Ahead of Publication / Just Accepted

The prevalent ownership of mobile devices and increasing use of AI learning tools among college English as Foreign Language (EFL) learners has triggered wide discussions on the application of AI learning tools in developing English skills. This mixed-method cross-sectional study tries to understand how language-learning-related psychological factors, including Willingness to Communicate (WTC), Self-perceived Communication Competence (SPCC), and Speaking Anxiety (SA), are related to learners’ acceptance and adoption of AI speaking apps based on the Technology Acceptance Model. In this study, 280 Chinese students from 6 different universities, including both undergraduate and postgraduate levels, participated in the questionnaire, with 8 of them further taking part in semi-structured interviews. Binary Logistic Regression analysis revealed that WTC is positively correlated with the behavioral intention to use the apps and perceived usefulness, while SPCC is negatively correlated with perceived usefulness. SA, however, showed no significant correlation with learners’ acceptance of the apps. The interviews provided additional insights, with participants highlighting the motivational mechanisms, the utility during fragmented time and the emotional relief provided by the apps as the key factors driving their use. The findings underscore the potential of mobile AI speaking apps in enhancing English speaking practice while also revealing certain limitations.

This qualitative case study is situated in the context of a beginning French language course at a Sino-foreign joint-venture institution in China. The course involved an eTandem component for ten Chinese students learning French who were paired with learners of Chinese in France. The eTandem was accompanied with iterative cycles of conversation transcriptions and guided reflections to think about how students prepared for their interactions, what they learned, what challenges they encountered, and what and how they planned to improve. Introducing the REFLECT model of reflections to support learner agency through metacognition, this article reports on the perceptions of students regarding their agency for linguistic and intercultural growth. The post-conversations and end of the semester reflections of the ten Chinese students were analyzed inductively and thematically. Findings indicate that students developed metacognition and learner agency by engaging in proactive behaviors including input, interaction, feedback, and information-seeking. This article provides theoretical and practical implications connecting eTandem, reflections, metacognition, and learner agency in the languaculture classroom, transcending the Franco-Chinese context.

This study examines the workload, translation quality, and user perception associated with Chinese–English (C–E) post-editing (PE) performed by student translators using three tools: DeepL, ChatGPT-4o, and DeepSeek-V3. Thirty Master of Translation and Interpreting (MTI) students from a comprehensive university in China participated in the study. Source texts of varying types, but similar complexity level, served as the materials for raw machine translation outputs. By analyzing variables such as task duration, keyboard events and pause length, translation quality assessment data of 120 post-edited files, and questionnaire responses, the study yielded the following findings: (1) The use of neural machine translation (NMT) or large language models (LLMs) did not significantly impact overall PE workload, though students assisted by LLMs required fewer keyboard events when editing popular science texts; (2) LLM-assisted PE demonstrated higher quality than those edited with DeepL, as evidenced by fewer error counts and lower error scores; (3) Participants perceived ChatGPT-4o to be the most effective tool for error correcting ability and task efficiency. DeepSeek-V3 was rated highest in terms of user experience, while DeepL was regarded as the most reliable in terms of information accuracy. By comparing students’ PE workload, translation quality and user perception across different scenarios, this study offers new insights into human–AI collaboration in PE workflows and its implications for translation pedagogy in the AI era.

This exploratory study addresses the ongoing challenge of making grammar teaching more effective and engaging. Recognising that no single approach fits all learners, the paper underscores the necessity for teachers to tailor their strategies to accommodate varying needs, interests and learning styles. By leveraging computer-assisted techniques, corpus linguistics enables the analysis of extensive, principled databases of authentic language to uncover linguistic patterns. This paper introduces a data-driven learning (DDL) approach that utilises corpus-based natural language within a 4P model (Presentation, Problem-solving, Practice, Production) to teach wh -questions. The effectiveness of this approach was assessed through pre- and post-tests, student evaluations and interviews with selected participants. Findings indicate significant improvements in students’ understanding of wh -questions and an increase in motivation to learn. The research demonstrates how educators can effectively integrate the form-focused, corpus-based DDL approach into the 4P model, fostering a well-rounded focus on form, meaning and use in grammar instruction.

As AI technologies increasingly permeate educational environments globally, understanding how to craft and utilize AI prompts effectively has become crucial for educators. This paper examines the intersection of AI implementation and pedagogical practices in Hong Kong, China secondary schools, particularly exploring how prompt engineering mediates technological capabilities and educational objectives. This study identifies effective practices and challenges in implementing AI-enhanced instruction through a systematic review of existing literature and mixed-methods research. The findings reveal that educators’ proficiency in prompt engineering significantly influences their ability to leverage AI tools for personalized learning experiences and the development of higher-order thinking. Analysis of classroom implementations reveals that well-constructed prompts significantly enhance student engagement and facilitate more precise alignment between AI capabilities and curriculum requirements.

This article examines the transformative role of Artificial Intelligence (AI), Generative AI (GenAI), and Large Language Models (LLMs) like ChatGPT and DeepSeek in language teaching and learning. It highlights their capabilities, including personalized content generation, automated assessment, real-time feedback, and administrative efficiency, while emphasizing the necessity of prompt engineering to optimize outputs. The PROMPT (Persona, Requirements, Organization, Medium, Purpose, Tone) framework is introduced as a structured approach to crafting effective prompts. Case studies, such as DeepSeek’s Python code generation, demonstrate practical applications. However, the article critically addresses limitations like bias, factual inaccuracies, and ethical concerns, advocating for rigorous fact-checking and balanced human-AI collaboration. By synthesizing research and practical examples, this article underscores AI’s potential to enhance language education while urging educators to adopt critical literacy and ethical frameworks to mitigate risks and ensure equitable, human-centered learning experiences.

This review introduces the AI CFT and examines its implications for language teacher education. It highlights the challenges faced by teachers and researchers and explores solutions to enhance language teachers’ competencies in the context of digital transformation in education.

Artificial intelligence (AI) is increasingly reshaping education by identifying students’ learning challenges and enhancing academic performance. However, many teachers lack a comprehensive understanding of AI, limiting their ability to fully utilise its potential in teaching. While existing research has examined students’ AI literacy in formal education, little is known about the perceptions and experiences of English as a Foreign Language (EFL) practitioners in shadow education. To address this gap, this study employed in-depth semi-structured interviews with nine Chinese EFL practitioners in shadow education to explore their perceptions and practices regarding AI literacy. The findings reveal diverse perspectives on AI, varying levels of AI-related knowledge, different approaches to applying and evaluating AI tools, and ethical considerations, all of which are shaped by distinct teaching experiences across various contexts. These insights illustrated key dimensions of existing AI literacy frameworks within the new context of Chinese shadow education. We also highlighted the importance of considering AI’s implications for educational equity and social justice, which informed the development of a five-dimensional AI literacy framework for EFL practitioners in the shadow education sector. This framework encompasses a human-centred mindset, AI knowledge and understanding, AI application and evaluation, AI ethics, and systemic and societal implications of AI. Our study offered recommendations for EFL teachers in shadow education, teacher educators, and educational institutions seeking to equip language teachers with the necessary attitudes, knowledge and skills to navigate the evolving landscape of AI-driven language education.

The rapid development and adoption of ChatGPT have sparked increasing academic interest in its translation performance. However, little attention has been paid to the role of prompts – a key factor influencing ChatGPT’s output. This study addresses this gap by analyzing the consideration of prompts in 32 articles that evaluated ChatGPT’s translation performance, selected through the PRISMA framework. The findings reveal that prompt consideration is generally inadequate: nearly a third of the articles did not mention prompts, and most studies that did acknowledge prompts only addressed superficial aspects, such as merely mentioning the concept or specifying the prompts used, rather than treating prompts as a key variable. Additionally, prompt usage is often limited and lacks justification. The analysis further reveals a significant disciplinary disparity, as studies in computer science and information technology have demonstrated the most comprehensive approaches, surpassing those in translation and linguistics, as well as other disciplines. Based on these findings, this study proposes a series of implementation-focused recommendations to enhance prompt consideration in ChatGPT-related research, and underscores the importance of interdisciplinary collaboration to foster a more integrated research approach.

Following the generative artificial intelligence (GenAI) boom of the early 2020s, research in applied linguistics has become preoccupied with identifying how artificial intelligence (AI) and GenAI can be used effectively in research and education. As we emerge from our initial reactionary perspectives, there is an increased interest in delineating AI literacies so as to support learners who wish to engage with AI and GenAI as part of their learning process. This paper adds to this growing body of work, offering insight into critical AI literacies for applied linguistics and language education. Based on the critical grounded theory analysis of a focus group with Spanish students of applied linguistics, this paper teases apart the students’ technical understandings of AI, use of critical thinking when engaging with AI, awareness of the ethical concerns surrounding AI, and practical applications of AI. The discussions revealed a complex interaction of practical, ethical, and analytical considerations, emphasizing AI’s potential to augment but not replace human expertise. Ethical considerations were linked with critical thinking, reflecting a deep integration of moral and practical dimensions in student discussions. Our analysis seeks to inform current research that develops both frameworks and theoretical models for language education and applied linguistics education.

This paper I attempt three things: to discuss the nature and definition of critical digital literacy, to share some of my research findings into the nature of critical digital literacy in both primary and secondary classrooms in the UK, and finally, to consider how to deal with the new challenges to critical digital literacy that artificial intelligence poses. In discussing critical digital literacy, I draw upon three theoretical frameworks as lenses through which to consider different aspects of critical literacy: first, the theories of Paulo Freire, which show how critical literacy does not simply read the world- it rewrites it, and it changes it; second, Coiro’s perspective on the epistemological mutations of the concept of “truth” that have been brought about and accelerated by the Internet; third, the perspectives of Mikhail Bakhtin, who had much to say about the nature of learning, in particular arguing that all learning is dialogic, and also that many questions to which we might seek definitive answers are in the end “unfinalizable”.

CALL publications are not short of systematic literature reviews. The majority of these reviews aim to identify publication trends, research themes, types of technology used, factors influencing technology adoption, task/learning activity design, effectiveness of CALL, theoretical underpinning and so on. No previous publication has focused exclusively on CALL research from the perspective of research design although appropriate design is the first step in ensuring the quality of research outcomes. This study reports on the findings of a research design-focused review of 338 full articles published in five SSCI and ESCI CALL journals between 2023 and 2024. Strengths identified include clear statements of context of study and research purpose, question and/or hypothesis; increasing adoption of mixed-method approach; popularity of experimental research; sound theoretical foundation; multiple data sources/triangulation; specific accounts of data collection and analysis; and acknowledgement of limitations. Aspects which are found to be weak include predominance of micro-level research; scarcity of replication studies; insensitivity to researcher biases; overwhelming dominance of non-probabilistic, convenience sampling; shortage of longitudinal research; undue reliance on self-reported data and unverified and/or unavailable instruments; insufficient attention to ethical issues; over-favoring case study and survey research; and use of ambiguous units of measurement. Implications from the findings are discussed. Broader issues beyond the findings are raised which warrant more attention. By highlighting the strengths and weaknesses of CALL journal publications in terms of research design rather than appraising individual studies, it is hoped that researchers and gatekeepers (editors/reviewers) alike will pay more attention to the design of a study.

As generative AI models such as ChatGPT reshape language education, understanding how teachers engage with AI-mediated practices and negotiate professional identities becomes essential. This longitudinal multiple-case qualitative study investigates how non-native pre-service English teachers at a university in the Hong Kong SAR, China, developed AI literacies and negotiated their professional identities during a 13-week M.Ed. course integrating ChatGPT-facilitated pedagogy. Notably, the study draws on Darvin and Norton’s investment model, reconceptualizing AI literacies as situated social practices that mediate the negotiation of identity, capital, and ideology. Data were collected through end-of-course interviews, learner artifacts, and teaching materials. Findings reveal that PSTs’ development of AI literacies is a multidimensional process involving (1) recognizing AI’s (linguistic) strengths, (2) refining prompt engineering competencies, and (3) cultivating critical awareness of AI’s limitations and strategic use. While participants leveraged AI-mediated linguistic, cultural, and technical capital to enhance perceived professionality and assert more legitimate language teacher identities, their tendency to adopt AI-generated “native-like” outputs without sufficient critical evaluation may risk reinforcing existing linguistic hegemony. The study offers practical implications for integrating AI literacies development into teacher education, balancing technological affordances with emancipatory pedagogy.

Technological advancements have facilitated access to corpus tools, which are significant in language learning and teaching. One way to harness corpus tools is through concordancers that grant access to authentic language usage. With corpus-based language learning growing in significance, this media review evaluates Fraze.it – a multilingual online concordancer, characterizing its features, highlighting its pedagogical affordances, and outlining its technical capabilities. It, thereby, aims to propose a critical review of the platform and recommend relevant instructional activities and technical improvements. More specifically, this review intends to introduce an under-researched corpus tool at the disposal of foreign language learners and teachers. Findings reveal that Fraze.it allows for contextualized vocabulary learning, better grammatical/syntactical understanding, pronunciation awareness/improvement, and collocational awareness. However, inappropriate/irrelevant images, malfunctioning of multilingual translation, synonyms, and definitions are among its downsides. Notwithstanding the limitations, Frazei.it might be a promising tool for vocabulary expansion, reading comprehension, writing enhancement, speaking/pronunciation drills, listening/dictation activities, and grammar/syntax. Nevertheless, further research is urged to corroborate the results and explore the tool’s applications across diverse contexts.

The rise of Generative Artificial Intelligence (GenAI) technologies and applications, such as ChatGPT, has prompted significant changes to the landscape of higher education. Amidst the excitement and debates about the potential of GenAI in education, it is often too easy to ignore the perspectives and attitudes of students and teachers towards GenAI and their views on how technology should be integrated into the learning environment. This study fills a gap in the literature by conducting a survey of Chinese university students and teachers to explore and compare their use patterns and attitudes toward GenAI. Intriguingly, the results revealed that despite the differences in use patterns, both teachers and students exhibited similar positive attitudes toward the utility and positive impact of GenAI. However, students seemed to be more concerned about the use of GenAI tools than teachers. The study also examined if language teachers and non-language teachers have different use patterns and attitudes toward GenAI. The study highlights the significance of considering both teacher and student perspectives in policy-making and curriculum design related to GenAI, as well as the need to understand the nuanced attitudes of teachers from different disciplines to provide targeted support.

As Generative AI is increasingly used by language learners for academic writing in the context of higher education, it becomes essential for educators to integrate AI literacies in the writing curriculum to guide students to use Generative AI effectively, critically and ethically. We aim to share our practice in relation to AI literacy training for English academic writing among EAP learners studying at a joint-venture EMI university in China. Specifically, learning activities were designed and implemented for students to understand Gen AI’s advantages and pitfalls, engineer effective prompts, enhance assessment literacies, exercise critical thinking and safeguard academic integrity during the process of using GenAI for academic writing. In addition, the pedagogical considerations and rationale behind the practice will be illustrated. Finally, the article will be concluded with reflection on the practice and further suggestions on delivering AI literacy training for language learning purposes.

With the increasing frequency of China–Africa interactions, the importance of Swahili in China–Africa relations is steadily growing. However, there is still a notable lack of research on Swahili news corpora and discourse analysis. This study adopts both quantitative and qualitative methods to examine China’s national image construction through media under the critical discourse analysis (CDA) framework. Focusing on the mainstream media China Radio International (CRI), a micro corpus was built to analyse word frequency, word clusters and concordance lines, with the aim of exploring linguistic features and discourse structures of news reports. The findings indicate that by covering China’s efforts in promoting China–Africa affairs with positive discourse, CRI generally holds a positive attitude in news reports to construct a responsible global actor role. Meanwhile, CRI’s discourse construction also exhibits an imbalance in lexical choice with a tendency to emphasize positive achievements while downplaying challenges and difficulties. This reflects underlying power dynamics, socio-cultural influences, and ideological tendencies. This study could provide a reference for optimizing the construction of China’s image in Africa through African indigenous language media.

The transformative potential of generative artificial intelligence (GenAI) in language education highlights the importance of AI literacy among teachers to ensure its ethical and effective integration into teaching practices. Although studies have examined the application of AI in language education, there is a lack of comprehensive reviews focusing on the interaction between language teachers and ChatGPT, a GenAI tool, particularly in fostering human–AI collaboration within educational contexts. This review addresses this gap by synthesising findings from empirical studies. The Scopus database was used as the primary source for this review. A total of 19 journal articles, published between 2023 and 2024, were identified. The review first analyses the research participants and research methods of the selected studies. Key themes are organised into five dimensions: AI foundations and applications, AI ethics, a human-centred mindset, AI pedagogy and AI for professional development, which are derived from the framework proposed by Miao and Cukurova (2024. AI competency Framework for teachers . Paris, France: UNESCO). This review adopts their framework as an analytical lens for evaluating both the opportunities and challenges associated with integrating ChatGPT into language education. The findings highlight the importance of a balanced approach to AI integration to safeguard educational integrity. By offering actionable insights for teachers, curriculum designers and policymakers, this review presents a roadmap for the responsible adoption of AI in language education, ensuring that teachers remain central to the learning process.

This study investigated how ten Chinese university students interacted with ChatGPT to facilitate their acquisition of Spanish as a second language. In a semi-informal educational setting, 370 ChatGPT prompts authored by the students and the AI-generated responses they yielded were collected over the course of one week. Secondary data sources included questionnaires and learning diaries. Findings revealed that students primarily sought assistance from ChatGPT with vocabulary acquisition, reading comprehension, and written expression, with fewer prompts focused on grammar or oral communication. The prompts showed significant variation in length, language preference, and pattern, though only a small portion involved more than a single prompt-response interaction. This trend suggests a largely task-oriented use of ChatGPT, with limited reflective engagement. This study points to the teacher ↔ AI ↔ learner relationship as a new dimension of interaction and offers practical recommendations for educators in AI-assisted language learning. Key suggestions include fostering students’ self-directed learning abilities and encouraging more reflective and systematic learning approaches.

Prior research has demonstrated that in collectivist cultures (i.e., China and the Philippines), a Triarchic Model of Grit, encompassing perseverance of effort, consistency of interests, and adaptability to situations, can be more fitting. This study explores the concept of second language (L2) grit within a collectivist context, investigating its association with positive psychological constructs in L2 learning among Chinese college students studying English as a foreign language (n = 459). In this pursuit, a specialized grit scale tailored for language learning, the L2 Grit Scale in Collective Cultural Context (L2GSC), was developed and validated to assess the domain-specific grit of L2 learners. The findings based on hierarchical regression and machine learning methods revealed that L2GSC positively predicted learners’ enjoyment and engagement in language learning, exceeding the effects found with triarchic model of grit scale, suggesting that L2GSC is suited to collectivist cultures.

This study examines the effectiveness of the pre-seminar online learning activities in a blended English for Academic Purposes (EAP) course, with a particular focus on learner engagement with these online activities. It investigates the influence of learners’ English proficiency levels on their online engagement and examines the impact of this engagement on their language performance. A mixed-methods study design was employed, collecting quantitative data from 131 students and qualitative interview data from a subset of 18 participants drawn from this cohort. Simple linear regression results revealed that learners’ English proficiency levels did not predict their online behavioural engagement, but had a small predictive force on online cognitive engagement. Multiple regression results indicated a medium-sized impact of online engagement on EAP learners’ overall final scores. Specifically, simple regression results showed that online cognitive engagement was a significant predictor of learners’ EAP reading, listening, and writing scores, while behavioural engagement significantly predicted writing and speaking scores. Qualitative insights highlighted the importance of interactions between learner and content, learner and socialiser, and learner and interface when designing the online learning activities of a blended EAP course. The study concludes with an effective blended EAP learning model that optimises the use of technological modalities, emphasises the importance of online pedagogical, social, and technological interactions, and highlights the integration between online and onsite lessons.

Corpus technology (CT) as an effective technology for language learning is slowly being adopted by in-service language teachers. However, there is limited focus on pre-service language teachers, who first need to experience CT and gain a basic corpus literacy as learners. Therefore, this research aimed to demonstrate the learning benefits of CT to pre-service teacher and explore their intentions for future use of CT. A corpus pedagogy supported by constructivism, socio-cultural theory, and noticing hypothesis, was designed to help three-classes of pre-service ESL teachers in Chinese Hong Kong to improve citation skills using Parallel EAP Corpora. A mixed-method design was adopted by collecting data via tests, surveys, classroom interactions and interviews. The results revealed that the participants learned to use reporting verbs for citations, acquired adequate corpus literacy, positively evaluated the theory-supported pedagogy, and expressed strong intentions to use CT for learning vocabulary and academic writing, conducting research to complete course assignments, and facilitating future teaching. The findings highlight the significance of designing corpus-based learning activities through theory-supported pedagogy and reveal that introducing CT to pre-service ESL teachers who have multiple identities as language learners, novice researchers, critical thinkers, and future teachers, can have important pedagogical implications.

Effective use of advanced vocabulary is crucial for higher-level English language learners, particularly as they progress towards C1 proficiency according to the Common European Framework of Reference for Languages (CEFR). However, incorporating sufficiently sophisticated vocabulary in writing remains challenging for many learners. This quasi-experimental study investigated the effectiveness of an AI-assisted intervention designed to enhance awareness and use of advanced vocabulary among B2 level first-year English Studies students in Poland ( n  = 71). The intervention, integrated into regular coursework over five sessions, included exposure to advanced vocabulary through reading, explicit instruction on word proficiency levels, and opportunities for vocabulary use in writing. Data were collected through summary writing and word recognition tasks. The CEFR checker, an AI-based text analyser, was employed to identify C1+ and B2 level vocabulary in students’ writing. Results revealed modest but noticeable improvements in both the use and recognition of C1+ and B2 level vocabulary over time. The study also found changing correlations between vocabulary use and recognition across sessions, particularly for C1+ words. These findings highlight the potential of AI-assisted instruction in enhancing advanced vocabulary among higher proficiency EFL learners. The study contributes to the growing body of CALL literature on AI-assisted L2 instruction and opens avenues for further research on new pedagogical approaches to facilitate advanced vocabulary development in EFL writing.

The intention of pre-service foreign language teachers to adopt technology (PIAT) reflects their willingness to integrate educational technology tools into their teaching practices, which is essential for the digital transformation of language education and the professional development of teacher candidates. Drawing upon the Situated Expectancy-Value Theory, this study utilizes a structural equation model to analyze the factors influencing the intention of pre-service foreign language teachers to adopt technology. The results reveal that: (1) four motivational factors (i.e., self-efficacy, perceived enjoyment, perceived importance, and perceived usefulness) significantly predict PIAT, while perceived anxiety negatively correlates with, but does not significantly predict PIAT; (2) mindset significantly impacts perceived enjoyment, perceived importance, perceived usefulness, and perceived anxiety; (3) social influence exerts a significant influence on self-efficacy and mindset. The study concludes with implications for enhancing the digital literacy of foreign language teacher candidates.

Change, whether slowly developing over decades or rapid change caused by worldwide crises, affects education and educators profoundly. This article argues for a change in language teacher education embracing the changing world of online communication, and emphasising the epistemological awareness language teachers need to bring to the online classroom. Based on Computer-Assisted Language Learning (CALL) research and best practice examples from across the world, the paper outlines past changes, contextualises online language teaching in the wider area of online communication and describes progress in practice, theory and epistemological understanding of online teaching. It culminates in a series of evidence-based suggestions for future training and education of language teachers that will integrate adequate pedagogy and an enhanced epistemological understanding.

Significant advancements in mobile technology have offered tremendous potential for mobile-assisted vocabulary learning (MAVL). Given the relatively few studies on learners’ acceptance of MAVL, this study integrates the Technology Acceptance Model (TAM) with the Self-Determination Theory to assess participants’ acceptance and motivation levels regarding MAVL applications and to identify predictors of users’ intention to utilise these applications. Questionnaire data from 270 Chinese English as a foreign language university students revealed that the participants exhibited a strong intention to learn English vocabulary via mobile applications, and they reported high levels of motivation. Additionally, structural equation modelling analysis results indicated that perceived usefulness, perceived ease of use, and perceived competence accounted for 61 % of the variance in users’ behavioural intention to use MAVL applications, which demonstrated a stronger explanatory power than the original TAM. Perceived autonomy and perceived competence, however, failed to predict usage intention of MAVL. The proposed theoretical framework extends the original TAM and offers implications for application developers, suggesting the importance of designing user-friendly applications.

Though teacher conceptions of mobile language learning might affect their own language learning and their teaching practices, this is an area seldom examined. To address this paucity of research, fifty-nine students in an MA ELT (English Language Teaching) program, the majority of whom were in-service teachers, were investigated, via an open-ended survey, as to their understanding of mobile language learning and how they undertook mobile language learning and teaching. The results revealed that over eighty percent of the students understood mobile language learning as learning at any time and in any place or as the use of mobile technologies for learning. The participants mainly used mobile devices to practice their listening, reading and vocabulary. A cross-sectional analysis demonstrated that conceptualizing mobile learning as learning at any time and in any place is linked to the practices of listening, reading and online classes, while the conception of mobile learning as using mobile technologies is primarily associated with learning vocabulary, which indicates that conceptions of mobile learning are connected to learning practices. In terms of teaching, the participants reported mobile devices offered the most assistance via enhancing teaching effects, offering richer resources, helping with assigning homework, and communication with parents and students. The findings contribute to our understanding of teacher conceptions of mobile language learning and their teaching practices, which bear significant implications for mobile pedagogy and teacher education.