considering intervarietal variation, mode differences, and multiple linguistic levels. Relying on comparable written and spoken corpus data, we investigate the valency patterns and senses of the verb TAKE across two LEs (Mainland Chinese English (MCE) and Belgian French-speaking English (BFE)) and two NEs (Singapore English (SgE) and Hong Kong English (HKE)) within the Extra- and Intra-territorial Forces (EIF) Model. The study examines whether internal linguistic factors, namely, mode (writing and speech) and linguistic levels (valency patterns and senses), influence the variety positioning along the LEs-NEs continuum and whether this positioning reflects the expected proximity cline to native English (NativeE) (BFE > MCE > HKE > SgE) established within the EIF Model. Our quantitative results reveal that individual varieties intermingle depending on mode and linguistic levels rather than occupying stable positions along the LEs-NEs continuum. Dendrogram analyses yield distinct variety clustering patterns that contradict the expected proximity cline to NativeE. Qualitatively, we identify some shared linguistic features across LEs and NEs that suggest common underlying language learning strategies. These results contribute to variationist linguistics by demonstrating that English varieties exhibit dynamic development trajectories shaped by language-internal factors (e.g., mode and linguistic levels). We propose refining the EIF Model to incorporate language-internal dimensions, thereby bridging the gap between LEs and NEs through a more nuanced theoretical framework. Full article

Breast cancer is a leading cause of mortality among women, disproportionately affecting Hispanic populations in the U.S., particularly those with limited health literacy and language access. To address these disparities, we present a bilingual, web-based educational platform tailored to low-literacy Hispanic users. The platform supports full navigation in English and Spanish, with seamless language switching and both written and spoken input options. It incorporates automatic speech recognition (ASR) capable of handling code-switching, enhancing accessibility for bilingual users. Educational content is delivered through culturally sensitive videos organized into four categories: prevention, detection, diagnosis, and treatment. Each video includes embedded and post-video assessment questions aligned with Bloom’s Taxonomy to foster active learning. Users can monitor their progress and quiz performance via a personalized dashboard. An integrated chatbot, powered by large language models (LLMs), allows users to ask foundational breast cancer questions in natural language. The platform also recommends relevant resources, including nearby treatment centers, and support groups. LLMs are further used for ASR, question generation, and semantic response evaluation. Combining language technologies and LLMs reduces disparities in cancer education and supports informed decision-making among underserved populations, playing a pivotal role in reducing information gaps and promoting informed healthcare decisions. Full article

The growing application of artificial intelligence in education has created new avenues for second language learning. The following research explores the impact of learning with the help of chatbots on English conversation among secondary students in the Northern Borders Region in Saudi Arabia. The quasi-experimental design involved 30 students divided into two groups: an experimental group that interacted with an intervention using a GPT-powered chatbot for three weeks, and a control group that underwent traditional teaching. Pre- and post-tests were given to assess conversation competence. At the same time, students’ attitudes toward the chatbot-assisted learning experience were measured through questionnaires, teacher observation, and usage logs in the chatbot. Results showed statistically significant improvement in the experimental group’s speaking competence (mean gain = 5.24, p < 0.001). Students showed high motivation, elevated confidence, and high satisfaction with the learning experience provided through the chatbot (overall attitude mean = 4.35/5). Teacher observations testified that the students were much more engaged and spontaneous, and using the chatbot was positively correlated with score gain (r = 0.61). The outcomes indicate that chatbot-based learning is a practical approach for facilitating the development of spoken English, particularly in low-resource learning environments. The research provides empirical proof in favour of the incorporation of interactive AI into EFL teaching in all the secondary schools in Saudi Arabia. Full article

Ireland, historically a country of emigration, has transformed into a hub of immigration. Today, over 200 languages are spoken among its 5.25 million residents, with approximately 750,000 individuals speaking a language other than English or Irish at home. This growing linguistic diversity is increasingly reflected in Irish primary classrooms, where teachers are called upon to support students from a wide range of linguistic and cultural backgrounds). In response, Teaching English as an Additional Language (EAL) modules have expanded across initial teacher education (ITE) programs in Ireland. This study examines over two decades of teacher development initiatives, tracing a shift from an earlier bilingual model—where multilingualism was viewed primarily as second language acquisition—to a more expansive, European-informed vision of plurilingualism. Drawing on recommendations for reflexive, linguistically and culturally responsive education, this research adopts an insider/outsider discursive case study approach to explore student teachers’ preparedness to support multilingual learners in Irish primary schools. Conducted through a collaboration between an Irish teacher educator/module coordinator and an intercultural education specialist, this study employs reflexive thematic analysis) of student teachers’ self-reports from a twelve-week elective module on linguistic and cultural diversity within a Primary Bachelor of Education program. Data were drawn from surveys (n = 35) across three module iterations in 2019, 2021, and 2023. Findings indicate student teachers’ growing awareness of language teaching strategies and resources, developing positive orientations toward inclusive and plurilingual pedagogy, and emerging skills in professional collaboration. However, areas for further development include strengthening agency in navigating real-world multilingual teaching scenarios and embedding deeper reflexivity around linguistic identities, integrating students’ home language and intercultural learning. The paper concludes with recommendations to expand access to language teaching resources for diverse student profiles and support collaborative, shared EAL leadership through professional learning communities as part of teacher education reform. Full article

This study presents the first systematic evaluation of in-context learning for Tarifit machine translation, a low-resource Amazigh language spoken by 5 million people in Morocco and Europe. We assess three large language models (GPT-4, Claude-3.5, PaLM-2) across Tarifit–Arabic, Tarifit–French, and Tarifit–English translation using 1000 sentence pairs and 5-fold cross-validation. Results show that 8-shot similarity-based demonstration selection achieves optimal performance. GPT-4 achieved 20.2 BLEU for Tarifit–Arabic, 14.8 for Tarifit–French, and 10.9 for Tarifit–English. Linguistic proximity significantly impacts translation quality, with Tarifit–Arabic substantially outperforming other language pairs by 8.4 BLEU points due to shared vocabulary and morphological patterns. Error analysis reveals systematic issues with morphological complexity (42% of errors) and cultural terminology preservation (18% of errors). This work establishes baseline benchmarks for Tarifit translation and demonstrates the viability of in-context learning for morphologically complex low-resource languages, contributing to linguistic equity in AI systems. Full article

This study explores the effects of interacting with AI vs. human interlocutors on English language learners’ speaking performance in a game-based learning context. We developed Taboo Talks, a word-guessing game in which learners alternated between giving and guessing clues with either an AI or a human partner. To evaluate the impact of interaction mode on oral proficiency, participants completed a story retelling task, assessed using complexity, accuracy, and fluency (CAF) metrics. Each participant engaged in both partner conditions, with group order counterbalanced. The results from the retelling task indicated modest improvements in fluency and complexity, particularly following interaction with the AI partner. Accuracy scores remained largely stable across conditions. Post-task reflections revealed that learners perceived AI partners as less intimidating, facilitating more relaxed language production, though concerns were noted regarding limited responsiveness. Qualitative analysis of the gameplay transcripts further revealed contrasting interactional patterns: AI partners elicited more structured interactions whereas human partners prompted more spontaneous and variable interactions. These findings suggest that AI-mediated gameplay can enhance specific dimensions of spoken language development and may serve as a complementary resource alongside human interaction. Full article

Science, technology, engineering, arts, and mathematics (STEAM) education represents an array of fields that have significant promise for the future careers of students. However, in deaf education, little research has been conducted to understand how best to provide access to STEAM learning opportunities for deaf students. This manuscript explores STEAM learning and Deaf Education through the lens of translanguaging. Translanguaging is the use of multiple linguistic resources by multilingual individuals. The authors recorded deaf teens attending a STEAM camp as they engaged in a collaborative problem-solving activity to explore the language resources they used to make sense of and communicate their understanding of the problem during various stages of the activity (gathering information, generating ideas, and evaluating ideas). We viewed their interactions through a translanguaging lens. We found that the campers used an array of both language-based (ASL, spoken English, gesture, and fingerspelling) and tool-based (writing on a whiteboard, engaging with informational papers, using computers or phones) translanguaging activities to gather information and communicate with one another. While selection of language resources did not differ by activity stage, they did differ by group, suggesting that campers were sensitive to the communication needs of their group mates. Full article

Paraphrasing means expressing the semantic meaning of a text using different words. Paraphrasing has a significant impact on numerous Natural Language Processing (NLP) applications, such as Machine Translation (MT) and Question Answering (QA). Machine Learning (ML) methods are frequently employed to generate new paraphrased text, and the generative method is commonly used for text generation. Generative Pre-trained Transformer (GPT) models have demonstrated effectiveness in various text generation tasks, including summarization, proofreading, and rephrasing of English texts. However, GPT-4’s capabilities in Arabic paraphrase generation have not been extensively studied despite Arabic being one of the most widely spoken languages. In this paper, the researchers evaluate the capabilities of GPT-4 in text paraphrasing for Arabic. Furthermore, the paper presents a comprehensive evaluation method for paraphrase quality and developing a detailed framework for evaluation. The framework comprises Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Lexical Diversity (LD), Jaccard similarity, and word embedding using the Arabic Bi-directional Encoder Representation from Transformers (AraBERT) model with cosine and Euclidean similarity. This paper illustrates that GPT-4 can effectively produce a new paraphrased sentence that is semantically equivalent to the original sentence, and the quality framework efficiently ranks paraphrased pairs according to quality criteria. Full article

Multi-word expressions make up a large proportion of the English language and particularly spoken language. Using multi-word expressions can assist with the impression of fluency, making them useful for language learners to know and use. However, proven methods for teaching this language phenomenon are required, so that learners can easily use multi-word expressions in their conversations. The purpose of the study was to examine the efficacy of a fluency workshop focused on multi-word expression use in conversation and to determine the most appropriate implementation for the Japanese context. An action research structure was used over three iterations of the fluency workshop, learner feedback and teacher observations were used to make improvements. Learner feedback regarding the usefulness of each activity for learning English was compared between the original cohort and subsequent cohorts. The results showed significant differences in levels of perceived usefulness for activities where improvements were made, but also for some activities where no specific improvement was made, suggesting that teaching improves through practice. Pedagogical implications include maximising the time on task via clear instructions, providing visual time constraints, and offering scaffolding to support the use of multi-word expressions when recall seems beyond a learner. Full article

Linguistic inequity drives systemic disparities in healthcare for non-native English speakers. This study evaluates a project to train and provide qualified medical interpreters (QMI) to assist volunteer and safety-net clinics and community-based organizations in supporting healthcare for immigrants and refugees. We provided scholarships to bilingual community members to take a medical interpreter training course and developed a workforce for those who passed the training course. We focused on lesser-spoken foreign languages such as Arabic, Amharic, Pashto, Dari, and Burmese. Those who passed the course participated in a semi-structured interview to learn about their experiences in the training program, as well as barriers and facilitators to becoming a QMI. To date, 23 people have passed the training and are part of the QMI workforce program that has provided 94 h of interpreter services over four months, serving 66 individual patients. The evaluation showed that community members have interest in becoming QMIs and many have the required language proficiency to enroll and pass training. Finding full-time employment for less spoken languages has proven to be challenging. Full article

The massive relocation of international students calls for a thorough investigation of diverse difficulties faced by them, among which language-related barriers are reported to have serious consequences. The main goal of this research is to investigate accent-related challenges as barriers to comprehension and effective communication faced by international students in the United Kingdom (UK), along with the factors that helped or could help the students in terms of having better experiences. The scope of this study is limited to native British accents. The study relies on data collected to analyse the impact of native-accented speech, both qualitatively and quantitatively, on the listening experiences of currently enrolled or recently graduated international students in a British university. The underlying mixed-method approach is comprised of a survey and an interview. Analysis of data collected from the survey (n = 33 participants) revealed that 42% of the participants considered native-accented speech as the biggest factor affecting their listening comprehension. This is followed by a fast speech rate, which was selected by 36% of the participants. Regarding mitigation of the difficulties, participants showed mixed responses in terms of adopting various strategies. During the interview, participants (n = 6) shared their listening comprehension experiences, particularly those encountered during the initial months after their arrival in the UK. The results obtained are potentially useful in terms of students’ support, English as a Second Language (ESL) curriculum design, English language teachers’ training and establishing learning pedagogies. Full article

Learners of additional languages, particularly in adulthood and instructed settings, are typically exposed to large quantities of written input from the earliest stages of learning, with varied and far-reaching effects on L2 phonology. Most research investigating this topic focuses on learning across languages that share the same orthographic script, often involving the Latin alphabet and English. Without exploring phonological learning over a greater diversity of spoken and written language combinations, our understanding of orthographic effects on L2 phonology remains narrow and unrepresentative of the many individuals acquiring languages across writing systems, globally. This paper draws together preliminary research relating to the influence of written input, in a distinct script from known languages, on L2 phonology. Studies are grouped into those with naïve participants, where the written forms are entirely unfamiliar to the participant, and those with experienced learners, who have varying levels of proficiency and familiarity with the target orthography. While there is great scope and need for further investigation, initial evidence suggests that even entirely unfamiliar written input impacts phonological learning and is certainly influential with growing proficiency in the spoken and written language. The article concludes with theoretical and methodological considerations for future research in this emerging field. Full article