The increasing linguistic demands in global hospitality, coupled with the challenges faced by hotel staff in delivering effective English communication, call for innovative training approaches that integrate authentic tasks, technology, and psychological readiness. The scarcity of research on technology-enhanced Task-Based Language Teaching (TBLT) in hospitality training necessitates an investigation into its effectiveness in developing communicative competence, confidence, and Willingness to Communicate (WTC) among hotel staff. This study investigated a 16-week English program grounded in TBLT, incorporating AI-driven tools (Fully Fluent®, an AI-powered language learning app offering real-time feedback and pronunciation support) and role-play. Using a quasiexperimental pretest–posttest design complemented by qualitative analysis, the study involved 25 hotel employees. Results showed significant improvements in writing (M = 63.44 to 82.36, p < 0.05, r = 0.2) and speaking (M = 72.08 to 82.88, p < 0.05, r = 1.8), with stronger effects in speaking, highlighting the impact of role-play and AI-assisted pronunciation practice. While WTC components—confidence (M = 4.14), motivation (M = 4.17), and reduced anxiety (M = 4.14)— increased, no significant correlations emerged with language performance (p > 0.05), suggesting linguistic gains stemmed from structured task exposure rather than self-perceived readiness. Thematic analysis revealed greater confidence and preparedness but persistent challenges in spontaneous guest interactions and accent comprehension, underscoring the need for extended taskbased interventions with adaptive AI-driven simulations. Findings affirm TBLT’s pedagogical value in workplace training and its potential to bridge the gap between structured learning and dynamic realworld communication in hospitality settings.
2025-10-10
This study examines the impact of ICT-supported collaborative writing on English-major students’ performance in cause–effect essay writing at a public university in Vietnam. Adopting a quasiexperimental design, the research utilized pre- and post-tests, a questionnaire, and semi-structured interviews for data collection. Quantitative data were analyzed using SPSS 22.0, including descriptive statistics, paired-sample and independent t-tests, as well as Cohen’s d to measure effect size and identify differences in writing performance between the experimental and control groups before and after the intervention. For qualitative data, Creswell’s (2013) spiral model was employed, with MAXQDA™ Analytics Pro 2020 supporting data organization and analysis. The findings revealed that the use of ICT tools significantly enhances students’ cause–effect essay writing, primarily by promoting effective peer collaboration and enabling immediate feedback. While both groups showed improvement, the experimental group outperformed the control group with a large effect size. Students in the experimental group also reported higher levels of engagement and viewed ICT as a valuable aid in organizing ideas and accessing learning resources. These findings offer important pedagogical implications, suggesting that collectively integrating ICT tools into collaborative writing tasks can effectively support students’ academic writing development.
This study explores the acceptance of English language teachers in Ho Chi Minh City regarding the integration of Facebook into their instructional practices. It focuses on how perceptions of usefulness and ease of use may influence their intention to adopt the platform for vocabulary and grammar instruction. Employing a mixed-methods approach, the research collected data from 150 teachers divided into two age groups (22–39 and 40+). Qualitative insights were gathered through semi-structured interviews grounded in the Technology Acceptance Model (TAM), followed by a quantitative survey measuring perceived usefulness, ease of use, satisfaction, attitude, and intention to use. The findings suggest that all hypothesized relationships within the TAM framework were statistically significant, with younger teachers exhibiting a stronger correlation between satisfaction and attitude, indicating potential generational differences in affective responses to technology. Both age groups acknowledged Facebook’s pedagogical potential in fostering student engagement and collaborative learning, while also noting challenges such as distractions and time constraints. The study aims to contribute to teacher-centered research on educational technology adoption and offers preliminary implications for the development of age-sensitive training programs and supportive policy initiatives in language education.
The rapid advancement of artificial intelligence (AI) technologies in language education has garnered significant attention from researchers who have explored AI applications in various language learning domains. Though there are numerous systematic reviews of studies on the application of AI in language education, speaking skills remain a crucial capacity for ESL/EFL learners to master. Most of these existing systematic reviews have primarily focused on areas such as language learning, teaching, and writing. Notably, only one systematic review has specifically addressed the use of AI Chatbots in speaking, leaving a gap in reviewing empirical research on this vital aspect of language learning. To bridge this research gap, this systematic review mainly investigated research trends on the applications, benefits, challenges, and implications of AI technologies in ESL/EFL speaking skills. It systematically reviewed 55 empirical studies about AI technologies in speaking skills published from 2017 to 2024, which were systematically selected from major academic databases (Web of Science, Scopus, ERIC, and Google Scholar) using the PRISMA flowchart and predefined inclusion/ exclusion criteria. The data was generated from analysis of those empirical studies. The findings revealed that AI tools have several advantages in improving speaking skills, promoting learners’ language proficiency and assisting students with pronunciation correction. However, the review also emphasized the potential limitations and challenges in using AI technologies in ESL/EFL speaking environments, such as inequality and accessibility, inaccurate speech recognition, and over-reliance. In addition, the review provided valuable technological, pedagogical, educational and research implications in the context of ESL/EFL speaking.
This study explored how a training program consisting of AI tools, social media feedback, and an iterative translation process influenced Thai pre-service teachers’ development of translation skills and creativity. Fifty third-year students in a ‘General Translation 2’ course participated in a translation process consisting of three stages, namely an initial manual translation, an AI-assisted revision, and a final creative adaptation. Groups of three or four students were tasked with translating an authentic Thai social media advertisement. The study utilized a mixed-methods approach, integrating quantitative assessments of translation accuracy, fluency, and creativity with qualitative insights derived from written reflections, group interviews, and social media engagement. Content analysis and thematic analysis were used to analyse the data. The findings revealed clear improvements in translation accuracy, fluency, and creativity, particularly with AI assistance. However, student groups which actively refined AI-generated content appeared to perform better, highlighting the importance of human oversight. Public feedback, particularly from social media, influenced students’ engagement strategies, revealing that audience appeal was more critical than translation accuracy alone. Groups that used humour, relatable language, and interactive elements received higher audience engagement. The study emphasizes that while AI tools aid students’ technical accuracy, they should not substitute for human creativity and judgment. Thus, balancing AI tools with critical thinking is crucial in translation. The study also suggests that incorporating social media feedback can improve students’ understanding of audience expectations.
Integrating technology into teaching and learning has become an undeniable trend in English education. This study investigates the impact of the combination of asynchronous AI-based tool tasks with synchronous, interactive practices such as virtual exchanges and pronunciation workshops on EFL learners’ pronunciation development. Conducted over a 12-week schedule, the research included 23 Vietnamese university students who acquired pronunciation via self-directed training using the ELSA Speak software and engaged in facilitated virtual exchange sessions and workshops. A pre-test/ post-test design comprising mixed-method data through the test-retest results, the ELSA Speak app’s dashboard, and focus group discussions was used. Quantitative data revealed significant development of some segmental (phonemes) and suprasegmental (intonation) aspects of pronunciation but not fluency. Besides the frequency of the ELSA Speak app usage, students’ perceived English level seemed to have an impact on their pronunciation learning. Qualitative findings revealed that the students appreciated the interactivity and feedback of the app but also pointed out drawbacks such as cost, limited use, and the difficulty of ensuring regular use. The research accentuates the pedagogical potential of integrating AI-powered apps with real-time communicative practice. It suggests more focus on motivation, teaching, and accessibility to enhance learning gains to their maximum. Pedagogical implications for technology-mediated language learning research and instructional design are suggested to inform subsequent research.
As blended learning becomes more prevalent in English as a Second Language (henceforth ESL) instruction, identifying pedagogical approaches that balance linguistic support with learner engagement is increasingly important. This quasi-experimental, explanatory sequential mixedmethods study investigated the effectiveness of Task-supported language teaching (henceforth TSLT) in enhancing the writing performance of intermediate ESL learners within a blended learning environment. Over six weeks, 69 participants were assigned to either an experimental group (n = 35) receiving TSLT instruction or a control group (n = 34) receiving Presentation–Practice–Production (henceforth PPP) instruction, both delivered through a blended format. Writing performance was measured through pretest and posttest argumentative essays, which were scored using an analytic rubric. Quantitative results showed significant gains in the TSLT group across all writing domains, particularly in content and vocabulary. During the focus group interview, participants regarded TSLT tasks as structured, meaningful, and engaging. Furthermore, the affordances offered by digital tools in such an environment enable opportunities for timely feedback and revision beyond class time. These findings suggest that blended TSLT may enhance writing performance.
This study investigated the role of artificial intelligence (AI) in second language education (SLE) across Asia - a region marked by profound linguistic and cultural diversity. AI offers innovative tools to support language learning in these complex contexts. Applying a mixed-methods design, the study gathered quantitative data from more than 400 participants (386 learners and 48 educators) across multiple Asian countries, alongside qualitative feedback and a comprehensive literature review. The study examined the perceived effectiveness, accessibility, and cultural relevance of AI in language education. The findings indicated that among the AI tools available, aspects like language comprehension, vocabulary practice, and verbal communication were the easiest for users to navigate. Participants particularly appreciated features such as personalized learning paths, immediate feedback, and the integration of culturally relevant content that resonates with their preferences. This study aslo pointed out a strong and statistically significant link between the use of AI tools and perceived improvements in language proficiency. However, challenges remain, including issues related to inadequate digital infrastructure, affordability, and cultural mismatches. While AI holds transformative promise for inclusive and personalized language education in Asia, addressing issues of access and cultural sensitivity is essential. Future research should focus on developing sustainable, contextually grounded AI solutions to support diverse learners across the region.
Informal Digital Learning of English (IDLE) refers to self-directed English language activities conducted in informal digital environments, driven by learners’ interests and goals. Although IDLE is recognized as a valuable supplement to formal language education, its potential remains underexploited by many Vietnamese students, including those majoring in English linguistics (EL). This multiple case study investigates the underlying factors contributing to their reluctance to engage in IDLE among EL students at two English-Medium-Instruction (EMI) universities in Vietnam. A preliminary online questionnaire was distributed to 300 EL students to recruit participants for this study. Using the purposive sampling method, seven students were selected for in-depth semistructured interviews, each lasting between 30 and 45 minutes. Findings indicate that although all participants acknowledged the benefits of IDLE, they engaged only at a relatively limited level, and they favored receptive over productive skills. More importantly, their reluctance was primarily attributed to two interrelated factors: an unsupportive learning environment and low motivation. These factors were further influenced by other elements such as learner identity, learner autonomy, and peer judgment. To some extent, this study offers a nuanced understanding of the possible hidden barriers to IDLE engagement and provides potential practical implications for curriculum development, pedagogical practices, and assessment reforms in Vietnamese EL programs.
In recent years, podcasts have become increasingly popular as a means of supporting language development, particularly in terms of incidental vocabulary learning and speaking fluency. Although podcasts are increasingly integrated into educational settings, research specifically focusing on their effectiveness in EFL classrooms remains relatively scarce. This study explores the impact of three English-language podcast series: All Ears English (AEE), Splendid English (SE), and Coffee Break English (CBE) on vocabulary acquisition and speaking performance among EFL learners. A quasiexperimental approach was used, involving 139 Iranian participants who were assigned to one of four groups: three experimental groups, each exposed to a different podcast series over a 13-week period, and one control group (CG) that continued with conventional instruction without access to podcasts. Standardized tests evaluated vocabulary and speaking abilities at both the beginning and conclusion of the intervention period. All three podcast groups achieved better results than the CG did in both vocabulary and speaking assessments after the intervention. The experimental groups showed no significant differences in their outcomes which indicates that each podcast series provided equivalent benefits. The research demonstrates that podcast-based instruction in any format can effectively support traditional EFL teaching methods. The flexible nature of podcasts makes them suitable for educators to select multiple resources which provide authentic language exposure and support incidental learning for their students.
2026-07-16
Growing evidence supports machine translation’s (MT) effectiveness for second and foreign language (L2) writing among English as a Foreign Language (EFL) learners. Although MT use by less-proficient learners is often criticised for causing disengagement from the writing process, how these learners actually interact with MT remains underexplored. Adopting a multidimensional engagement framework, this explanatory sequential mixed-methods study examines the engagement of CEFR A2-level EFL learners in MT-assisted writing. A novel instrument, the Engagement in MT-assisted Writing scale, was administered to 434 university students to assess four engagement dimensions: behavioural, cognitive (pre- and post-editing), affective, and social. Cluster analysis identified four distinct engagement profiles (high, moderate, affective–social low, and low), with social engagement being consistently weak across all groups. To contextualise these patterns, follow-up interviews were conducted with seven students—three from the high, three from the moderate, and one from the affective–social low group. The findings revealed that learners primarily used MT as a translanguaging resource for L2 writing, showing cognitive engagement through pre- and post-editing strategies to enhance MT output. Participants in the moderate and affective–social low groups also used MT to support multilingual, real-life communication. Despite frequent MT use, two moderate-group participants expressed uncertainty about revising MT outputs, while one reported feeling guilty about relying on MT. These insights may reshape educators’ perspectives on students’ MT use, highlighting the importance of targeted strategy instruction. Further, adopting translanguaging approaches can help students use MT purposefully to express their voices more confidently in L2 writing.
Despite growing recognition of the value of artificial intelligence (AI) in English as a Foreign Language (EFL) instruction, adoption at the school level remains limited due to a lack of understanding about the complex factors influencing teachers’ post-training acceptance. This study examined the interrelationships among subjective norms, technologist roles, student influence, process facilitation, compatibility, perceived attitudes, and behavioral intentions in Indonesian senior high school EFL teachers following a professional development workshop on AI integration. Using validated survey instruments, data from 146 teachers were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Matrix Analysis (IPMA). Quantitative results showed that subjective norms significantly affected process facilitation and compatibility, while student influence strongly predicted technologist roles, compatibility, and process facilitation. Technologist roles and compatibility were pivotal in shaping positive attitudes and intentions to adopt AI. IPMA identified compatibility as a key area for targeted improvement. The findings stress the need for ongoing, context-sensitive professional development to promote effective and sustainable AI integration in EFL teaching.
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