Pub Date : 2026-03-01Epub Date: 2026-02-11DOI: 10.1016/j.jeap.2026.101644
Fabio Cangero
Foreign Language Anxiety (FLA) is a persistent affective barrier in English for Academic Purposes (EAP), yet little research has examined how artificial intelligence (AI) might support learners emotionally. This article presents a proof-of-concept study investigating the feasibility of using AI-driven sentiment analysis to identify and mitigate FLA. A small Padlet-based corpus of student reflections (n = 41) was analysed using GPT-4-based sentiment classification, followed by an AI-mediated reframing activity completed by a volunteer sub-sample of learners (n = 14). Rather than making generalisable empirical claims, the study explores the potential and limitations of large language models as tools for affective scaffolding. Results indicate that students frequently express mixed emotions, combining anxiety with hope and motivation, and that AI-supported reframing may promote short-term reassurance and increased confidence. The paper discusses methodological and ethical considerations and outlines how affect-aware AI tools could be meaningfully integrated into EAP pedagogy.
{"title":"AI-driven sentiment analysis for mitigating foreign language anxiety (FLA) in EAP: A proof-of-concept study","authors":"Fabio Cangero","doi":"10.1016/j.jeap.2026.101644","DOIUrl":"10.1016/j.jeap.2026.101644","url":null,"abstract":"<div><div>Foreign Language Anxiety (FLA) is a persistent affective barrier in English for Academic Purposes (EAP), yet little research has examined how artificial intelligence (AI) might support learners emotionally. This article presents a proof-of-concept study investigating the feasibility of using AI-driven sentiment analysis to identify and mitigate FLA. A small Padlet-based corpus of student reflections (n = 41) was analysed using GPT-4-based sentiment classification, followed by an AI-mediated reframing activity completed by a volunteer sub-sample of learners (n = 14). Rather than making generalisable empirical claims, the study explores the potential and limitations of large language models as tools for affective scaffolding. Results indicate that students frequently express mixed emotions, combining anxiety with hope and motivation, and that AI-supported reframing may promote short-term reassurance and increased confidence. The paper discusses methodological and ethical considerations and outlines how affect-aware AI tools could be meaningfully integrated into EAP pedagogy.</div></div>","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101644"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-12DOI: 10.1016/j.jeap.2026.101647
Feng Kevin Jiang , Ken Hyland
English for Academic Purposes (EAP) has evolved from its beginnings as a pragmatic branch of English language teaching into a mature, interdisciplinary field concerned with the linguistic mediation of knowledge. While continuing to play a crucial role in the higher education of students around the world, contemporary developments have radically reconfigured the conditions of academic communication in which it operates. English now functions within a global-digital-plural ecosystem characterised by multilingual practices, multimodal genres, and algorithmic mediation. These changes present EAP with new theoretical and pedagogical challenges that extend beyond language description or skills instruction, demanding critical engagement with the ethical, technological, and epistemic dimensions of academic literacy. This paper proposes that we need a new research agenda for EAP that addresses three intersecting domains: the impact of artificial intelligence on academic writing and authorship; the diversification of academic communication in a globalised and open-science environment; and the implications of disciplinary hybridity and epistemic pluralism for pedagogy and research. In doing so, it positions EAP as a critical and epistemic discipline central to understanding how language, technology, and knowledge co-evolve and operate in the contemporary academy.
{"title":"EAP in a changing world: Towards a new research agenda","authors":"Feng Kevin Jiang , Ken Hyland","doi":"10.1016/j.jeap.2026.101647","DOIUrl":"10.1016/j.jeap.2026.101647","url":null,"abstract":"<div><div>English for Academic Purposes (EAP) has evolved from its beginnings as a pragmatic branch of English language teaching into a mature, interdisciplinary field concerned with the linguistic mediation of knowledge. While continuing to play a crucial role in the higher education of students around the world, contemporary developments have radically reconfigured the conditions of academic communication in which it operates. English now functions within a global-digital-plural ecosystem characterised by multilingual practices, multimodal genres, and algorithmic mediation. These changes present EAP with new theoretical and pedagogical challenges that extend beyond language description or skills instruction, demanding critical engagement with the ethical, technological, and epistemic dimensions of academic literacy. This paper proposes that we need a new research agenda for EAP that addresses three intersecting domains: the impact of artificial intelligence on academic writing and authorship; the diversification of academic communication in a globalised and open-science environment; and the implications of disciplinary hybridity and epistemic pluralism for pedagogy and research. In doing so, it positions EAP as a critical and epistemic discipline central to understanding how language, technology, and knowledge co-evolve and operate in the contemporary academy.</div></div>","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101647"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-27DOI: 10.1016/j.jeap.2026.101639
Sanghee Kang , Minkyung Kim
This study examined how EAP learners collaboratively engage with AI-generated feedback on co-constructed texts and to explore how learner engagement with AI-generated feedback influences their uptake behaviors and subsequent individual revisions. Thirty-seven university-level EFL students from two intact EAP writing classes completed two collaborative writing tasks in pairs and received ChatGPT's feedback on their co-constructed texts. They then collaboratively reviewed the feedback and independently produced individual texts on the same prompts.
To examine learner engagement with AI-generated feedback, transcribed peer interactions were analyzed in terms of engagement level. To assess influencing factors, learner perceptions of usefulness of AI feedback and feedback-related factors (i.e., ChatGPT's feedback category and linguistic focus) were also analyzed. Revision quality was evaluated in terms of lexical sophistication, syntactic complexity, and overall accuracy. A generalized linear mixed-effects model and linear mixed-effects models were used to examine uptake behaviors and revision quality, respectively.
Results indicated that learners exhibited varying levels of engagement with AI-generated feedback, from extensive discussion to limited acknowledgement. Higher engagement and more positive perceptions of AI feedback were associated with greater successful uptake. Additionally, greater engagement predicted higher lexical sophistication in revisions, while correct uptake was linked to increased syntactic complexity and greater overall accuracy. Implications of these findings are discussed in terms of implementing AI-generated feedback in EAP instruction.
{"title":"AI-generated feedback in an EAP writing classroom: The collaborative process of feedback, uptake, and revision quality","authors":"Sanghee Kang , Minkyung Kim","doi":"10.1016/j.jeap.2026.101639","DOIUrl":"10.1016/j.jeap.2026.101639","url":null,"abstract":"<div><div>This study examined how EAP learners collaboratively engage with AI-generated feedback on co-constructed texts and to explore how learner engagement with AI-generated feedback influences their uptake behaviors and subsequent individual revisions. Thirty-seven university-level EFL students from two intact EAP writing classes completed two collaborative writing tasks in pairs and received ChatGPT's feedback on their co-constructed texts. They then collaboratively reviewed the feedback and independently produced individual texts on the same prompts.</div><div>To examine learner engagement with AI-generated feedback, transcribed peer interactions were analyzed in terms of engagement level. To assess influencing factors, learner perceptions of usefulness of AI feedback and feedback-related factors (i.e., ChatGPT's feedback category and linguistic focus) were also analyzed. Revision quality was evaluated in terms of lexical sophistication, syntactic complexity, and overall accuracy. A generalized linear mixed-effects model and linear mixed-effects models were used to examine uptake behaviors and revision quality, respectively.</div><div>Results indicated that learners exhibited varying levels of engagement with AI-generated feedback, from extensive discussion to limited acknowledgement. Higher engagement and more positive perceptions of AI feedback were associated with greater successful uptake. Additionally, greater engagement predicted higher lexical sophistication in revisions, while correct uptake was linked to increased syntactic complexity and greater overall accuracy. Implications of these findings are discussed in terms of implementing AI-generated feedback in EAP instruction.</div></div>","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101639"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"In this BALEAP news item, four members of the Teacher Education in EAP SIG share their reflections on the work of the SIG over the past five years","authors":"Angeliki Apostolidou , Stella Bunnag , Lindsay Knox , Carole MacDiarmid","doi":"10.1016/j.jeap.2026.101654","DOIUrl":"10.1016/j.jeap.2026.101654","url":null,"abstract":"","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101654"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147448569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-13DOI: 10.1016/j.jeap.2026.101646
Lucy Macnaught
This conceptual paper provides a social semiotic perspective on GenAI technologies in English for Academic Purposes contexts. It focuses on the process of customising AI chatbots to steer how an LLM responds. Through discussing two customised chatbots for Master's of Nursing Science students who are writing research proposals, the paper argues that the theoretical framework of Systemic Functional Linguistics is ideal for chatbot design. Examples use Cogniti software to show how EAP teachers can custom design a chatbot with minimal coding. These examples illustrate how SFL informs decisions about the scope of customised chatbots and the metalanguage within system messages. The discussion of system messages focuses on the challenge of creating consistency with how customised chatbots identify and describe the function of language features when generating feedback messages. The paper argues that this metalanguage should correspond to the metalanguage which students experience in face-to-face teaching and learning as well as online materials. Such continuity involves principled choices about where AI is integrated in teaching and learning sequences. It also involves clarity about the knowledge that students are expected to apply during ‘conversations’ with AI. In this regard, the paper draws attention to a social semiotic reading of Vygotsky's semiotic mediation. It argues that anticipating what is mediated is crucial for the process of customising a chatbot and making new knowledge visible to our students.
{"title":"Customising chatbots for writing development: Anticipating semiotic mediation with the theoretical architecture of systemic functional linguistics","authors":"Lucy Macnaught","doi":"10.1016/j.jeap.2026.101646","DOIUrl":"10.1016/j.jeap.2026.101646","url":null,"abstract":"<div><div>This conceptual paper provides a social semiotic perspective on GenAI technologies in English for Academic Purposes contexts. It focuses on the process of customising AI chatbots to steer how an LLM responds. Through discussing two customised chatbots for Master's of Nursing Science students who are writing research proposals, the paper argues that the theoretical framework of Systemic Functional Linguistics is ideal for chatbot design. Examples use Cogniti software to show how EAP teachers can custom design a chatbot with minimal coding. These examples illustrate how SFL informs decisions about the scope of customised chatbots and the metalanguage within system messages. The discussion of system messages focuses on the challenge of creating consistency with how customised chatbots identify and describe the function of language features when generating feedback messages. The paper argues that this metalanguage should correspond to the metalanguage which students experience in face-to-face teaching and learning as well as online materials. Such continuity involves principled choices about where AI is integrated in teaching and learning sequences. It also involves clarity about the knowledge that students are expected to apply during ‘conversations’ with AI. In this regard, the paper draws attention to a social semiotic reading of Vygotsky's semiotic mediation. It argues that anticipating what is mediated is crucial for the process of customising a chatbot and making new knowledge visible to our students.</div></div>","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101646"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-11DOI: 10.1016/j.jeap.2026.101649
A. Bakogiannis , S. Lorrimer , E. Papavasiliou
Inclusion has become a defining marker of quality and equity in higher education (HE), yet its operationalisation in English for Academic Purposes (EAP) teaching remains underexplored. This paper presents the first empirical phase of a multi-stage BALEAP-funded project investigating inclusive teaching practices in EAP. Using an exploratory qualitative survey of 23 EAP practitioners across diverse institutional roles and global contexts, the study captures how inclusion is understood, enacted, and constrained within English-medium HE environments. Thematic analysis identified two overarching domains: barriers to inclusion, including limited awareness and training, prescriptive curricula, lack of diversity consideration, time constraints, and prohibitive course costs, and approaches to inclusion, encompassing differentiated instruction, culturally responsive pedagogy, reflective practice, personalised learning, and cooperative, student-centred engagement. Findings reveal that while inclusivity is widely endorsed as an ethical and pedagogical imperative, its translation into practice is hindered by structural and institutional limitations. EAP educators often navigate tensions between linguistic rigour and equity, highlighting the need for systemic frameworks that recognise inclusivity as a core professional competency rather than an optional enhancement. The study contributes novel empirical evidence by translating existing inclusion theory into an EAP context and extending the focus from individual practices to an organisational ethos, thereby providing a diagnostic foundation for subsequent project phases that develop practical and policy-oriented recommendations. It argues that meaningful inclusion requires coordinated institutional action aligning policy, curriculum, and professional development to position linguistic and cultural diversity as drivers of educational excellence rather than challenges to be managed.
{"title":"Inclusive pedagogies and practices of English for Academic Purposes (EAP) in higher education (HE): An exploratory survey-based study","authors":"A. Bakogiannis , S. Lorrimer , E. Papavasiliou","doi":"10.1016/j.jeap.2026.101649","DOIUrl":"10.1016/j.jeap.2026.101649","url":null,"abstract":"<div><div>Inclusion has become a defining marker of quality and equity in higher education (HE), yet its operationalisation in English for Academic Purposes (EAP) teaching remains underexplored. This paper presents the first empirical phase of a multi-stage BALEAP-funded project investigating inclusive teaching practices in EAP. Using an exploratory qualitative survey of 23 EAP practitioners across diverse institutional roles and global contexts, the study captures how inclusion is understood, enacted, and constrained within English-medium HE environments. Thematic analysis identified two overarching domains: <em>barriers to inclusion</em>, including limited awareness and training, prescriptive curricula, lack of diversity consideration, time constraints, and prohibitive course costs, and <em>approaches to inclusion</em>, encompassing differentiated instruction, culturally responsive pedagogy, reflective practice, personalised learning, and cooperative, student-centred engagement. Findings reveal that while inclusivity is widely endorsed as an ethical and pedagogical imperative, its translation into practice is hindered by structural and institutional limitations. EAP educators often navigate tensions between linguistic rigour and equity, highlighting the need for systemic frameworks that recognise inclusivity as a core professional competency rather than an optional enhancement. The study contributes novel empirical evidence by translating existing inclusion theory into an EAP context and extending the focus from individual practices to an organisational ethos, thereby providing a diagnostic foundation for subsequent project phases that develop practical and policy-oriented recommendations. It argues that meaningful inclusion requires coordinated institutional action aligning policy, curriculum, and professional development to position linguistic and cultural diversity as drivers of educational excellence rather than challenges to be managed.</div></div>","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101649"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-16DOI: 10.1016/j.jeap.2026.101652
Lijie Hao , Shahazwan Mat Yusoff , Kun Tian
This study investigates how AI-enhanced task-based learning (AI-TBL) affects postgraduate EFL students' higher-order thinking skills (HOTS) and academic writing self-efficacy (SEF) compared with traditional task-based learning (TBL). Guided by constructivist learning theory, a 14-week quasi-experimental design was implemented with 168 postgraduate participants from various disciplines, all enrolled in an English for Academic Purposes (EAP) course at a Chinese university. The experimental group engaged in AI-enhanced instruction via the Yuketang platform, integrating AI-adaptive prompting, AI-generative feedback and AI-supported collaboration, while the control group followed conventional TBL without AI. Data were analyzed using MANCOVA, ANCOVA, and Epistemic Network Analysis (ENA). Findings revealed that AI-TBL significantly improved students’ critical thinking and problem-solving abilities, though its impact on creativity was limited. In academic writing self-efficacy, notable gains emerged in linguistic knowledge, information organization, writing performance, and self-regulation, whereas rehearsal and memory efficacy remained unchanged. The Epistemic Network Analysis (ENA) results reveal that AI-TBL fostered denser interconnections between HOTS and SEF, forming a cohesive structure linking critical thinking, problem-solving, linguistic confidence, and metacognitive control. However, the impact on creativity and rehearsal-memory efficacy was minimal. The study contributes to understanding how AI scaffolding facilitates the relationship between HOTS and SEF in task-based pedagogy, supporting deeper engagement and metacognitive control. Implications for AI-enhanced EAP instruction emphasize the balance between technological adaptivity and human-guided creativity and reflection.
{"title":"Impacts of AI-enhanced task-based learning on EFL postgraduates’ higher order thinking skills and English academic writing self-efficacy","authors":"Lijie Hao , Shahazwan Mat Yusoff , Kun Tian","doi":"10.1016/j.jeap.2026.101652","DOIUrl":"10.1016/j.jeap.2026.101652","url":null,"abstract":"<div><div>This study investigates how AI-enhanced task-based learning (AI-TBL) affects postgraduate EFL students' higher-order thinking skills (HOTS) and academic writing self-efficacy (SEF) compared with traditional task-based learning (TBL). Guided by constructivist learning theory, a 14-week quasi-experimental design was implemented with 168 postgraduate participants from various disciplines, all enrolled in an English for Academic Purposes (EAP) course at a Chinese university. The experimental group engaged in AI-enhanced instruction via the Yuketang platform, integrating AI-adaptive prompting, AI-generative feedback and AI-supported collaboration, while the control group followed conventional TBL without AI. Data were analyzed using MANCOVA, ANCOVA, and Epistemic Network Analysis (ENA). Findings revealed that AI-TBL significantly improved students’ critical thinking and problem-solving abilities, though its impact on creativity was limited. In academic writing self-efficacy, notable gains emerged in linguistic knowledge, information organization, writing performance, and self-regulation, whereas rehearsal and memory efficacy remained unchanged. The Epistemic Network Analysis (ENA) results reveal that AI-TBL fostered denser interconnections between HOTS and SEF, forming a cohesive structure linking critical thinking, problem-solving, linguistic confidence, and metacognitive control. However, the impact on creativity and rehearsal-memory efficacy was minimal. The study contributes to understanding how AI scaffolding facilitates the relationship between HOTS and SEF in task-based pedagogy, supporting deeper engagement and metacognitive control. Implications for AI-enhanced EAP instruction emphasize the balance between technological adaptivity and human-guided creativity and reflection.</div></div>","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101652"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-08DOI: 10.1016/j.jeap.2025.101615
Lei Zhang, Rui Jiang
This study explores the value of adding a diachronic perspective to EAP pedagogy, which is illustrated with a case study that incorporated diachronic changes of the discourse act of graphic data commentary into its teaching. A four-step teaching procedure was implemented in a one-semester academic English writing course for EFL learners. It was found that adding the diachronic content into the teaching process can enhance learners’ awareness of the historical changes in academic language, which in turn assists them to fulfill EAP reading/writing tasks situated in different historical periods.
{"title":"Integrating a diachronic perspective into the teaching of academic discourse acts: A case study of graphic data commentary","authors":"Lei Zhang, Rui Jiang","doi":"10.1016/j.jeap.2025.101615","DOIUrl":"10.1016/j.jeap.2025.101615","url":null,"abstract":"<div><div>This study explores the value of adding a diachronic perspective to EAP pedagogy, which is illustrated with a case study that incorporated diachronic changes of the discourse act of graphic data commentary into its teaching. A four-step teaching procedure was implemented in a one-semester academic English writing course for EFL learners. It was found that adding the diachronic content into the teaching process can enhance learners’ awareness of the historical changes in academic language, which in turn assists them to fulfill EAP reading/writing tasks situated in different historical periods.</div></div>","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101615"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-23DOI: 10.1016/j.jeap.2026.101638
Valentina Morgana, Francesca Poli
This study investigates the impact of AI-assisted task-based language teaching (TBLT) on the acquisition of academic English collocations in spoken discourse. While TBLT has been widely studied, its application to spoken academic collocations remains under-researched. The objectives are (1) to examine whether guided use of a generative AI tool with structured activities enhances collocation learning more effectively than unguided interactions, and (2) to compare AI-assisted instruction with traditional, technology-mediated, non-AI-based approaches in fostering collocational competence. A specialised list of academic spoken collocations was used to inform task design and to identify collocations in learner outputs. Seventy-five B2-level university students in foreign language programs were randomly assigned to one of three groups: an unguided group engaging in open-ended discussions with ChatGPT, a guided group completing structured tasks with AI, and a control group receiving non-AI-based instruction. For the analysis, a subset of 37 participants was examined after data cleaning, producing 259 observations and an average of 8500 words per student. A pretest–posttest design with a delayed posttest was used to assess short- and long-term learning gains, and participant–AI outputs were analysed using linear mixed-effects models. Results indicate that learners in both AI-supported conditions showed higher frequency and accuracy of academic spoken collocations than the non-AI control group. Generally, the unguided interaction with AI was linked to more frequent and accurate use of collocations. Overall, the findings suggest that generative AI can support the development of academic spoken collocations in task-based EAP instruction, highlighting the importance of task design and learner engagement.
{"title":"Guided and unguided GenAI tasks for learning academic spoken collocations","authors":"Valentina Morgana, Francesca Poli","doi":"10.1016/j.jeap.2026.101638","DOIUrl":"10.1016/j.jeap.2026.101638","url":null,"abstract":"<div><div>This study investigates the impact of AI-assisted task-based language teaching (TBLT) on the acquisition of academic English collocations in spoken discourse. While TBLT has been widely studied, its application to spoken academic collocations remains under-researched. The objectives are (1) to examine whether guided use of a generative AI tool with structured activities enhances collocation learning more effectively than unguided interactions, and (2) to compare AI-assisted instruction with traditional, technology-mediated, non-AI-based approaches in fostering collocational competence. A specialised list of academic spoken collocations was used to inform task design and to identify collocations in learner outputs. Seventy-five B2-level university students in foreign language programs were randomly assigned to one of three groups: an unguided group engaging in open-ended discussions with ChatGPT, a guided group completing structured tasks with AI, and a control group receiving non-AI-based instruction. For the analysis, a subset of 37 participants was examined after data cleaning, producing 259 observations and an average of 8500 words per student. A pretest–posttest design with a delayed posttest was used to assess short- and long-term learning gains, and participant–AI outputs were analysed using linear mixed-effects models. Results indicate that learners in both AI-supported conditions showed higher frequency and accuracy of academic spoken collocations than the non-AI control group. Generally, the unguided interaction with AI was linked to more frequent and accurate use of collocations. Overall, the findings suggest that generative AI can support the development of academic spoken collocations in task-based EAP instruction, highlighting the importance of task design and learner engagement.</div></div>","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101638"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-24DOI: 10.1016/j.jeap.2026.101637
Kailun Wang , Luxin Yang , Rui Yuan
This qualitative case study focuses on the boundary-crossing experience of an English teacher during an institutional curriculum reform in a Chinese university. It collected multiple sources of data, including semi-structured interviews, classroom observations, and artifacts (e.g., policy documents and teaching materials). Data analysis shows that Helen's (pseudonym) boundary-crossing experiences in course teaching and academic research through learning mechanisms in terms of identification, coordination, reflection, and transformation. During Helen's boundary crossing, various factors at personal, interpersonal, and institutional levels positioned her as a boundary broker while also inducing tensions and challenges. The study illustrates how language teachers utilize learning mechanisms to transform their habitual ways of sense-making and reconstruct professional identities during boundary-crossing experiences. It also contributes to our understanding of teachers' boundary crossing as a multilevel, non-linear, complex, and sometimes stakes-laden practice. The study provides practical recommendations for language teachers' professional development and curriculum reforms.
{"title":"From English to ESAP: Probing the boundary-crossing experience of a language teacher in a Chinese university","authors":"Kailun Wang , Luxin Yang , Rui Yuan","doi":"10.1016/j.jeap.2026.101637","DOIUrl":"10.1016/j.jeap.2026.101637","url":null,"abstract":"<div><div>This qualitative case study focuses on the boundary-crossing experience of an English teacher during an institutional curriculum reform in a Chinese university. It collected multiple sources of data, including semi-structured interviews, classroom observations, and artifacts (e.g., policy documents and teaching materials). Data analysis shows that Helen's (pseudonym) boundary-crossing experiences in course teaching and academic research through learning mechanisms in terms of identification, coordination, reflection, and transformation. During Helen's boundary crossing, various factors at personal, interpersonal, and institutional levels positioned her as a boundary broker while also inducing tensions and challenges. The study illustrates how language teachers utilize learning mechanisms to transform their habitual ways of sense-making and reconstruct professional identities during boundary-crossing experiences. It also contributes to our understanding of teachers' boundary crossing as a multilevel, non-linear, complex, and sometimes stakes-laden practice. The study provides practical recommendations for language teachers' professional development and curriculum reforms.</div></div>","PeriodicalId":47717,"journal":{"name":"Journal of English for Academic Purposes","volume":"80 ","pages":"Article 101637"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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