Search Results (108)

This study explores the digital competence of foreign language teachers in Bulgarian secondary education by focusing on the institutional context of which they are a part, the strengths and gaps of their competence, and their levels of competence. It draws upon empirical data that were collected and analyzed within an integrated, dual-instrument framework, combining the SELFIE (Self-reflection on Effective Learning by Fostering the Use of Innovative Educational Technologies) and SELFIE for TEACHERS (Self-reflection on Effective Learning by Fostering the Use of Innovative Educational Technologies for Teachers) EU-aligned assessment tools. The results from the questionnaire data show that the foreign language teachers state that they work in a relatively good technological environment and evaluate the usage of digital technologies for teaching and communication purposes within the school context as a salient aspect of their digital competence. The results also reveal three areas in the study participants’ digital competence that are in need of improvement: (1) empowering learners/personalizing the educational process, (2) assessment and (3) facilitating learners’ digital competence. In addition, the findings indicate that the foreign language educators rate their digital competence at a low to medium level. By blending institutional and teacher-oriented perspectives into a single integrated study of Bulgarian secondary school foreign language teachers, this investigation extends the existing research and makes evidence-based recommendations for institutional capacity building, teacher education policy and targeted professional development aimed at improving the educators’ digital competence. Full article

The pedagogical efficacy of artificial intelligence (AI) technologies in education heavily depends on cultural, technological, and cognitive contexts. Prior studies examined AI-driven learning outcomes without accounting for cultural variability or sufficiently anchoring their analyses in robust theoretical frameworks. The current study discusses the interconnection between AI technologies, learner competencies, and educational outcomes, in addition to the significance of digital and media literacy in secondary foreign language teaching. It employs Hofstede’s cultural dimensions theory, the technology acceptance model, and sociocultural learning theory to examine how AI technologies affect learning outcomes of English as a foreign language among Lebanese high school students. One hundred and eighty high school students in Mount Lebanon were given a 20-item survey using a quantitative research design. The results were analyzed using statistical tests and analyses in SPSS version 27.0.1. The findings indicate that AI technologies significantly enhance student learning outcomes: affective and motivational outcomes (45%), social and collaborative competencies (35%), and English language proficiency (accounting for 43% of variance). Furthermore, these relationships are strongly moderated by digital and media literacy, which increases the beneficial effects of AI on learning outcomes. The findings also show that students’ opinions, engagement, and acceptance of AI-supported language learning are influenced by cultural traits. Full article

Multimodal Learning Analytics (MMLA) is an extension of Learning Analytics that combines multiple data streams such as audio, video, physiological signals, logs, and spatial trails to analyze learning processes that cannot be easily captured through any single modality. This review synthesizes research on sensing and instrumentation, feature extraction, multimodal fusion, modeling approaches, and end-to-end systems that provide feedback and support reflection. We also discuss how generative AI and Large Language Models (LLMs) increasingly improve MMLA pipelines by enabling scalable semantic and pragmatic analysis of learner discourse and interaction. In addition, we review robustness issues that arise when working with real-world data (e.g., noise, missing data, and scalability) and responsible deployment issues such as privacy and student-focused views of fairness, accountability, transparency, and ethics (FATE). Full article

Integrating people with hearing disabilities into schools is one of the biggest problems that Latin American societies face. Mexican Sign Language (MSL) is the main language and culture of the deaf community in Mexico. However, its use in formal education is still limited by structural inequalities, a lack of qualified interpreters, and a lack of technology that can support personalized instruction. This study outlines the conceptualization and development of a mobile application designed as an adaptive assistive technology for learning MSL, utilizing a combination of computer vision techniques, deep learning algorithms, and personalized pedagogical interaction. The suggested system uses convolutional neural networks (CNNs) and pose-estimation models to recognize hand gestures in real time with 95.7% accuracy. It then gives the learner instant feedback by changing the difficulty level. A dynamic learning engine automatically changes the level of difficulty based on how well the learner is doing, which helps them learn signs and phrases over time. The Scrum agile methodology was used during the development process. This meant that educators, linguists, and members of the deaf community all worked together to design the product. Early tests show that sign recognition accuracy and indicators of user engagement and motivation show favorable performance and are at appropriate levels. This proposal aims to enhance inclusive digital ecosystems and foster linguistic equity in Mexican education through scalable, mobile, and culturally relevant technologies, in addition to its technical contributions. Full article

As the English language spreads worldwide, debate has intensified over introducing it early in multilingual school systems. In the Arab world, this question is especially sensitive because Arabic is closely linked to cultural and religious identity, and early English policies may shift the language balance in primary education. This review synthesizes 31 peer-reviewed studies on childhood English learning and early English teaching practices, addressing key aspects of age of acquisition, bilingual outcomes, and language maintenance or identity. Using transparent search and selection reporting, we examined studies published between 2000 and 2025. Findings cluster around four themes: age of acquisition, mother-tongue maintenance and identity, teacher preparation and pedagogy, and social outcomes. The evidence from the review shows that earlier exposure can support pronunciation, fluency, and metalinguistic awareness, but the strength and direction of these gains depend primarily on program quality and bilingual model design. Additive approaches that maintain and value Arabic literacy while providing rich, high-quality English input are often associated with better learning outcomes than subtractive arrangements that reduce Arabic use. However, effects vary by context and implementation quality. Where Arabic is reduced without adequate support, learners may face risks such as weaker first-language development and heightened identity-related strain. However, these outcomes are not inevitable and are moderated by factors such as teacher preparation, instructional design, and school–home language support. We propose a balanced early-English design that builds progressive English proficiency while maintaining continuous Arabic-medium literacy, supported by targeted teacher professional development, family and community engagement, and continuous Arabic-medium literacy. The review concludes with policy and practice implications for curriculum designers, school leaders, and decision-makers, and calls for longitudinal, classroom-based research on identity trajectories and English-medium instruction in Arab primary education. Full article

In this study, we examined the interrelations between motivation and self-regulated learning (SRL) strategies in the context of online English language instruction among Chinese university students. Data were collected from 1100 first-year undergraduates enrolled in an online College English course. Canonical correlation analysis revealed significant multivariate associations between motivational constructs and SRL strategies. Cluster analysis further identified two distinct learner profiles, Engaged Strategic Learners and Disengaged Learners, demonstrating differences in motivation, SRL use, and online learning experiences. Thematic analysis of open-ended responses offered additional insights into students’ perceived challenges and instructional needs. Our findings contribute to a deeper understanding of how motivational and SRL characteristics influence learners’ engagement and outcomes in online English learning environments. Full article

English argumentative writing is a cornerstone of academic and professional communication, yet it remains a significant challenge for second-language (L2) learners. While Large Language Models (LLMs) show promise as components in automated feedback systems, their responses are often generic and lack the structural insight necessary for meaningful improvement. Existing Automated Essay Scoring (AES) systems, conversely, typically provide holistic scores without the kind of actionable, fine-grained advice that can guide concrete revisions. To bridge this systemic gap, we introduce ARGUS (Argument Understanding and Structured-feedback), a novel neuro-symbolic system that synergizes the semantic understanding of LLMs with the structured reasoning of Graph Neural Networks (GNNs). The ARGUS system architecture comprises three integrated modules: (1) an LLM-based parser transforms an essay into a structured argument graph; (2) a Relational Graph Convolutional Network (R-GCN) analyzes this symbolic structure to identify specific logical and structural flaws; and (3) this flaw analysis directly guides a conditional LLM to generate feedback that is not only contextually relevant but also pinpoints precise weaknesses in the student’s reasoning. We evaluate ARGUS on the Argument Annotated Essays corpus and on an additional set of 150 L2 persuasive essays collected from the same population to augment training of both the parser and the structural flaw detector. Our argument parsing module achieves a component identification F1-score of 90.4% and a relation identification F1-score of 86.1%. The R-GCN-based structural flaw detector attains a macro-averaged F1-score of 0.83 across the seven flaw categories, indicating that the enriched training data substantially improves its generalization. Most importantly, in a human evaluation study, feedback generated by the ARGUS system was rated as consistently and significantly more specific, accurate, actionable, and helpful than that from strong baselines, including a fine-tuned LLM and a zero-shot GPT-4. Our work demonstrates a robust systems engineering approach, grounding LLM-based feedback in GNN-driven structural analysis to create an intelligent teaching system that provides targeted, pedagogically valuable guidance for L2 student writers engaging with persuasive essays. Full article

Early mathematical learning predicts later academic achievement, and creativity within mathematics plays a central role in higher-order thinking. This study examined the effects of linguistically responsive mathematics enrichment programs for nurturing mathematical creativity. Participants were 250 high-potential kindergarten English Learners across six urban schools in New York, Texas, and California. A linguistically responsive enrichment intervention adapted from the Mentoring Young Mathematicians (M²) math curriculum was implemented for 80 h across seven months. Using the Tangram Creativity Assessment, fluency, flexibility, and originality were measured in students’ tangram problem solving. Additional predictors included Tangram Problem Solving Speed (TPSS), general reasoning (CogAT), and mathematical achievement (NWEA MAP Math). ANCOVA showed significant post-test differences favoring the intervention group across all creativity components. Two-group structural equation modeling analysis supported measurement invariance and explained 55–60% of posttest creativity variance. TPSS emerged as the strongest predictor, with greater effects for the intervention group. These findings highlight the potential of enrichment programs and language-accessible geometry tasks to cultivate creativity in young gifted ELs by strengthening their mathematical foundation while supporting flexible and original problem solving. Full article

Researchers have found that learners’ second language development is influenced by internal and external individual differences but only few studies have adopted a longitudinal approach. In the present study, I aimed to investigate how several internal and external individual differences were interrelated and whether and how these variables predicted L2 English speaking development in adolescent learners. I conducted a dense longitudinal study with frequent measurements of L2 speaking skills. Learners in the first year of secondary school (11 to 13 years old, n = 48) did a weekly speaking task from September to May. At the start of the study, the participants also did multiple tasks, which measured various individual differences. Spearman correlations were calculated to shed light on the relationships between individual-difference variables, and generalized additive mixed models were used to model learning trajectories over time and to investigate the role of individual differences in this development. Results showed that learners’ speaking scores were predicted by time and prior L2 English receptive vocabulary knowledge, which was the main predictor of L2 speaking skills. Vocabulary knowledge furthermore significantly correlated with measures of out-of-school exposure and motivation. The results showed the key role of vocabulary in the early stages of L2 English learning. Full article

The assimilation–accommodation theory provides a crucial theoretical framework for understanding the neural mechanisms of second language (L2) processing. Chinese characters, as logographic scripts, contain diverse strokes and components with high visual complexity, and their grapheme–phoneme conversion differs fundamentally from alphabetic writing systems. Existing studies have identified unique neural patterns in Chinese language processing, yet a systematic synthesis of L2 Chinese processing remains limited. This review focuses on the brain mechanisms underlying Chinese language processing among L2 learners with diverse native language backgrounds. On the one hand, Chinese language processing relies on neural networks of the native language (assimilation); on the other hand, it recruits additional right-hemisphere regions to adapt to Chinese characters’ visuospatial complexity and grapheme–phoneme conversion strategies (accommodation). Accordingly, this review first synthesizes current brain imaging studies on L2 Chinese processing within this theoretical framework, noting that prevailing paradigms—limited to lexical and sentence-level processing—fail to capture the complexity, hierarchy, and dynamics of natural language. Next, this review examines the application and implications of naturalistic stimuli paradigms in neuroimaging research of L2 Chinese processing. Finally, future directions for this field are proposed. Collectively, these findings reveal neuroplasticity in processing complex ideographic scripts. Full article

Technological advances have enabled the development of innovative educational tools, particularly those aimed at supporting English as a Second Language (ESL) learning, with a specific focus on oral skills. However, pronunciation remains a significant challenge due to the limited availability of personalized learning opportunities that offer immediate feedback and contextualized practice. In this context, the present research proposes the design, implementation, and validation of an immersive application that leverages virtual reality (VR) and artificial intelligence (AI) to enhance English pronunciation. The proposed system integrates a 3D interactive environment developed in Unity, voice classification models trained using Teachable Machine, and real-time communication with Firebase, allowing users to practice and assess their pronunciation in a simulated library-like virtual setting. Through its integrated AI module, the application can analyze the pronunciation of each word in real time, detecting correct and incorrect utterances, and then providing immediate feedback to help users identify and correct their mistakes. The virtual environment was designed to be a welcoming and user-friendly, promoting active engagement with the learning process. The application’s distributed architecture enables automated feedback generation via data flow between the cloud-based AI, the database, and the visualization interface. Results demonstrate that using 400 samples per class and a confidence threshold of 99.99% for training the AI model effectively eliminated false positives, significantly increasing system accuracy and providing users with more reliable feedback. This directly contributes to enhanced learner autonomy and improved ESL acquisition outcomes. Furthermore, user surveys conducted to understand their perceptions of the application’s usefulness as a support tool for English learning yielded an average acceptance rate of 93%. This reflects the acceptance of these immersive technologies in educational contexts, as the combination of these technologies offers a realistic and user-friendly simulation environment, in addition to detailed word analysis, facilitating self-assessment and independent learning among students. 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

Interest in language-learning motivation has been growing recently, particularly in multilingual contexts where individuals acquire additional languages beyond English. Despite increasing the focus on multilingualism within second-language acquisition (SLA) research, less research focuses on the motivational dynamics of multilingual learners in learning languages other than English (LOTE). Addressing this gap, the present study investigates the complex motivational factors influencing multilingual university students in learning French as an additional language and LOTE within the Belgian context. The participants consisted of 121 multilingual university students who were learning French as an additional language and LOTE. Data were collected through questionnaire and semi-structured interviews, and analyzed using a combination of quantitative and qualitative methods to provide a comprehensive understanding of learners’ motivational profile. Findings revealed that multilingual learners’ motivation is multifaceted and dynamic, shaped by a combination of intrinsic interests (e.g., cultural appreciation and personal growth), extrinsic goals (e.g., academic and career aspirations), integrative motives, and prior language-learning experiences. The study also sheds light on the overlapping and evolving nature of motivational patterns and provides nuanced insights into LOTE learning motivation within multilingual settings. Full article

Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and a Rewrite agent dynamically adapts science passages via an explicit message-passing protocol. We implement structured system and user prompts as inter-agent communication schemas to enable real-time content adaptation. The results of an ordinal logistic regression analysis hinted that students may be more likely to prefer texts aligned with their profile, demonstrating the feasibility of multi-agent system-driven personalization and highlighting the need for additional work to build upon this pilot study. Beyond empirical validation, we present a modular multi-agent architecture detailing agent roles, communication interfaces, and scalability considerations. We discuss design best practices, ethical safeguards, and pathways for extending this framework to collaborative agent networks—such as feedback-analysis agents—in K-12 settings. These results advance both our theoretical and applied understanding of multi-agent LLM systems for personalized learning. Full article

In this paper we describe the processes and challenges involved in the design, implementation, and assessment of a small-scale intervention in four primary schools in Ghana’s Central Region that aimed to enhance learners’ mother tongue and bilingual literacy practices whilst at the same time strengthening their sense of cultural identity. Within the framework of Educational Design Research (EDR), this paper describes the steps that were involved in the development process, from context analysis to the design of a locally developed Fante–English bilingual storybook, as well as the formative evaluation of this prototype. This paper shows how to translate contextual findings into a final product, while sharing with the reader important findings for each phase in the process. Formative evaluation in the form of a teacher workshop, surveys, and classroom observations was used. Results showed that, in the opinion of teachers, Fante–English bilingual books can promote learners’ cultural identity, self-awareness, and a sense of prestige when they speak the language. Not only do the books preserve the Fante language and culture, but they show learners that Fante is just as important as English. A second round of formative evaluation showed that additional teacher manual and training could benefit the outcome of the bilingual method. Full article