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- PaperApplied Linguistics24 Jul 2026
INTRODUCING SECOND LANGUAGE ASSESSMENT
Lin Shi, Lianzhen He
This work provides an overview of key concepts and practices in second language assessment.
- PaperarXiv — Language & NLP (cs.CL)23 Jul 2026
DONDO: Open w2v-BERT Speech-Recognition Base Models for African Languages
Paul Azunre
DONDO introduces 21 monolingual and 5 multilingual automatic speech recognition base models covering 27 African language varieties, built on the w2v-BERT 2.0 encoder and fine-tuned on read speech from religious texts. The models achieve word error rates of 10–13% across multilingual families, closing the gap to monolingual performance while enabling language-steering via a one-hot prefix. All models are released under Apache-2.0 to support further fine-tuning and commercial use, potentially benefiting over 100 million first-language speakers.
Original abstract
We present DONDO, a family of open, permissively licensed automatic speech recognition (ASR) base models for African languages, built on the w2v-BERT 2.0 self-supervised speech encoder. DONDO comprises twenty-one monolingual models and five multilingual models spanning twenty-seven language varieties across Ghana, Sierra Leone, Nigeria, Senegal, Kenya and Zimbabwe. Models are fine-tuned primarily on read speech drawn from religious texts, which offer broad, license-clear and orthographically consistent coverage for languages that otherwise lack transcribed audio. We describe a two-step (and, for one family, three-step) learning-rate-annealed fine-tuning procedure that first adapts a shared multilingual model at a high learning rate and then anneals it to recover, and in several cases surpass, strong monolingual baselines. We further describe a lightweight language-conditioning mechanism that injects a one-hot language identity as a sequence of prefix frames prepended to the acoustic features, allowing a single multilingual checkpoint to be steered to a target language at inference. Across the five multilingual families the annealed models reach average word error rates (WER) of 10-13%, closing most of the gap to monolingual models while covering many languages in a single checkpoint. All models are released on the Hugging Face KhayaAI organisation under the Apache-2.0 license (attribution only) so that others may fine-tune them freely, including for commercial use. We provide a conservative estimate that the languages covered are spoken by on the order of one hundred million first-language speakers, and by substantially more when second-language use is included.
- PaperarXiv — AI in Education (cs.CY)23 Jul 2026
AI Assistants Overassist
Verona Teo, Raghav Jain, Tobias Gerstenberg, Max Kleiman-Weiner
Large language models are increasingly used as tutors but tend to intervene too early and too frequently, often providing complete solutions instead of targeted hints. A new benchmark, Int-Bench, evaluates LLM interventions across three domains and finds that LLMs prioritize short-term task success over fostering deep learning. This overassistance contrasts with human behavior and may hinder cognitive engagement and long-term learning.
Original abstract
Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems. While guidance from AI assistants can scaffold thinking and foster learning, such benefits depend on how they help--for instance, intervening too early or too frequently may hinder true learning and cognitive engagement. Yet how AI systems navigate intervention decisions during problem-solving remains poorly understood. Here, we introduce Int-Bench, a simulation-based benchmark for evaluating LLM interventions during learning. Int-Bench simulates a "student" solving a problem while a "teacher" monitors the student's reasoning and decides whether, when, and how to intervene. Across three domains--code debugging, mathematics, and brain teasers--we evaluate LLM teachers on the frequency and timing of interventions, as well as their impact on both immediate task success and generalization to new problems. We also compare LLMs to humans, finding that LLMs intervene more frequently and earlier than humans. Moreover, in contrast to humans, they tend to provide complete solutions rather than targeted hints. These findings suggest that current LLM assistants often optimize for short-term success rather than supporting the reasoning processes needed for deeper learning and long-term success.
- PaperLanguage Teaching Research23 Jul 2026
When Teacher Enthusiasm Meets Student Engagement: The Reciprocal Role of Perceived Learning Value in the Second Language Classroom
Hoi Vo, Hang Ho, Dung Mai, Clarence Ng
A longitudinal study of 317 Vietnamese EFL learners found that teacher enthusiasm and student engagement are reciprocally related over time, mediated by students' perceived value of L2 learning tasks. This reciprocal relationship remained robust after controlling for prior levels of each construct, highlighting the co-regulated and cyclical nature of the L2 classroom affective-motivational climate.
Original abstract
Second language (L2) classroom is a dynamic and evolving space shaped by ongoing interactions between teachers and students. Understanding how these dynamic interactions unfold from a psychological perspective is critical for advancing L2 learning and teaching. This study contributes to this endeavour by examining the reciprocal relationship between teacher enthusiasm and L2 student classroom engagement as well as the mediating role of perceived learning value in this reciprocal relationship. Data were collected from 317 English as a foreign language learners in Vietnam at three time points across an academic semester and analysed by means of random-intercept cross-lagged panel modelling. Results revealed that teacher enthusiasm and L2 student engagement were reciprocally related over time, and this reciprocity was mediated by students’ perceived value of L2 learning tasks. These mediated reciprocal relations remained robust even after controlling for prior levels of each construct. The findings highlight the co-regulated and cyclical nature of L2 classroom affective-motivational climate and offer practical implications for L2 pedagogy.
- PaperLanguage Teaching Research23 Jul 2026
The Activity Design Typology: A Practitioner-Centered Approach to Understanding Task Structure
Lara Bryfonski, Caitlyn Pineault
An analysis of 114 activities created by world language teachers in a task-based teacher education program revealed that traditional task definitions inadequately capture their design intricacies. A directed content approach identified five distinct activity categories, leading to the Activity Design Typology, which offers a practitioner-centered framework for understanding, explaining, and evaluating instructional tools.
Original abstract
As the central construct in task-based language teaching (TBLT), “task” has been defined in a myriad of ways. Though popular criterion-based definitions offer a tantalizingly straightforward description of the construct, they have, at times, proved difficult to apply in practitioner contexts. With over 17 definitions identified in the literature, prominent scholars have advocated for efforts that offer a more unified understanding of “task” that may be better able to support task-based language teaching in implementation initiatives. This exploratory, qualitative study examines how two cohorts of K–16 U.S.-based world language teachers ( n = 35) approached task design in a task-based teacher education program from a materials development perspective. An analysis of the 114 activities teachers created revealed that although submissions had task characteristics, the intricacies of their designs were not adequately captured by traditional task criterion. Subsequent analysis following a directed content approach identified five distinct categories of activity design. Advancing previous interest in expanding the binary distinction of “task” versus “non-task,” we drew on these categories to develop the Activity Design Typology. The typology serves as a resource for understanding, explaining, and evaluating instructional tools for their pedagogic and empirical value. Use cases for the task typology are discussed in relation to initiatives aimed to further instructed second language acquisition research agendas and support TBLT implementation efforts.
- PaperLanguage Teaching Research23 Jul 2026
Approaches to Boost Foreign Language Learners’ Positive Emotions: A Qualitative Study on Learners’ and Teachers’ Perspectives in the Modern Foreign Language Context in England
Pia Resnik, Christine Schallmoser, Jean-Marc Dewaele
A qualitative study of secondary-level teachers and learners in England reveals that teachers significantly impact fostering positive emotions in foreign language classrooms, while students lack awareness of language learning benefits beyond English. The findings also highlight the need for government, schools, and parents to elevate the status of modern foreign languages to promote positive social change.
Original abstract
This study builds on insights into the crucial role learner emotions play in their flourishing. It investigates learners’ and teachers’ perspectives on how to enhance positive emotions both inside and outside modern foreign language classrooms in England, amidst a language crisis and reduced modern foreign language uptake across the United Kingdom. A thematic qualitative text analysis of 18 semi-structured interviews with secondary-level teachers and learners from 7 schools across England revealed that both learners and teachers emphasise the significant impact of teachers on fostering positive emotions in the classroom. They also showed that students lack awareness of the benefits of learning languages beyond English. Furthermore, the interview data suggests that the government, schools, school leaders, and parents must also engage to elevate the status of modern foreign languages and promote positive social change.
- PaperOpenAlex — TESOL research22 Jul 2026
Gamification in English Language Teaching: Effects on Vocabulary Acquisition Among First-Year Secondary School Students
Elfer Fabian Medina-Mamani, Diomar Aneska Jove-Luque, Kevin Mario Laura-De La Cruz, Pilar Valeria Zapana Aguilar et al.
A study examined the effects of gamification on vocabulary acquisition among first-year secondary school students in English language teaching, finding positive impacts.
- PaperTESOL Quarterly22 Jul 2026
Unveiling the AI Divide: A Comparative Study of Novice and Veteran EFL Teachers' Experience of Using Generative AI in Teaching
Qi Lin
A comparative study of novice and veteran EFL teachers reveals that the AI divide emerges mainly at the levels of AI capability and AI outcomes rather than access, shaped by factors such as perceived ease of use, educational background, social support, and professional development opportunities.
Original abstract
The rapid advancement of artificial intelligence (AI) has brought both opportunities and challenges to the field of education. Within this evolving landscape, the emergence of the AI divide has become a pressing concern, particularly in teacher education and EFL instruction. This study developed a three‐level three‐factor conceptual framework to explore the AI divide through the dimensions of AI access, AI capability, and AI outcomes, while also considering technological, personal, and institutional influences. Using this framework, the study employed a comparative research design to examine how novice and veteran EFL teachers integrated AI differently in their teaching practices. The findings revealed that the AI divide between novice and veteran EFL teachers mostly occurred at the second level (AI capability) and third level (AI outcomes), rather than the first level (AI access). These disparities were shaped by a combination of technological, personal, and institutional factors, including perceived ease of use of AI, educational background, social support, and access to AI‐related professional development. This study extends the concept of AI divide into the field of EFL education and offers valuable theoretical insights and practical implications for more equitable AI integration in EFL teaching.
- PaperOpenAlex — TESOL research22 Jul 2026
AI in language education
Jake Cummings
AI tools in second language acquisition promise efficiency and personalization but are often disconnected from authentic learning and equity. Teacher preparation programs lag behind, leaving educators underprepared. This chapter analyzes AI through Communities of Inquiry and Second Language Acquisition frameworks, finding that educational value depends on teacher digital competence and critical use.
Original abstract
AI tools are increasingly embedded in second language acquisition (SLA), yet their integration raises unresolved tensions between technological potential and pedagogical practice. While AI applications promise efficiency, personalization, and extended opportunities for interaction, these affordances are often overstated or disconnected from authentic learning, cultural exchange, and equity. At the same time, teacher preparation programs have been slow to adapt, leaving teachers underprepared to critically evaluate AI-mediated language learning environments. This chapter examines AI in SLA through the interpretive frameworks of the CoI and SL2, positioning teacher digital competence as a mediating construct between technological capability and educational value. Traditional SLA approaches provide historical context, while adaptive systems, chatbots, and GenAI are analyzed in terms of presence, interaction, authenticity, and cultural depth. Three themes emerge: Expanded opportunities for cognitive and social presence, persistent risks related to inequity, and the central role of teacher judgment. The chapter concludes that the educational value of AI depends less on technical innovation than on the preparedness of teachers to use it critically, ethically, and inclusively to design and mediate AI-supported language learning experiences.
- PaperOpenAlex — TESOL research22 Jul 2026
Evidence for the Development of L2 Grammar‐for‐Interaction: The Case of Wenti shi (“the Problem Is”) in L2 Chinese Interaction
Di Lan, Shuangyun Yao
Traced the longitudinal development of an L2 Chinese learner's use of 'wenti shi' over 15 months using CA-for-SLA, UBL, and IL. Found functional expansion from literal problem-stating to topic management and disagreement preface, with changes in syntax, prosody, and sequential positioning. Demonstrates how grammar-for-interaction emerges from semantic persistence and interactional needs even in low-frequency constructions.
Original abstract
This study adopts an integrated framework combining Conversation Analysis for Second Language Acquisition (CA‐for‐SLA), Usage‐Based Linguistics (UBL), and Interactional Linguistics (IL) to trace the longitudinal development of an L2 Chinese learner's use of the construction wenti shi (“the problem is”, WTS) over a 15‐month period. Although quantitative analysis shows that the overall frequency of WTS did not increase substantially, qualitative findings reveal a clear developmental trajectory. Initially employed in its literal sense to present a factual problem, WTS was later mobilized to foreground an issue by upgrading the speaker's epistemic stance and projecting further talk. Building on this core function, two trajectories of functional expansion emerged: one oriented to managing topic transition and the other to prefacing contrasting stance and, eventually, disagreement. These functional developments were accompanied by changes in the construction's syntactic formatting, prosodic chunking, and sequential positioning, each unfolding at a different pace. By tracing the developmental trajectory of WTS, the study demonstrates how grammar‐for‐interaction emerges through the interplay between semantic persistence and interactional needs, while also showing that discourse‐marker‐like development can emerge even in constructions with relatively low token frequency.
- PaperLanguage Teaching Research22 Jul 2026
AI-Assisted Digital Storytelling in EFL Teacher Education: A Qualitative Case Study of Digital and Multimodal Literacy Development
Bessie Mitsikopoulou
A qualitative case study examined how EFL student teachers used generative AI tools (text-to-image, video editors, speech synthesis) to collaboratively design digital stories. Analysis of 69 reflective papers found that algorithmic limitations prompted students to become critical designers of meaning, with the human element remaining essential for narrative coherence.
Original abstract
This qualitative case study explores how English as a foreign language (EFL) student teachers engaged with generative artificial intelligence (GAI) in the collaborative design of digital stories within an advanced undergraduate course in 2024. During the semester, participants critically evaluated and applied various AI applications, such as text-to-image generators, automated video editors and speech synthesis tools, to support different stages of multimodal story production, including script drafting, visual design, audio narration and video editing. Drawing on a corpus of 69 reflective papers (approximately 75,000 words), the study examines how student teachers integrated visual, auditory and textual elements to achieve multimodal cohesion and narrative coherence. The analysis focuses on participants’ practices and strategies for aligning modes, maintaining consistency across media, and adapting to the constraints of AI-generated content. Findings indicate that the project’s primary pedagogical significance lay not in technical efficiency but in the productive ‘friction’ generated by algorithmic limitations, which led participants to move beyond passive tool use and become critical designers of meaning, demonstrating sophisticated multimodal awareness. While AI supported rapid content generation, the human element remained the narrative’s essential ‘emotional glue’, ensuring affective resonance and coherence. Overall, the study suggests that experiential engagement with AI-assisted composition may enable future educators to move beyond functional skills and develop into critically reflective architects of digital narratives.
- PaperRELC Journal22 Jul 2026
Translanguaging for critical multilingual language awareness: Classroom activities for socially just language education
Jennifer Burton, Shakina Rajendram
Introduces a Translanguaging for Critical Multilingual Language Awareness (TL-for-CMLA) approach, operationalized through two concrete pedagogical activities: multilingual versions of Little Red Riding Hood for metalinguistic awareness and multilingual spoken word poetry for identity exploration. The activities challenge target-language-only assumptions and engage learners critically and affectively, aiming to foreground power, racialization, and linguistic hierarchies in language education.
Original abstract
This article introduces a Translanguaging for Critical Multilingual Language Awareness (TL-for-CMLA) approach and illustrates how it can be operationalized through concrete pedagogical activities in language teacher education and additional language classrooms. The paper argues for an explicitly critical orientation that foregrounds power, racialization, and linguistic hierarchies rather than treating multilingualism as a neutral resource. The purpose of this innovation in practice article is to present two classroom activities that bridge critical language ideologies and everyday teaching practice in English as a Second Language (ESL) and Kindergarten-Grade 12 (K-12) classrooms with multilingual learners. The first activity uses multilingual versions of Little Red Riding Hood to develop metalinguistic awareness and challenge target-language only assumptions. The second activity centers multilingual, identity focused spoken word poetry to engage learners affectively, critically, and multimodally with questions of language, identity, and belonging. Together, these activities present adaptable examples, reflection prompts, and underlying rationales that can be applied across proficiency levels and educational contexts. This paper contributes theoretically by clarifying TL-for-CMLA and pedagogically by offering adaptable activities that support socially just language education.
- PaperOpenAlex — TESOL research22 Jul 2026
Second language embodiment in actor training through mindful practice
Kristýna Zinková
Applied Practice as Research with EAL acting students to explore how a second language can become disembodied and affect performative authenticity. Found that training linguistic awareness through mindful practices like Dialogical Acting enhances second language embodiment. Supports integrating deep linguistic embodiment into multilingual acting curricula.
Original abstract
At the turn of the twenty first century, the Embodied Cognition Theory emerged, positing that human cognition, including language, is inherently embodied. This theory suggests that even imagining movement can activate muscles similarly to actual movement, highlighting the mind and body as an interconnected system. However, it raises a question: are second languages also embodied? While this theory has intrigued performative sciences, more rigorous research is required. To investigate this matter, I applied Practice as Research as part of my Master’s Thesis at the Academy of Performing Arts in Prague. Working with EAL acting students,I explored how, or why, a second language (English) can be “disembodied” and how this affects performative authenticity. My findings revealed that linguistic awareness may be a vital tool for enhancing performance. The research incorporated the Academy's practice Dialogical Acting with The Inner Partner, investigating how well awareness can be trained in order to increase the embodiment of a second language. By fostering awareness in second-language acquisition, including sound and rhythm in life-like situations, performers can deepen their understanding of linguistic systems and enhance their personal linguistic capabilities. This research supports integrating deep linguistic embodiment into multicultural and multilingual acting curricula, offering a pathway to more authentic performances.
- PaperOpenAlex — TESOL research22 Jul 2026
Analysis of challenges and opportunities in english language teaching to improve its methodologies at CEBG Nuevo San José, 2025
Benicio Ramón Chacón Rodríguez
Analyzes challenges and opportunities in English language teaching at CEBG Nuevo San José, a rural multigrade school lacking specialized teachers and resources. Uses interviews, observations, and surveys to understand student attitudes and proposes recommendations for improving language learning in underserved contexts.
Original abstract
La dirección principal de esta investigación es analizar e investigar los desafíos y oportunidades de la enseñanza del inglés que se presentan en el CEBG Nuevo San José, una escuela de entorno rural multigrado, en la actualidad no se cuenta con un docente especializado en la materia. Esta carencia, acompañada a la falta de recursos didácticos, dificulta al docente de grado regular que pueda impartir una enseñanza de calidad a un grupo de alumnos de diferentes niveles en la misma aula. Esto representa un gran obstáculo para que los estudiantes progresen en su aprendizaje y en su desarrollo académico. La adquisición de una segunda lengua es un instrumento esencial para su desarrollo profesional y educativo futuro, pero se ve limitada debido a que no reciben la orientación adecuada. Esta investigación utiliza diferentes instrumentos, como entrevistas, observaciones de profesores y alumnos, y encuestas, para comprender cómo se sienten los alumnos respecto al aprendizaje de un nuevo idioma. También busca explorar las cualidades de los estudiantes y la necesidad de utilizar tecnologías y herramientas reales que ayuden a mejorar sus habilidades de aprendizaje de idiomas de una manera real, sostenible y organizada en entornos rurales multigrado. El objetivo principal de esta investigación es generar recomendaciones claras sobre la importancia del aprendizaje de idiomas en entornos donde hay escasez de profesores especializados y promover políticas educativas más inclusivas.
- PaperOpenAlex — TESOL research22 Jul 2026
Inclusive English Language Teaching: Bibliometric Analysis of Multilingualism and Inclusive Classroom Practices
Cristine Ombao
A bibliometric analysis of 56 articles from Scopus (2017-2025) mapped publication trends, leading contributors, and thematic clusters in multilingualism and inclusive classroom practices in English Language Teaching. The United States and Indonesia led research output, with inclusive education and ELT as dominant theoretical anchors. Underdeveloped areas include decolonization and disability-inclusive pedagogy.
Original abstract
This bibliometric analysis examined published research on multilingualism and inclusive classroom practices in English Language Teaching retrieved from the Scopus database from 2017 to 2025. Guided by three research questions, the study mapped publication trends, leading journals, authors, institutions, and dominant thematic clusters across 56 articles using VOSviewer for keyword co-occurrence visualization. The findings revealed a steady acceleration in publication output over the analyzed window, with inclusive education and English language teaching emerging as the dominant theoretical anchors of the field. The United States and Indonesia led the field in research output, followed by a globally distributed tier including Poland, Iran, Spain, and South Africa. The University of Warsaw (3 publications) and Isfahan University of Technology (2 publications) ranked as the most productive institutions, while Chan and Lo, Lau and Shea, and Nijakowska et al. registered as the most cited authors. The keyword analysis clustered scholarship around inclusive, multilingual, EFL/ESL, and higher education strands, with peripheral domains such as decolonization and disability-inclusive pedagogy remaining underdeveloped. The findings provide a foundation for advancing evidence-based, multilingual, and equitable ELT research through the next decade.
- PaperOpenAlex — TESOL research22 Jul 2026
ENHANCING FOREIGN LANGUAGE TEACHING METHODOLOGY THROUGH ARTIFICIAL INTELLIGENCE: A CASE STUDY OF ENGLISH LANGUAGE LEARNING
Farmonova Naimakhon Furkat kizi
AI tools like intelligent tutoring systems and adaptive learning platforms significantly improve vocabulary acquisition, communicative competence, and learner autonomy in English language teaching, though challenges such as teacher readiness and ethical concerns persist.
Original abstract
Artificial Intelligence (AI) is rapidly transforming educational practices, particularly in the field of foreign language teaching. This study explores how AI can enhance English language teaching methodology in higher education. Using a mixed-method research design, the study evaluates the impact of AI-based tools such as intelligent tutoring systems, natural language processing applications, and adaptive learning platforms on students’ language proficiency. The findings reveal that AI significantly improves vocabulary acquisition, communicative competence, and learner autonomy. However, challenges related to teacher readiness, infrastructure, and ethical considerations remain. The study contributes to the development of modern AI-based pedagogical frameworks.
- PaperTESOL Quarterly22 Jul 2026
Pedagogical Translanguaging in Motion: Multimodal Mediation in Preservice Teachers' Storytelling with Young English Learners
Sue Garton, Serdar Tekin
This study investigated how eight preservice English teachers in Turkish primary schools used translanguaging and multimodal strategies during storytelling lessons over eight weeks. Initially, teachers relied on shared linguistic repertoires to manage interaction and scaffold comprehension, but over time they shifted toward increased use of gesture, visuals, and realia while verbal translanguaging became more selective. The findings provide classroom-based evidence of how translanguaging practices evolve and offer implications for multilingual pedagogy and teacher education.
Original abstract
Amid growing interest in multilingual pedagogies, translanguaging has increasingly been positioned as a response to monolingual teaching norms. Yet empirical accounts of what pedagogical translanguaging looks like in everyday classrooms—particularly in teaching English to young learners—remain limited. At the same time, although storytelling occupies a central place in young learner pedagogy, little research has examined how teachers mobilize linguistic and semiotic resources during storytelling lessons. Addressing both gaps, this study draws on the pedagogical translanguaging framework and the Dynamic Model of Multilingualism to investigate the evolving practices of eight preservice English teachers (PSTs) in Turkish state primary schools. The data set comprises 32 video‐recorded storytelling lessons over 8 weeks and follow‐up in‐depth interviews. Findings show that PSTs initially drew on shared linguistic repertoires to manage classroom interaction and scaffold story comprehension. Over time, however, these practices shifted, and translanguaging use decreased as PSTs increasingly employed multimodal strategies, including gesture, visual supports, and realia. Findings suggest how teachers' mediational practices became increasingly adaptive, with verbal translanguaging becoming more selective alongside increased use of multimodal mediation. The study contributes classroom‐based evidence of how translanguaging evolves over time and offers implications for multilingual pedagogy and teacher education in young learner contexts.
- PaperOpenAlex — TESOL research22 Jul 2026
Learners’ perceptions and challenges of microlearning in Indian ESL tertiary education
K. Arunesh Kumar, N. Hema
A quantitative study of 48 Indian ESL undergraduates found positive perceptions of microlearning's flexibility and interactivity, but identified challenges such as poor internet connectivity and content fragmentation. A significant negative correlation between perceptions and challenges indicates that greater obstacles worsen attitudes toward microlearning, highlighting the need for context-sensitive technology integration.
Original abstract
The swift digitalization of higher education has fuelled the curiosity surrounding microlearning as a new form of pedagogy in the field of English Language Teaching (ELT), particularly English as a Second Language (ESL) instruction. Although microlearning is increasingly used globally, little empirical evidence has investigated on how learners perceive microlearning in the context of tertiary level ESL learners in India. This paper explores perceptions of microlearning among ESL learners at the tertiary level, challenges faced by these learners, and the correlation between their perceptions and challenges. Using a quantitative research design, a study involving 48 Indian ESL undergraduate students was conducted, where a structured questionnaire with perception and challenge scales was administered. Data analysis was done using descriptive statistics, reliability analysis, and Pearson correlation. The results indicate that students tend to have positive attitudes towards microlearning, especially its flexibility, interactivity, and vocabulary/grammar acquisition support. Nevertheless, there are still important limitations, including the lack of internet connectivity and issues with content depth and fragmentation. The correlation between the perceptions of learners and the challenges met was statistically significant and negative, denoting that greater impediments have a negative impact on attitudes towards microlearning. The research highlights the significance of technology integration that is learner-focused and context-related and provides pedagogical implications in support of educators, curriculum developers, and policymakers aiming to maximize the use of microlearning in Indian tertiary ESL institutions.
- PaperOpenAlex — TESOL research22 Jul 2026
THE TRANSFORMATIVE ROLE OF ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING
Madaminova, Nargizaxon Jahongir qizi,
The integration of artificial intelligence (AI) into English Language Teaching (ELT) is prompting a reevaluation of curriculum development, learner assessment, and teacher responsibilities. A thematic analysis reveals a shift toward personalized learning and data-informed curricula, suggesting AI will redefine teachers' roles and learning objectives rather than replace traditional instruction.
Original abstract
The integration of artificial intelligence (AI) into English Language Teaching (ELT) signifiesa critical evolution in both pedagogical practice and instructional design. As AI technologiesrapidly develop and become widely accessible, educators and researchers are reevaluating thefoundational components of language instruction, including curriculum development, learnerassessment, teacher responsibilities, and communicative goals. This article explores themedium-term implications of AI in ELT, identifying key challenges and opportunities arisingfrom its implementation. A thematic analysis of current discourse in the field reveals a shifttoward personalized learning, data-informed curricula, and the need for new literacies amongboth teachers and learners. The findings suggest that AI will not replace traditional instructionbut rather redefine the teacher's role and reshape learning objectives.
- PaperEdArXiv (OSF Preprints)21 Jul 2026
Changes in Motivational Beliefs and Science Identity During an AI-Assisted Reading of Primary Scientific Literature
Sebastian Alvarado, Bradley Bergey, Leif Philips, Maral Tajerian
Undergraduate biology students using the AI tool Scite to read primary scientific literature showed increases in intrinsic, utility, and attainment values and science identity, alongside an unexpected rise in perceived costs. The study, guided by Expectancy-Value Theory, suggests complex motivational implications of AI-assisted engagement with primary scientific literature.
Original abstract
Reading primary scientific literature is essential yet challenging for undergraduate science students, who often report low confidence and difficulty navigating complex texts. Guided by Expectancy-Value Theory, this mixed-methods study examined changes in students' motivational beliefs across a biology literature review assignment in which students used Scite, an AI-powered citation analysis tool, including science identity; self-efficacy; and intrinsic, utility, and attainment values and perceived costs of reading primary scientific literature. Students enrolled in a 200-level Foundations of Research in Biology course were assigned a structured literature review on pharmacological interventions for a disease of their choice, using Scite.ai to locate, evaluate, and synthesize primary research. A racially diverse sample of 27 undergraduate biology students (48% women, 42% first-generation college students) completed a posttest questionnaire, including a subsample (n = 21) with matched pretest questionnaire responses. Paired-sample t tests revealed statistically significant, medium-sized increases in intrinsic, utility, and attainment values and science identity, alongside an unexpected rise in perceived costs (d = .51-.65); results revealed a small nonsignificant increase in self-efficacy. Students rated Scite as highly useful for identifying, summarizing, and synthesizing research. Thematic analysis of open-ended responses revealed the salience of perceived utility, enjoyment, cognitive support, and credibility, alongside concerns about reliability, ease of use, and occasional identity-related conflicts. Findings suggest that AI-assisted engagement with PSL carries complex motivational implications, warranting controlled investigation.
- PaperEdArXiv (OSF Preprints)21 Jul 2026
Toward Responsible AI Implementation in K–12 Music Education: A Human-Centered Conceptual Framework Informed by Emerging U.S. Educational Priorities
Jose Gregorio Reinoso Yepez
A conceptual framework for responsible AI implementation in K–12 music education is developed, drawing on U.S. educational priorities and international governance. The framework emphasizes human-centered design, pedagogical integrity, teacher agency, AI literacy, and ethical governance. While focused on music, the principles may apply to other K–12 subjects.
Original abstract
Artificial intelligence (AI) is increasingly reshaping K–12 education, influencing curriculum design, instructional practice, and educational governance. Emerging U.S. priorities emphasize not only technological innovation but also responsible implementation, AI literacy, data privacy, and equitable access. These priorities are especially relevant to K–12 music education, where learning depends on cognitive, artistic, emotional, and social dimensions requiring sustained teacher guidance. Although AI-supported tools offer promising applications for adaptive practice, automated feedback, and AI-assisted composition, their value depends on implementation conditions that preserve pedagogical integrity and teacher agency. This conceptual article develops a Human-Centered Conceptual Framework for Responsible AI Implementation in K–12 Music Education, drawing on U.S. priorities, international governance frameworks, and contemporary scholarship. The framework positions AI as a support system within an educational ecosystem shaped by pedagogy, educator leadership, AI literacy, ethical governance, professional capacity building, and continuous improvement, offering a conceptual foundation for future research, policy, and practice in music education and other K–12 settings.
- PaperEdArXiv (OSF Preprints)21 Jul 2026
From Environmental Noise to Pedagogical Signal: Analyzing the Educational Potential of Strategic Cinema in Representing Teachers' Professional Crises (A Case Study of Social Studies Pre-Service Teachers)
Hossein Talebzadeh
A qualitative study evaluated the 'Image Pedagogy' framework using strategic films as pedagogical laboratories for social studies pre-service teachers. Exposure to cinematic narratives improved their ability to differentiate 'environmental noise' from 'pedagogical signals' based on the Signal-to-Noise Ratio model, fostering a cognitive shift from emotional descriptivism to strategic analysis. The approach provided a safe environment for encountering classroom crises and enhanced managerial competencies under uncertainty.
Original abstract
This qualitative study was conducted to evaluate the operational framework of "Image Pedagogy." Utilizing an Observation–Analysis–Synthesis protocol, three strategic films were analyzed as dedicated pedagogical laboratories. The findings indicate that exposure to cinematic narratives significantly improves pre-service teachers' capability to differentiate "environmental noise" from "pedagogical signals" based on the Signal-to-Noise Ratio (SNR) model, inducing a cognitive transformation from "emotional descriptivism" to "strategic analysis." The results demonstrate that this approach, by redefining "professional experiences," provides a safe environment for encountering classroom crises and enhances educators' managerial competencies under conditions of acute uncertainty.
- PaperarXiv — AI in Education (cs.CY)21 Jul 2026
Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks
Rawaa Alatrash, Mohamed Amine Chatti, Hong Yang, Yumeng Wang
Introduces MR-ConceptGCN, an unsupervised method for sequential learner modeling using multi-relational graph convolutional networks. It combines personal knowledge graphs and sentence embeddings to capture learner interactions and generate sequential models. An online study with 31 users demonstrated improvements in accuracy, usefulness, diversity, and satisfaction for an educational recommender system.
Original abstract
User modeling is a critical task in a variety of personalized systems. Recognizing their effectiveness in learning from graph-structured data, Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), are increasingly employed for user modeling. However, existing approaches typically treat different relation types in a graph as homogeneous, limiting their ability to capture richer semantics and construct more informative user models. While multi-relational GNNs (MR-GNNs) have been adopted for representation learning and recommendation, their application for user modeling remains unexplored. Moreover, existing GNN-based user modeling approaches ignore the user interaction sequence. To address these research gaps, in this work we propose MR-ConceptGCN, a novel fully unsupervised approach focused on concept-based sequential learner modeling using multi-relational GCNs (MR-GCNs). MR-ConceptGCN effecively combines Personal Knowledge Graphs (PKGs), MR-GCNs, and the pre-trained language model SBERT to obtain enhanced relation- and semantic-aware representations of the PKG items. The enriched embeddings of the knowledge concepts that a learner did not understand when interacting with learning materials in CourseMapper are then used to construct a sequential learner model that combines long-term and short-term learner interactions. We report the results of an online user study (n = 31), demonstrating the benefits of MR-ConceptGCN in terms of several important user-centric aspects including accuracy, usefulness, diversity, and satisfaction with an educational recommender system.
- PaperarXiv — AI in Education (cs.CY)21 Jul 2026
Assessment in Team Problem-Solving Exercises in Computing Education
Valdemar Švábenský, Jan Vykopal, Sukrit Leelaluk, Pavel Čeleda et al.
Compares clustering and large language models for post-exercise team assessment in tabletop exercises, using data from 81 participants. Clustering proved valid and reliable for grouping teams by task approach, while newer LLMs (GPT-5.2) showed lower error than GPT-4o when scoring communication. Methods are integrated into the open-source INJECT platform to support scalable assessment in computing education.
Original abstract
This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs). TTXs enable learner teams to prepare for workplace tasks and practice crisis responses, such as resolving cybersecurity incidents. While assessment is essential for determining how well teams achieve learning objectives, the complex, open-ended nature of TTXs often leads to delayed or incomplete feedback. TTX learning platforms can record teams' actions and communication; yet, leveraging these data to assess performance is underexplored. To address this gap, we compared two post-TTX team assessment methods -- clustering and large language models (LLMs) -- using an original dataset from 81 participants across two countries. We evaluated these methods against instructor-assigned scores based on standardized rubrics. Clustering grouped teams that approached TTX tasks similarly, enabling instructors to deliver faster, targeted feedback to teams within a cluster. This method was valid and reliable, with low computational requirements. LLMs used the standardized rubrics to assess teams' communication. While GPT-4o frequently disagreed with instructor scores, GPT-5.2 demonstrated considerably lower error. The researched methods have been integrated into INJECT, an open-source TTX learning platform, to support scalability and teaching practice. To encourage community adoption, we publicly share all datasets, software tools, and a full-fledged TTX scenario.
- PaperRELC Journal21 Jul 2026
Enhancing English Public Speaking Self-Efficacy and Performance Through a CDIO-Based English Course
Yen-Hui Lu
This quasi-experimental study integrated the CDIO framework into English public speaking instruction for EFL university students in Taiwan. The experimental group showed significantly higher self-efficacy and superior performance in content, organization, and delivery compared to traditional training, but no improvement in language accuracy. Pedagogical implications include using authentic project-driven tasks and supplementing with language-focused interventions.
Original abstract
This study investigates the impact of integrating the Conceive–Design–Implement–Operate (CDIO) framework into English public speaking (EPS) instruction for English as a foreign language (EFL) university students in Taiwan. EPS has been widely recognized as a critical competence in academic and professional contexts, yet EFL learners often struggle with anxiety, limited practice opportunities and low self-efficacy. To address these challenges, the study implemented a quasi-experimental design with two Freshman English classes: a control group receiving traditional presentation training; and an experimental group engaged in a CDIO-based presentation training. Over an 18-week semester, students’ self-efficacy was measured using a validated EPS self-efficacy scale, while performance was evaluated with a rubric assessing language use, content, organization and delivery. Pre-test results showed no significant differences between groups, ensuring baseline equivalence. Post-test findings revealed that the experimental group demonstrated significantly higher self-efficacy, as well as superior performance in content development, organizational structure and delivery techniques. However, no significant improvements were found in language accuracy and fluency. These results suggest that CDIO-based instruction enhances students’ confidence and competence in public speaking through authentic, project-driven tasks. The findings yield three key pedagogical implications: (a) integrating authentic, product-based tasks such as sustainable innovation pitches can foster mastery experiences and boost learner motivation and self-efficacy; (b) using CDIO's four-phase structure enables instructors to scaffold content and organizational development through guided brainstorming, outlining and rehearsed delivery; and (c) to address limitations in grammar and vocabulary acquisition, supplementary language-focused interventions such as vocabulary mini-tasks and grammar workshops should be embedded within the CDIO cycle to balance fluency with accuracy. These strategies can optimize the effectiveness of CDIO-based EPS instruction in EFL contexts.
- PaperOpenAlex — TESOL research21 Jul 2026
Methodological guidelines for english language teaching to help parents and educators at CEBG José Marciano Moreno
Manuel Antonio Cárdenas Aguirre, Patrick Alexis Gil Meneses
Designed a participatory English language training program for parents and educators at a multi-grade school in Panama, addressing the lack of specialized teachers and limited parental support. Four interactive sessions covered the importance of English, daily integration strategies, reading techniques, and building a support network. Outcomes included improved teacher methodologies, empowered parents, and a motivational mural symbolizing community unity.
Original abstract
Este proyecto está centrado en el diseño de un programa participativo de formación del idioma inglés, dirigido a padres y educadores del CEBG José Marciano Moreno, en la comunidad de Buen Retiro, Antón, provincia de Coclé. Esta iniciativa surgió ante la problemática de las escuelas multigrado-panameñas, en donde carecen de docentes especializados, y esto le sumamos el limitado apoyo de los padres debido a su desconocimiento del idioma. El objetivo principal es crear una red de apoyo sostenible para mejorar la calidad de la enseñanza, pero sobre todo empoderar a los padres con herramientas prácticas que sirvan como guía en el aprendizaje del idioma inglés. Teniendo como intervención un diagnóstico inicial de necesidades y la implementación de cuatro sesiones de capacitación interactiva. Estas sesiones abordaron temas como la importancia del inglés, estrategias para incorporarlo en la vida diaria, técnicas de lectura y la creación de una red de apoyo. Como resultado, se fortalecieron las habilidades metodológicas de los docentes, se empoderó a los padres con herramientas prácticas y se fomentó la creación de un mural motivacional que simboliza la unión de la comunidad. Este estudio de caso, con enfoque participativo, demuestra que la implicación conjunta de familias y educadores es clave para superar las limitaciones estructurales.
- PaperOpenAlex — TESOL research21 Jul 2026
The Impact of AI Empowerment on the Development of College Students' English Speaking Skills: A Mixed-Methods Analysis Based on Speech Competence Assessment and Self-Directed Learning Efficiency
Jian Li, Luo ZhiYao
A mixed-methods study with 20 Chinese college students found that using the Doubao AI platform for autonomous speaking practice significantly improved fluency, pronunciation, and lexical resources. AI empowerment enhanced self-directed learning efficiency through synergy between self-determination theory and self-regulated learning, though a 'situational gap' between virtual practice and real interaction persisted.
Original abstract
In the context of the digital transformation of global education, artificial intelligence (AI) has become a transformative force in the field of second language acquisition. This study explores the impact of AI empowerment on the development of Chinese college students’ oral English competence and the efficiency of self-directed learning (SDL). The core of the research is to explore the synergy between the self-determination theory (SDT) and the self-regulated learning (SRL) model to clarify the interaction between technology empowerment and psychological mechanisms. Under this framework, SDL is the main learning mode, while SDT provides motivational basis (answering students “why” initiates SDL), and SRL provides strategic guarantee (answering students “how” manages learning process). This study adopts a mixed research method and conducts a six-week quasi-experiment with 20 International Business English majors students from Guangdong University of Foreign Studies. During the winter vacation, participants used the Doubao AI platform for autonomous speaking practice and received instant, data-driven feedback. The data were comprehensively analyzed through pre-test and post-test (measuring fluency, lexical resources, accuracy and pronunciation), validated questionnaires and qualitative interviews. The results show that AI empowerment significantly improves objective speaking ability, especially in terms of fluency, pronunciation and lexical resource. More importantly, the study found that there is a deep synergistic effect between the learner’s motivation process and the strategy process: the satisfaction of the two basic psychological needs of autonomy and ability (SDT) provides the internal motivation for students to enter the three cycle stages of SRL; the effective strategy execution guided by the role of AI “virtual supervisor” further strengthens the learners’ sense of achievement, thus jointly improving the overall SDL efficiency. Despite these advances, the study also found a “situational gap” between virtual practice and real social interaction. This study highlights the importance of an AI-supported teaching model that combines technical efficiency with human-led strategic guidance to optimize the self-directed development of oral English in the digital age.
- PaperOpenAlex — TESOL research21 Jul 2026
The Role of Artificial Intelligence in Lesson Planning and the Development of English Teaching Methods: Teachers’ Perspectives
Azza Elmadani, Shiraz Al-Ali, Salma Mohammed Osman Elias, Mudathir Yousif Mohamed Ahmed et al.
Artificial Intelligence (AI) is transforming English Language Teaching by aiding lesson planning, instructional design, material development, personalized instruction, assessment, and classroom interaction. Teachers report that AI saves time, offers differentiated instruction options, and provides adaptive materials, while ethical concerns include overdependence, plagiarism, privacy, and teacher qualifications. AI is seen as supplementary rather than a replacement for human educators.
Original abstract
In today's world, Artificial Intelligence (AI) represents an innovative factor in education, especially in English Language Teaching (ELT). The purpose of this research paper is to explore the influence of AI on lesson planning and English teaching methodologies based on teachers' insights. Specifically, it will be considered the impact of AI on instructional design, material development, personalized instruction, assessment, and classroom interaction in the context of English language teaching. For analysis, a qualitative-descriptive and analytical approach will be applied using a review of empirical findings, scientific literature, and personal experience related to integrating AI in ELT. According to results, artificial intelligence contributes to teachers' performance by saving their time, offering options for differentiated instruction, providing adaptive instructional materials, and enabling innovation in the pedagogy field. At the same time, the use of technology is viewed as supplementary to traditional educational activities rather than replacement to human educators. Ethical aspects associated with the integration of AI in the process of education, overdependence on AI-based resources, plagiarism and academic integrity, privacy protection issues, and teacher qualifications should be considered in the case of English teaching.
- PaperOpenAlex — TESOL research21 Jul 2026
An Exploratory Investigation of ELT Instructors’ Attitudes Toward Bilingual Medium of Instruction (BMI) and the Role of Technology in Bangladeshi Classrooms
Bushra Jesmin Trisha, Swaha Das, Ritu Ghosh
A survey of 18 ELT instructors at a Bangladeshi university found 88.9% support for bilingual medium of instruction (BMI), citing improved comprehension and learner confidence. However, 66.7% worried about reduced English exposure, and most reported barriers such as inadequate training, restrictive policies, and limited resources. Instructors held positive attitudes toward AI tools and gamification, though implementation varied with available resources.
Original abstract
This study investigates the outlooks, reported practices, and perceived constraints of English Language Teaching (ELT) instructors concerning Bilingual Medium of Instruction (BMI) in Bangladeshi university classrooms. Eighteen instructors from Feni University participated in a structured survey. The research is organized around three objectives: (a) examining instructors’ perceptions of translanguaging practices, (b) investigating the obstacles they report when implementing bilingual instruction, and (c) exploring how technological integration may facilitate translanguaging pedagogy. Within this sample, 88.9% of respondents indicated support for bilingual instruction, citing enhanced comprehension, increased learner confidence, and faster learning as the most frequently reported benefits. Approximately 66.7% of participants expressed concern about reduced English exposure, and a majority reported inadequate training, restrictive institutional policies, and limited bilingual resources as recurring barriers. Respondents held positive attitudes toward AI-driven tools and gamification, although the extent of implementation appeared to vary with available resources. The study discusses these findings in relation to each research objective, situating the sample-level results within the broader literature on translanguaging, language policy, and educational technology. The discussion concludes with practical implications for teacher education, institutional policy, and bilingual resource development, and acknowledges the limitations of the current study alongside directions for future inquiry.
- PaperJournal of Second Language Writing21 Jul 2026
Writing the local: How place-based writing mediates learners’ perceived authenticity in L2 writing
Delin Kong
Examines how place-based writing tasks influence learners' perceptions of authenticity in second language writing contexts.