Although essential for teachers'professional development, developing content knowledge (CK) remains a challenge in pre-service language teacher education, where applied linguistics concepts are often perceived as disconnected from practice. While research on technology integration emphasizes pedagogical or technological knowledge (TK), little is known about how emerging tools can mediate CK development. Thus, this study implemented a design-based learning project: 23 pre-service English teachers used generative AI (GenAI) and the metaverse to design their brands and 3D showrooms, applying applied linguistics concepts. Reflective journals and semi-structured interviews were analyzed through thematic analysis to explore how CK developed through TK and learner agency. The findings show that their CK was enhanced when applied linguistics knowledge was used to craft brands and showrooms through iterative design cycles. Agency mediated this process, as they calibrated the outputs and collaborated through distributed agency, using GenAI and metaverse affordances. Finally, reflective inquiry enhanced critical AI literacy, which supported CK development by prompting participants to work on GenAI-mediated decisions (e.g., reliability, bias, authorship, and inclusion) while designing their artefacts. The study discusses theoretical implications for affordance-agency mechanisms in technology-mediated learning and pedagogical implications for integrating GenAI-metaverse design and critical AI literacy in teacher education.
While previous studies have highlighted the significant roles of both social and emotional factors in shaping learners' behavioral intention, relatively few have examined how these factors interact across diverse second or foreign language (L2) learning environments. To fill in the existing gap, this study examined the relationship between L2 belongingness, personal L2 enjoyment, and willingness to communicate (WTC) in both in-person and online classes. Data from 293 first-year Turkish English as a foreign language (EFL) university students were analyzed using hierarchical regression analysis. The results indicated that students with higher academic and social belongingness reported higher WTC in both settings. Additionally, L2 enjoyment moderated this relationship. The findings demonstrate how external factors—teacher and peer support, coupled with an internal emotional state—personal enjoyment—drives EFL learners’ communication willingness in different L2 learning contexts. The findings highlight the importance of creating a supportive and inclusive learning environment where educators foster connections among students and between students and teachers in face-to-face and online language classes. Incorporating enjoyable and engaging L2 activities is also recommended to boost students' communication willingness in different L2 learning contexts.
Virtual Exchange (VE) has been gaining popularity amongst Foreign Language (FL) teachers as it allows students the opportunity to use the FL they are studying in real-world situations with peers around the world. While VE allows for extensive language production and the development of intercultural competence, the assessment of these aspects within VE poses significant challenges for teachers. Through the use of a qualitative descriptive design, this paper examines the assessment path eight teachers from six countries took as their students participated in a VE. It outlines the reasoning behind the assessment designs they chose, how they implemented them, the challenges they faced and the manner in which they overcame them. The teachers were surveyed and interviewed before and after participating in an eight-week, mainly asynchronous VE. Interviews were recorded, transcribed and then thematically analyzed as were the open-ended survey questions. Results show objectives of developing communicative and intercultural competence were major considerations when designing and implementing assessment of student participation in the VE. Most participants favored formative approaches and incorporated both quantitative and qualitative assessment to encourage intercultural communication. Analysis resulted in recommendations to help practitioners enhance their assessment of students’ participation in VE.
This paper examines how pronunciation instruction is enacted in online Swedish as a Second Language (SSL) within Municipal Adult Education (MAE). Using Actor-Network Theory (ANT), classroom observations and teacher interviews trace how human and non-human actors (e.g., teachers, students, platforms, cameras, microphones, recordings) shape instruction. Five analytic scenes show that: (1) close-up camera/microphone affordances amplify articulatory modeling; (2) prosody practice becomes an auditory, distributed activity coordinated via audio files and chat functions; (3) corrective feedback is configured by one-speaker-at-a-time interfaces and headphone listening; (4) assessment is redistributed over time; and (5) domestic soundscapes and connectivity contingencies unevenly condition what is hearable and assessable. In the paper, pedagogical implications grounded in these scene-based analyses are articulated. Pronunciation instruction is thus shown to emerge as a sociomaterialpractice, enacted through the actors and infrastructures of the online classroom.
This study investigates dynamic fluctuations in willingness to communicate (WTC) and emotional states among six Chinese English as a Foreign Language (EFL) learners during interactions with Replika, an AI-powered chatbot. Using an idiodynamic method and stimulated recall interviews, the study provides a granular understanding of WTC and emotional dynamics. Participants exhibited a range of emotional states, including epistemic and retrospective emotions, which were intermittently elicited by moment-tomoment interactional events. Fluctuations were induced by various factors, including anthropomorphism, conversational responsiveness, perceived naturalness of interaction, and learners' perceptions of Replika as either a conversational companion or a tool for language practice. Finally, this study proposes expectation appraisal as a key mediating mechanism between interactional factors, emotional states, and WTC. Learners' expectations constituted a significant factor influencing emotional outcomes and ongoing WTC. These findings are discussed with reference to the L2 WTC pyramid model, emphasizing the interplay between emotions and communicative behaviors in AI-mediated language-learning environments.
Using a within-subject design, this study engaged 60 Chinese EFL university students in reading English academic passages either with GenAI chatbot support or with peer support. Employing questionnaires, we compared the levels of positive and negative emotions and intrinsic and extraneous cognitive loads reported by students in these two reading conditions. Additionally, semi-structured interviews were conducted with 16 students to delve into the factors affecting their emotions and cognitive loads in the two interactive reading conditions. The results of questionnaires show that students experienced a significantly higher level of positive activating and deactivating emotions such as hope and relief and a lower level of negative activating emotion such as anxiety, as well as significantly lower levels of intrinsic and extraneous cognitive load in chatbot-supported reading. The interviews further reveal that students attributed the observed advantages of reading with chatbot to its immediate support, high efficiency, convenience of tracking the discussion record, and the low-stress learning environment. In the meantime, they reported disadvantages in emotional communication and social dynamics when communicating with AI chatbot. Overall, this study has elucidated the mechanisms underlying differential effects of reading with GenAI chatbot support versus peer support on students’ emotions and cognitive loads.
As part of a research project on the use of digital games for language learning, this study explored the emotional trajectory and experiences of EFL learners during game-enhanced digital multimodal composing (DMC), with a focus on enjoyment and boredom. Informed by the control-value theory, this exploratory multiple case study centered on two groups of learners who volunteered to participate in this out-of-school project in which they played the digital game Genshin Impact and completed DMC over the course of six weeks. Both quantitative and qualitative data were collected and analyzed, including emotion questionnaires, participants’ gaming journals, DMC productions, semi-structured interviews, and critical incident forms. The results showed that despite fluctuations, the high achievers in DMC demonstrated high levels of enjoyment and moderate levels of boredom. Low achievers experienced relatively high levels of boredom and moderate levels of enjoyment during DMC tasks. Analysis further revealed the control and value ascribed to the tasks as potential causes for these differences and emotional fluctuations. The findings also highlight the role identity played in the control-value appraisal of the learning activity. The pedagogical implications of the study are discussed and suggestions for future research are provided.
This study investigates how English as a Foreign Language (EFL) learners’ beliefs about technology acceptance predict their task-specific enjoyment and boredom during an AI-mediated speaking practice course. Specifically, it examines how perceptions of ease of use and usefulness influence both general and specific aspects of enjoyment (including task characteristics, personal enjoyment, and social interactions) and boredom (including task characteristics, personal boredom, and social interactions) in language learning. Data were collected from 141 Iranian EFL learners participating in an online speaking class. To enhance the precision and accuracy of the analyses, preliminary procedures identified the optimal measurement structure for enjoyment and boredom, leading to the adoption of a bifactor exploratory structural equation modeling (bifactor-ESEM) representation. The study then used a structural model to validate the relationships among learners’ perceptions and their emotional experiences. The findings highlight the predictive role of technology acceptance beliefs in shaping both general and specific facets of enjoyment and boredom, with global task-specific enjoyment negatively associated with global task-specific boredom. These results may inform the design of emotionally supportive Intelligent Computer-Assisted Language Learning (ICALL) activities tailored to the tasks’ unique demands and provide deeper insights into learners’ experiences and emotional well-being.
Guided by the control-value theory of achievement emotions, this study examines the relationships among two understudied foreign language emotions, namely pride and shame, control-value appraisals, engagement, and performance in a Computer-Assisted Language Learning (CALL) setting. A total of 652 Chinese university students from a massive open online course (MOOC) participated in the study. Structural equation modeling (SEM) results showed that control and value appraisals positively predicted pride but negatively predicted shame. Pride positively predicted each of the three dimensions of engagement (i.e., cognitive, emotional, and behavioral) while shame negatively predicted these dimensions, except for cognitive engagement. Emotional and behavioral engagement, but not cognitive engagement, positively predicted performance. Pride and shame mediated the relationship between control-value appraisals and emotional and behavioral engagement, which, in turn, mediated the relationship between pride or shame and performance. By contrast, pathways through cognitive engagement were not significantly linked to performance. Overall, pride and shame, along with emotional and behavioral engagement rather than cognitive engagement serially mediated the relationship between control-value appraisals and performance. We discuss the implications for language teachers and highlight the importance of addressing pride and shame, alongside their appraisal antecedents and learning outcomes in CALL.
Drawing on the Control-Value Theory (CVT), this study investigates the impact of vocabulary grit on behavioral engagement in AVL, with task enjoyment and boredom serving as mediators. A mixed-methods approach was employed, combining survey data from 102 Chinese university EFL learners with follow-up interviews from 10 participants. In addition, a domain-specific L2 vocabulary grit scale was developed for use in the study. Structural equation modeling (SEM) results indicate that vocabulary perseverance of effort (PE) strongly predicts behavioral engagement both directly and indirectly via task enjoyment. Vocabulary consistency of interest (CI) influences behavioral engagement primarily through its effect on task enjoyment. While task boredom negatively correlates with behavioral engagement, it does not significantly mediate the vocabulary grit-behavioral engagement relationship, suggesting the presence of coping mechanisms or external motivators. Qualitative findings further illustrate that students’ behavioral engagement is driven by a combination of perseverance of effort, interest, positive emotional reinforcement, and external motivational factors such as exams. These findings highlight the complex interplay between L2 grit, task emotions, and behavioral engagement in AVL. The study offers theoretical and pedagogical insights into fostering sustained engagement in AVL by enhancing perseverance, cultivating interest, and promoting positive emotions.
As an important development in CALL, artificial intelligence (AI)-assisted foreign language teaching not only offers unique advantages in leveled reading, vocabulary, pronunciation, self-assessment, personalized testing, and information retrieval but also effectively enhances learner interaction and emotional regulation in reading. Despite these advancements, research on learner emotional engagement in AI-augmented EFL reading instruction remains limited, and there is a lack of sufficient empirical understanding of the significance of learner interaction. This study constructs a predictive model for emotional engagement among EFL learners in AI-augmented educational contexts, aiming to analyze the impact of learners’ AI literacy and peer interaction on their emotional engagement in reading. The research was conducted among 650 EFL university students in central China. Findings indicate that in AI-augmented EFL learning scenarios, learner AI literacy and peer learning interactions positively influence emotional engagement in reading. Furthermore, learner interest in AI use and reading pleasure play partial mediating and serial mediating roles in this relationship, respectively. This study not only highlights the critical role of AI literacy in AI-assisted EFL reading but also clarifies the significance of interpersonal communication for emotional engagement in AI contexts.
The integration of artificial intelligence (AI) in L2 classrooms has garnered remarkable attention due to its potential to enhance students’ language achievements. While existing research has highlighted the implications of incorporating AI into L2 classrooms, there remains a gap in understanding how this incorporation may affect students’ achievement emotions and flow experiences. To narrow this gap, this intervention study sought to assess the influence of AI-enhanced instruction on L2 students’ positive achievement emotions: pride, hope, enjoyment, and their flow experiences. Furthermore, with the aid of latent growth curve modeling (LGCM), the study tried to track the developmental trajectory of L2 students’ positive achievement emotions and flow experiences over the course of a semester. To these aims, a large sample of 217 L2 students was recruited and randomly divided into the control or experimental groups. To measure participants’ flow and positive achievement emotions, two questionnaires were administered to them at distinct intervals throughout the intervention. The results evinced a notable enhancement in both the flow experience and positive achievement emotions of participants who were exposed to AI-enhanced instruction. This research underscores the critical role of AI-enhanced instruction in fostering students’ positive achievement emotions and flow experiences within L2 classrooms.
This study aims to contribute to the growing body of literature examining the socio-emotional and cognitive trajectories of participants in Virtual Exchange (VE). While sentiment analysis has been applied to asynchronous VE interactions and post-exchange written data, its use in analyzing oral interactions within synchronous VE settings remains limited. This research analyzes data from a VE in which Spanish, French, and Irish undergraduates collaborated via videoconferencing. Student dyads interacted using either English as a lingua franca (Spain-France) or bilingually in English and Spanish (Spain-Ireland), and the study examines differences in socio-emotional responses between the two groups. Using LIWC (Linguistic Enquiry Word Count) and supported by content-based qualitative analysis, findings revealed significant increases in word count, positive emotion, affect, and social processes, alongside reductions in negative emotion and anxiety between initial and final interactions. Notably, Group 2 (the L1-L2 group), despite having students with lower proficiency levels in the target language, showed higher results in positive emotion, social, and cognitive processes. This occurred even though they produced fewer words in the L2, highlighting the potential of employing both the L1 and L2 to enhance socio-emotional outcomes in VE.
This longitudinal qualitative study examines distributed agency between human writers and generative AI in the context ofL2 writing. Grounded in Bandura's theory of agency, the study analyzes students' written texts, reflective accounts, and AI interaction logs collected from Taiwanese university students. The findings indicate that human-AIdistributed agency shapes the enactment ofL2 writing across intentionality, forethought, self-reactiveness, and self-reflectiveness. Moreover, distributed agency both supports and constrains learners' engagement, depending on how it is exercised.
This study proposes the GenAI-Mediated Activity Theory (GMAT) as a conceptual framework for understanding how generative AI (GenAI) reshapes second language (L2) teachers' teaching preparation and practices within complex pedagogical ecosystems. Methodologically, the paper adopts a model-building approach. It recontextualizes and synthesizes conceptual elements from Engestr & ouml;m's (1999) expanded Activity Theory (AT) to construct the model. Thus, the study illuminates the evolving role ofL2 teachers as adaptive, multi-positional agents of teaching. Specifically, teachers may adopt GenAI to refine instructional goals, co-create pedagogical content, negotiate ethical guidelines, and participate in collaborative professional networks. While previous AT-based studies have primarily examined how technologies mediate components within an activity system, the GMAT model contributes this work by theorizing how GenAI contributes to the evolution of sociotechnical and pedagogical ecosystems in L2 education. Furthermore, the study outlines concrete pedagogical implications by examining how specific triadic relationships among the components interact to generate new GenAI-mediated instructional environments. Overall, the GMAT model provides both a theoretical and practical foundation for guiding instructional design and teacher development in the era of GenAI-enhanced education.
Against the backdrop of the rapid integration of generative artificial intelligence (GenAI) into informal digital learning settings, this study investigates how Chinese university English as a Foreign Language (EFL) students' L2 motivation interacts with their digital literacy and virtual intercultural experience (VIE) to shape their participation in GenAI-mediated informal digital learning of English (GenAI-IDLE). This study surveyed 568 Chinese undergraduate EFL students and employed a structural equation modeling approach to analyze the data. The findings reveal that students' ideal L2 selves and ought-to L2 selves positively and significantly predict their digital literacy and GenAI-IDLE, respectively. While their ideal L2 selves make a positive and direct impact on their GenAI-IDLE, their ought-to L2 selves do not predict their VIE. The results also demonstrate that students' digital literacy mediates the relationship between L2 motivation and GenAI-IDLE. Their digital literacy and VIE jointly play a chain mediating role in the association between L2 motivation and GenAI-IDLE. However, their VIE fails to mediate the link between the ought-to L2 self and GenAI-IDLE. By elucidating the motivational, digital, and behavioral mechanisms in GenAI-mediated informal learning environments, this study extends the application of Self-Determination Theory within in GenAI-powered educational psychology and provides pedagogical implications.
This study compared Task-Based Language Teaching (TBLT) delivered in virtual reality (VR) and in traditional settings, examining learning gains, transfer, retention, and learner perceptions. 22 participants completed pretests, immediate posttests, transfer tests, and a questionnaire, while 10 of them, selected to balance proficiency across groups based on posttest scores, additionally completed a two-month delayed posttest and follow-up interviews. Wilcoxon signed-rank tests showed significant learning gains in both VR and traditional groups (p < .001; p = .006, in target discourse and p = .004; p = .008 in listening tests). ANCOVA indicated no immediate group difference in posttest scores (p = .205; p = .322) or in transfer measures (p = .608). After two months, a Mann-Whitney U test showed the VR group preserved target discourse significantly better than the traditional group (p = .036). Mann-Whitney U analyses ofLikert-scale responses revealed greater enjoyment (p = .008), perceived retention (p = .009), reported difficulty (p = .01), and motivation (p = .033) in the VR group. Interview analyses (in vivo coding) highlighted VR affordances that promote contextualized practice and positive emotional engagement. These results suggest VR-TBLT may enhance long-term retention and learner engagement compared with traditional TBLT.
This study examines how a custom GPT-based chatbot can mediate learner development within the Zone of Proximal Development (ZPD) through dynamic assessment (DA) with beginner-level learners of Korean as a Foreign Language. The model was fine-tuned using OpenAI's My GPT platform, with a custom prompt specifying graduated mediation, responsive behavior guidelines, and target grammar points. Specifically, the study investigates how GPT operationalizes scaffolding processes in text-based dialogue by sustaining interaction, providing form-focused feedback, and adjusting support contingent on learner responsiveness. Ten English-speaking students in a Korean course at a U.S. university interacted with the chatbot weekly over four weeks. Qualitative analysis of 280 learner-GPT turns identified three mediation types: conversational, instructional, and developmental. Through these, the chatbot maintained natural and level-appropriate dialogue, delivered graduated mediation aligned with learner responsiveness, and used accurate learner responses as springboards to guide movement from the Zone of Actual Development toward the ZPD. Complementary quantitative measures showed higher uptake rates and significant gains in mean length of sentence and lexical diversity. These findings suggest that large language models, when carefully tuned, can emulate core principles of Vygotskian mediation and foster human-AI co-construction of learning within scaffolded interaction.
Debate is an effective pedagogical approach, yet it presents significant challenges for EFL learners. While Artificial Intelligence (AI) chatbots are increasingly integrated into education, their application in multi-skilled tasks like debate preparation remains underexplored. This study investigated the impact of a two-phase AI-assisted intervention on 48 EFL learners' debating self-efficacy and perceptions. Phase 1 involved traditional debate preparation without AI assistance, focusing on foundational skill development through instructor-led instruction. Phase 2 introduced AI chatbots for refinement of arguments, rebuttals, and delivery practice, allowing students to enhance their debates through AI-powered scaffolding. Data were collected via self-efficacy questionnaires at three time points (pre-intervention, post-Phase 1, and post-Phase 2), a post-intervention perceptions questionnaire, written reflections, and focus group interviews. Repeated-measures ANOVAs revealed significant stepwise increases in students' debating self-efficacy across the three time points, with the most substantial gains observed in debate skills and language use. The perceptions questionnaire corroborated these findings, demonstrating that students rated AI as most effective for refining speeches, locating evidence, and developing arguments, while perceiving it as least helpful for oral delivery practice. Furthermore, qualitative analysis yielded nuanced and contextualized insights regarding both the benefits and limitations ofAI-assisted debate preparation.
With recent trends toward integrating generative artificial intelligence (GenAI) in the multilingual classroom, there has been a surge of research on using chatbots for language learning and teaching. However, GenAI chatbots are now starting to be examined in digital multimodal composing (DMC). DMC has been explored markedly over the past decade and provides an opportunity to examine how multiliteracies can be integrated in GenAI-assisted DMC. In this technology-in-practice forum, the benefits and challenges of integrating GenAI-assisted DMC are reported. Implemented in an American university-based intensive English program in the mid-Pacific, the GenAI-assisted DMC project was employed in an eight-week upper-beginning English as a Second Language class through a multiliteracies framework-consisting of situated practice, overt instruction, critical framing, and transformed practice-drawing on pedagogical components for storytelling. The students created slideshow presentations through the assistance of the GenAI chatbot of their choice. These artifacts were created for future participants in the program. Reflections from the students indicate that engagement was sustained throughout the DMC process. However, the learners struggled with prompting the GenAI chatbot of their choice during the DMC task. By reflecting on opportunities to integrate GenAI-assisted DMC, this forum provides an example of how GenAI can be implemented in classrooms and the procedural steps involved in such a process. Lessons learned from this process are also discussed, which can be improved upon by future iterations.
