A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats

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This study aims to evaluate the role of AI in applied linguistics through a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Methods Using a sample derived from Scopus and Web of Science, we identified relevant studies by applying specific search terms. Our qualitative research design used the SWOT framework to systematically review and analyse studies, ensuring a robust synthesis of data. Results The results of our SWOT analysis revealed the following: 1) Strengths: Enhanced educational tools and resources through AI-driven personalization and interactive learning, increased efficiency and accessibility in generating educational materials, and innovative research applications leveraging semantic similarity measures and advanced linguistic analyses. 2) Weaknesses: Quality and accuracy concerns in AI-generated content, potential over-dependence on AI tools leading to diminished student creativity and ethical issues, and technical limitations in handling complex academic writing tasks. 3) Opportunities: Potential for educational innovation and pedagogical integration, advancements in AI technology to support linguistic research, and fostering global collaboration and access through AI tools. 4) Threats: Risks to academic integrity due to AI-generated content, technological and implementation challenges, and privacy and security concerns regarding data handling. Conclusions Based on the SWOT analysis, we introduced a strategic plan to maximize strengths and opportunities while addressing weaknesses and threats. The strategy includes promoting personalized learning through AI tools, streamlining the creation of educational materials, fostering innovative research applications, ensuring human oversight to maintain content quality, developing ethical guidelines to prevent misuse, investing in necessary infrastructure and training, and implementing robust data protection measures. 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F1000Research 2025, 13 :1040 ( https://doi.org/10.12688/f1000research.155378.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] Obied Alaqlobi 1 , Ahmed Alduais https://orcid.org/0000-0003-0837-4915 2 , Fawaz Qasem 1 , Muhammad Alasmari 1 Obied Alaqlobi 1 , Ahmed Alduais https://orcid.org/0000-0003-0837-4915 2 , Fawaz Qasem 1 , Muhammad Alasmari 1 PUBLISHED 17 Feb 2025 Author details Author details 1 Department of English Language & Literature, College of Arts of Letters, University of Bisha, Bisha, Aseer Province, Saudi Arabia 2 Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Veneto, Italy Obied Alaqlobi Roles: Conceptualization, Funding Acquisition, Investigation, Project Administration, Resources, Writing – Original Draft Preparation Ahmed Alduais Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Fawaz Qasem Roles: Conceptualization, Data Curation, Investigation, Resources, Validation, Writing – Original Draft Preparation Muhammad Alasmari Roles: Conceptualization, Data Curation, Investigation, Resources, Validation, Writing – Original Draft Preparation OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background The integration of generative Artificial Intelligence (AI), particularly large language models (LLMs) like ChatGPT and Gemini, into the field of applied linguistics presents transformative opportunities alongside notable challenges. This study aims to evaluate the role of AI in applied linguistics through a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Methods Using a sample derived from Scopus and Web of Science, we identified relevant studies by applying specific search terms. Our qualitative research design used the SWOT framework to systematically review and analyse studies, ensuring a robust synthesis of data. Results The results of our SWOT analysis revealed the following: 1) Strengths: Enhanced educational tools and resources through AI-driven personalization and interactive learning, increased efficiency and accessibility in generating educational materials, and innovative research applications leveraging semantic similarity measures and advanced linguistic analyses. 2) Weaknesses: Quality and accuracy concerns in AI-generated content, potential over-dependence on AI tools leading to diminished student creativity and ethical issues, and technical limitations in handling complex academic writing tasks. 3) Opportunities: Potential for educational innovation and pedagogical integration, advancements in AI technology to support linguistic research, and fostering global collaboration and access through AI tools. 4) Threats: Risks to academic integrity due to AI-generated content, technological and implementation challenges, and privacy and security concerns regarding data handling. Conclusions Based on the SWOT analysis, we introduced a strategic plan to maximize strengths and opportunities while addressing weaknesses and threats. The strategy includes promoting personalized learning through AI tools, streamlining the creation of educational materials, fostering innovative research applications, ensuring human oversight to maintain content quality, developing ethical guidelines to prevent misuse, investing in necessary infrastructure and training, and implementing robust data protection measures. READ ALL READ LESS Keywords Artificial Intelligence, Applied Linguistics, Generative AI, Large Language Models, Innovative Technology, SWOT Analysis, Educational Technology Corresponding Author(s) Ahmed Alduais ( [email protected] ) Close Corresponding author: Ahmed Alduais Competing interests: No competing interests were disclosed. Grant information: The Promising initiative Project under grant number (UB-Promising-29-1445). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Alaqlobi O et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Alaqlobi O, Alduais A, Qasem F and Alasmari M. A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.12688/f1000research.155378.2 ) First published: 10 Sep 2024, 13 :1040 ( https://doi.org/10.12688/f1000research.155378.1 ) Latest published: 17 Feb 2025, 13 :1040 ( https://doi.org/10.12688/f1000research.155378.2 ) Revised Amendments from Version 1 This revised version of the article incorporates significant updates to address feedback from peer reviewers and improve the depth, clarity, and critical engagement of the analysis. The literature review has been expanded to include seminal works and foundational studies on the integration of Generative AI in applied linguistics, providing a stronger theoretical grounding and addressing gaps in the existing literature. The SWOT analysis has been deepened to offer a more nuanced and critical examination of the strengths, weaknesses, opportunities, and threats of Generative AI in this field. Case studies and examples have been added to illustrate how specific factors manifest in language teaching, research, and global collaboration, ensuring a more reflective and analytical narrative. The discussion now includes actionable strategies tailored to the applied linguistics domain, emphasizing the development of ethical guidelines, enhancement of AI literacy, investment in infrastructure, and promotion of global collaboration to maximize the benefits of Generative AI while mitigating its risks. The revised manuscript critically examines the popular discourse promoted by tech company marketing departments, particularly in the discussion and limitations sections. This includes a reflective analysis of the ethical challenges and technical limitations of AI tools, ensuring a balanced and critical perspective. The methodology section has been expanded to provide more specifics on the thematic synthesis process, ensuring replicability. A table summarizing the search strategy has also been added, detailing the databases used, search terms, inclusion/exclusion criteria, and final sample size. Finally, the manuscript has undergone editorial refinement to improve readability and coherence. Transitions between sections have been strengthened, and subheadings have been added within the discussion section to organize the analysis of SWOT factors. These revisions aim to enhance the manuscript’s contribution to the field, ensuring it provides a comprehensive, critical, and actionable analysis of Generative AI’s role in applied linguistics. This revised version of the article incorporates significant updates to address feedback from peer reviewers and improve the depth, clarity, and critical engagement of the analysis. The literature review has been expanded to include seminal works and foundational studies on the integration of Generative AI in applied linguistics, providing a stronger theoretical grounding and addressing gaps in the existing literature. The SWOT analysis has been deepened to offer a more nuanced and critical examination of the strengths, weaknesses, opportunities, and threats of Generative AI in this field. Case studies and examples have been added to illustrate how specific factors manifest in language teaching, research, and global collaboration, ensuring a more reflective and analytical narrative. The discussion now includes actionable strategies tailored to the applied linguistics domain, emphasizing the development of ethical guidelines, enhancement of AI literacy, investment in infrastructure, and promotion of global collaboration to maximize the benefits of Generative AI while mitigating its risks. The revised manuscript critically examines the popular discourse promoted by tech company marketing departments, particularly in the discussion and limitations sections. This includes a reflective analysis of the ethical challenges and technical limitations of AI tools, ensuring a balanced and critical perspective. The methodology section has been expanded to provide more specifics on the thematic synthesis process, ensuring replicability. A table summarizing the search strategy has also been added, detailing the databases used, search terms, inclusion/exclusion criteria, and final sample size. Finally, the manuscript has undergone editorial refinement to improve readability and coherence. Transitions between sections have been strengthened, and subheadings have been added within the discussion section to organize the analysis of SWOT factors. These revisions aim to enhance the manuscript’s contribution to the field, ensuring it provides a comprehensive, critical, and actionable analysis of Generative AI’s role in applied linguistics. See the authors' detailed response to the review by Peter Bannister See the authors' detailed response to the review by Osamah Mohammed Alyasiri READ REVIEWER RESPONSES Introduction AI has become an unavoidable force in research and numerous aspects of our lives. Sam Altman, CEO of OpenAI, made a striking statement about AI’s potential to dramatically reshape the world, while noting that companies will need to adapt to its rapid changes (as cited in Streitfeld, 2023 ). This assertion underscores AI’s current prominence as a topic of discussion, regardless of its ultimate veracity. Altman’s perspective reflects the growing recognition of AI’s transformative potential across various sectors, including applied linguistics. While there are many proposed definitions of AI, they generally converge around the concept of creating computer programs or machines capable of exhibiting behaviour we would consider intelligent if displayed by humans ( Kaplan, 2016 ). Kaplan’s definition emphasizes the goal of replicating human-like intelligence in artificial systems, which has profound implications for fields like applied linguistics. This intelligent behaviour in AI systems is achieved through extensive exposure to and training on large datasets, leading to the capability of human language generation. The mechanism behind this generation is known as LLMs ( Birhane et al., 2023 ). Birhane and colleagues’ work highlights the critical role of LLMs in enabling AI systems to process and generate human-like language, a capability that is particularly relevant to applied linguistics. Prominent examples of LLMs employed as generative AI across various scientific fields, including applied linguistics, are the OpenAI GPT series, Google’s Gemini, and Microsoft’s Copilot. These models represent the cutting edge of AI technology in language processing and generation. The focus of this paper is to examine the SWOT (Strengths, Weaknesses, Opportunities, Threats) aspects of generative AI in applied linguistics. Leigh (2009, p. 115) defines the SWOT framework as: a process by which a group of stakeholders (a) identify internal and external inhibitors and enhancers of performance, (b) analyse those factors based on estimates of their contributions to net value and approximations of their controllability, and (c) decide what future action to take about those factors. This definition underscores the comprehensive nature of SWOT analysis in evaluating both internal and external factors affecting AI’s integration into applied linguistics. The application of SWOT analysis in applied linguistics is not novel. Al-Mutawa et al. (2024) utilized SWOT analysis to identify previously unexamined strengths and weaknesses in the writing performance of Kuwaiti ESL learners. Their study demonstrates the utility of SWOT analysis in uncovering nuanced aspects of language learning that might otherwise go unnoticed. Ge et al. (2023) examined the sustainability of language development using SWOT analysis, particularly focusing on the digitalization for sustainable foreign language education. Their work highlights the potential of SWOT analysis in addressing broader, systemic issues in language education. Tafazoli (2022) employed SWOT analysis to present English language teachers’ attitudes toward computer-assisted language learning in Spain. This study showcases the versatility of SWOT analysis in capturing complex attitudinal data from educators. More recently, Farrokhnia et al. (2024) analyzed ChatGPT in educational practice using SWOT analysis and provided recommendations. While Farrokhnia et al.’s paper explored the application of generative AI in higher education broadly, our study specifically focuses on the application of generative AI in the field of applied linguistics. Our study differs from previous research by providing a comprehensive SWOT analysis of generative AI, particularly LLMs like ChatGPT and Gemini, in the specific context of applied linguistics. While existing studies have explored aspects of AI in language teaching and learning, there remains a significant gap in the literature regarding a holistic analysis of AI’s multifaceted impacts in this field. By leveraging the SWOT framework, our research aims to offer a structured and actionable approach to maximizing benefits and mitigating risks associated with AI integration in applied linguistics. Seminal works on the integration of AI in education and linguistics provide a foundational understanding of its transformative potential. For example, Kaplan (2016) offers a comprehensive overview of AI’s capabilities and ethical considerations, while Birhane et al. (2023) explore the implications of LLMs in scientific research. Additionally, Farrokhnia et al. (2024) provide a detailed SWOT analysis of ChatGPT in educational practice, offering insights into its strengths and weaknesses. These studies collectively highlight the need for robust frameworks to guide the ethical and effective use of AI in applied linguistics. Generative AI has emerged as a transformative force in applied linguistics, with applications ranging from personalized language learning to automated assessment and multilingual education ( Bin-Hady et al., 2023 ; Hatmanto & Sari, 2023 ). Despite its potential, the integration of AI in this field remains underexplored, with limited research examining its ethical, technical, and pedagogical implications. Our study aims to fill this gap by conducting a comprehensive SWOT analysis of generative AI in applied linguistics, synthesizing findings from diverse studies to provide actionable insights for stakeholders. The primary aim of this study is to conduct a SWOT analysis to evaluate the integration of generative AI, specifically LLMs, in applied linguistics. We seek to identify and analyse the strengths, weaknesses, opportunities, and threats associated with AI usage in language teaching and learning, and to develop actionable strategies that maximize benefits while mitigating risks. The SWOT analysis was chosen for its structured approach in evaluating the complex impacts of AI in applied linguistics. This method allows for a systematic examination of both internal and external factors, providing a balanced view of positive and negative aspects associated with AI integration. By categorizing findings into strengths, weaknesses, opportunities, and threats, our SWOT analysis facilitates a nuanced understanding of how AI can enhance educational tools, improve efficiency, and foster innovative research, while also identifying potential challenges such as quality concerns, ethical issues, and technical limitations. This holistic approach ensures that our analysis is not only thorough but also actionable, enabling the development of strategic plans that maximize benefits and mitigate risks. Furthermore, our use of the SWOT method aligns with previous research in the field, providing a familiar and validated framework for stakeholders to engage with and apply the findings effectively. Methods Qualitative approach and research paradigm This study employed a qualitative research design, specifically a systematic review of the literature guided by the SWOT framework. This approach was chosen for its suitability in analysing complex phenomena, such as the integration of AI in applied linguistics. The SWOT framework allows for a structured and comprehensive examination of both internal factors (strengths and weaknesses) and external factors (opportunities and threats) influencing a particular phenomenon. In this context, the SWOT analysis helps identify and evaluate the potential benefits, challenges, prospects, and risks associated with incorporating AI technologies, particularly LLMs like GPT, into the field of applied linguistics. This study aligns with a postpositivist paradigm, acknowledging the existence of objective realities while recognizing the subjective interpretation of data by researchers. The postpositivist lens allows for a balanced approach, recognizing the influence of researcher perspectives while striving for objectivity in data analysis. The paper is reported according to the Standards for Reporting Qualitative Research (SRQR) ( O’Brien et al., 2014 ). The extracted data, analysis outline, PRISMA flowchart ( Page et al. 2021 ), and SRQR checklist are all accessible at https://doi.org/10.17605/OSF.IO/8AGCZ ( Alaqlobi et al. 2024 ). Researcher characteristics and reflexivity The research team consisted of four authors with diverse expertise in applied linguistics and AI. This multidisciplinary composition allowed for a comprehensive understanding of the research topic from various perspectives, minimizing potential biases and enriching the analysis. The team brought together a range of experiences in language teaching, second language acquisition, computational linguistics, and AI development, ensuring a well-rounded approach to examining the intersection of these fields. Throughout the study, the team engaged in regular reflexive discussions to critically examine their assumptions, interpretations, and potential influence on the research process. These discussions helped ensure that the analysis remained grounded in the data while acknowledging the researchers’ own positions within the field. Context This study was conducted within the rapidly evolving landscape of AI and its applications in education. The research specifically focused on the field of applied linguistics, examining the potential of generative AI, particularly LLMs like GPT, to impact language teaching and learning. The findings and implications of this study are situated within this specific context, recognizing the transformative potential of AI in reshaping educational practices and language learning experiences. The study acknowledges the growing interest in utilizing AI for personalized learning, automated language assessment, and the development of intelligent tutoring systems, all of which have significant implications for applied linguistics research and practice. Sampling strategy The sample for this study was derived from two major academic databases: Scopus and Web of Science. These databases were chosen for their comprehensive coverage of peer-reviewed literature in linguistics and applied linguistics, ensuring a representative sample of relevant research. The search was conducted on December 23, 2023, using a combination of keywords and search strings designed to capture studies related to generative AI and its applications in various subfields of linguistics. The search terms included terms like “GPT,” “Generative AI,” “Language Models,” “Applied Linguistics,” “Language Teaching,” “Language Learning," and other related terms. The initial search yielded 132 hits. After removing duplicates and irrelevant studies based on title and abstract screening, 121 studies remained. A full-text review further narrowed the sample to 24 studies, excluding editorials, correspondences, and studies that did not directly address the research question. The final sample size was determined by the principle of saturation, where no new relevant information emerged from the reviewed studies, indicating that the sample was sufficient to provide a comprehensive understanding of the topic. TITLE (“GPT” AND “Linguistics”) OR TITLE (“GPT” AND “applied linguistics”) OR TITLE (“GPT” AND “Phonetics”) OR TITLE (“GPT” AND “Phonology”) OR TITLE (“GPT” AND “Morphology”) OR TITLE (“GPT” AND “Syntax”) OR TITLE (“GPT” AND “Semantics”) OR TITLE (“GPT” AND “Pragmatics”) OR TITLE (“GPT” AND “Vocabulary”) OR TITLE (“GPT” AND “Reading”) OR TITLE (“GPT” AND “Writing”) OR TITLE (“GPT” AND “Speaking”) OR TITLE (“GPT” AND “Listening”) OR TITLE (“GPT” AND “Pragmatics”) OR TITLE (“GPT” AND “Language teaching”) OR TITLE (“GPT” AND “Language learning”) OR TITLE (“changes” AND “Language teaching”) OR SRCTITLE (“changes” AND “Language learning”)). Ethical issues pertaining to human subjects This study did not involve direct interaction with human subjects, relying solely on the analysis of published research articles. The data extracted from these articles were publicly available and did not contain any personally identifiable information. Therefore, ethical approval from an institutional review board and participant consent were not required. However, the research team adhered to ethical guidelines for research integrity, ensuring accurate and transparent reporting of findings and proper attribution of sources. Data collection methods Data collection involved a systematic review of the 24 selected studies. The research team developed a standardized data extraction form to ensure consistency and minimize bias in data collection. This form included fields for recording key information from each study, such as publication details, research aims, methodologies, key findings, and author conclusions related to the strengths, weaknesses, opportunities, and threats of AI in applied linguistics. The data extraction process involved carefully reviewing the full text of each article and extracting relevant information according to the predefined categories on the data extraction form. Data collection instruments and technologies No specific data collection instruments, such as surveys or interviews, were used in this study. The primary tools for data collection were the Scopus and Web of Science databases and their respective search interfaces. These platforms provided access to the research articles and facilitated the initial search and retrieval process. Additionally, reference management software was used to organize and manage the selected articles. Units of study The units of study were the 24 selected research articles that met the inclusion criteria. These articles represented a diverse range of research designs, methodologies, and perspectives on the application of AI in applied linguistics, providing a rich and multifaceted understanding of the field. The articles included both empirical studies and theoretical discussions, offering a balanced perspective on the practical applications and conceptual implications of AI in language learning and teaching. Data processing Data extracted from the selected studies were organized and managed using spreadsheet software. The data were systematically categorized according to the four quadrants of the SWOT framework: strengths, weaknesses, opportunities, and threats. This involved analysing the extracted data for recurring themes, patterns, and key findings related to each SWOT category. No data transformation or coding was required as the analysis focused on thematic synthesis of qualitative information. Data analysis Data analysis involved a thematic synthesis approach guided by the SWOT framework. The research team independently reviewed the extracted data and collaboratively identified recurring themes and patterns related to the strengths, weaknesses, opportunities, and threats of AI in applied linguistics. These themes were then discussed, refined, and organized within the SWOT framework to provide a structured overview of the findings. The thematic synthesis process involved identifying initial codes and themes, grouping similar codes into broader categories, and then developing overarching themes that captured the essence of the findings. The thematic synthesis was conducted in three stages: 1. Initial Coding: Each researcher independently reviewed the extracted data and identified initial codes related to strengths, weaknesses, opportunities, and threats. 2. Theme Development: Similar codes were grouped into broader categories, which were then refined into overarching themes through iterative discussions. 3. Validation: Themes were cross-verified by the research team to ensure consistency and accuracy, with discrepancies resolved through consensus. Techniques to enhance trustworthiness Several techniques were employed to enhance the trustworthiness of the findings. These included: • Triangulation: Using multiple databases (Scopus and Web of Science) and researchers with diverse expertise to ensure a comprehensive and balanced perspective, minimizing the limitations of relying on a single source or viewpoint. • Iterative analysis: The research team engaged in iterative discussions throughout the data analysis process to challenge interpretations, refine themes, and reach consensus on the findings, ensuring that the analysis was thorough and reflected the collective insights of the team. • Thick description: The research process and findings are presented with rich detail to enhance transparency and allow readers to assess the study’s rigor and transferability. Providing detailed descriptions of the methodology, data analysis procedures, and the researchers’ reflections on the process contributes to the study’s trustworthiness by making the research process transparent and replicable. Findings Strengths of using AI in applied linguistics The advent of AI in the field of applied linguistics has opened new avenues for enhancing educational tools and resources, increasing efficiency and accessibility, and fostering innovative research applications. These strengths, as highlighted by numerous studies, underscore the transformative potential of AI technologies like ChatGPT in language education and research. This essay delves into three primary strengths of AI in applied linguistics: enhanced educational tools and resources, increased efficiency and accessibility, and innovative research applications. Enhanced educational tools and resources AI technologies, particularly ChatGPT, have significantly enhanced educational tools and resources in language teaching and learning. ChatGPT provides authentic texts, supports personalized content, and caters to diverse learning styles, thus enriching the educational experience ( Kohnke et al., 2023 ; Özdemir-Çağatay, 2023 ). The ability to simulate human-like conversations allows for practical language learning through interactive narrative construction and simulated discourse ( Hatmanto & Sari, 2023 ). This interactive capability is crucial in fostering active engagement and learner autonomy, which are essential components of effective language learning ( Hatmanto & Sari, 2023 ). Moreover, the integration of AI into language education aligns well with established theoretical frameworks such as Communicative Language Teaching and Constructivist Learning Theory. ChatGPT’s ability to promote authentic language use, collaborative learning, and practical application of knowledge is consistent with these educational paradigms ( Hatmanto & Sari, 2023 ). For instance, virtual Socratic dialogues and interactive narrative construction facilitated by ChatGPT can significantly enhance learner engagement and fluency ( Hatmanto & Sari, 2023 ). Thus, AI tools not only complement traditional educational methods but also introduce innovative approaches that align with modern pedagogical theories. Increased efficiency and accessibility Increased efficiency and accessibility represent another significant strength of AI in applied linguistics. AI tools like ChatGPT streamline the process of generating educational materials, thereby reducing the workload for educators, and making language learning more accessible ( Zografos & Moussiades, 2023 ; Zuckerman et al., 2023 ). For example, AI-driven platforms can automatically generate vocabulary lists, writing prompts, and assessment items, which are essential resources for language educators ( Zografos & Moussiades, 2023 ). This automation allows educators to focus more on higher order teaching tasks, thereby improving the overall quality of education ( Zuckerman et al., 2023 ). Furthermore, AI technologies can significantly assist non-native English-speaking researchers by providing language refinement and feedback, thus lowering the barriers to academic writing in English ( Hwang et al., 2023 ). This support is particularly valuable for researchers who may struggle with the intricacies of academic English, enabling them to produce high-quality scholarly work ( Hwang et al., 2023 ). By democratizing access to academic resources, AI tools contribute to more inclusive and equitable educational and research environments ( Hwang et al., 2023 ). Innovative research applications Innovative research applications of AI in linguistics further highlight its transformative potential. LLMs like GPT-4o, Gemin 1.5, ChatGPT, etc. ( Poe ) can construct measures of semantic similarity that align closely with human judgment, thereby enhancing research methodologies in social sciences and linguistics ( Le Mens et al., 2023 ). This capability allows researchers to conduct complex analyses of language use, metapragmatics, and metadiscourse, providing deeper insights into linguistic phenomena ( Dynel, 2023 ). For instance, the ability to measure typicality and semantic similarity using AI tools can significantly advance research in sociolinguistics and cognitive linguistics ( Le Mens et al., 2023 ). Additionally, AI tools offer new paradigms for understanding language dynamics and interaction. By analysing user interactions with AI, researchers can gain insights into metalinguistic, meta-discursive, metacommunicative, and metapragmatic awareness ( Dynel, 2023 ). These insights are crucial for developing more effective language teaching strategies and for advancing theoretical understandings of language use ( Dynel, 2023 ). The integration of AI into linguistic research not only enhances methodological rigor but also opens new research questions and areas of inquiry ( Le Mens et al., 2023 ). Prompts in Figure 1 highlight the strengths of AI by showcasing its ability to personalize education, streamline resource creation, and enhance research methodologies. They also demonstrate how AI can effectively support diverse learning needs, improve efficiency in educational settings, and contribute to innovative research practices. Figure 1. Three GPT-4o prompts demonstrating strengths of using LLMs in applied linguistics. Weaknesses of using AI in applied linguistics While the integration of AI into applied linguistics offers numerous strengths, it is crucial to acknowledge the inherent weaknesses that accompany its adoption. These weaknesses include concerns about the quality and accuracy of AI-generated content, the potential for over-dependence and ethical issues, and various technical limitations. This essay will explore these three primary weaknesses in detail, drawing on evidence from relevant studies. Quality and accuracy concerns One of the most significant weaknesses of AI in linguistics is the quality and accuracy of AI-generated content. Despite advancements in AI technology, tools like ChatGPT often produce content that requires substantial human validation due to issues with accuracy, bias, and the generation of erroneous information ( Alkaissi & McFarlane, 2023 ; Silva et al., 2023 ). Studies have shown that AI-generated texts can contain factual inaccuracies and inconsistencies, which can mislead users and compromise the integrity of educational materials ( Dashti et al., 2023 ; Wu & Dang, 2023 ). For instance, AI tools have been found to generate incorrect references and citations, posing a significant challenge in academic writing ( Dashti et al., 2023 ). Furthermore, AI’s limitations in handling deep linguistic structures and complex syntactic nuances are evident. Research comparing AI-generated texts to human-written content reveals that AI often underperforms in areas such as deep cohesion and syntactic complexity ( Zhou et al., 2023 ). ChatGPT, while proficient in generating fluent text, struggles with maintaining logical coherence and producing sophisticated linguistic constructs that are essential for high-quality academic writing ( Zhou et al., 2023 ). These deficiencies necessitate ongoing human oversight and intervention to ensure the reliability and credibility of AI-generated content. Dependence and ethical issues Another critical weakness is the potential for over-dependence on AI tools, which raises ethical concerns. The convenience and efficiency offered by AI can lead to a reduction in human effort and creativity, potentially stifling student innovation and critical thinking ( Özdemir-Çağatay, 2023 ). The over-reliance on AI-generated content can diminish students’ engagement in the learning process and undermine their development of essential academic skills ( Hatmanto & Sari, 2023 ; Özdemir-Çağatay, 2023 ). This dependency not only affects students but also educators, who may become reliant on AI for lesson planning and content creation, thereby reducing their active involvement in pedagogical practices ( Özdemir-Çağatay, 2023 ). Ethical issues also arise concerning plagiarism and academic integrity. The use of AI tools in academic writing can blur the lines of authorship, leading to potential instances of plagiarism if AI-generated content is not properly attributed ( Jarrah et al., 2023 ). There is an urgent need for clear guidelines and ethical standards to govern the use of AI in educational and research contexts to prevent misuse and ensure transparency ( Doyal et al., 2023 ; Mondal & Mondal, 2023 ). The lack of such regulations poses a significant threat to the integrity of academic work and the credibility of scholarly publications ( Jarrah et al., 2023 ). Technical limitations Technical limitations further compound the weaknesses of AI in applied linguistics. Despite significant advancements, AI tools like ChatGPT still exhibit limitations in performing complex academic tasks, such as accurately generating references and conducting comprehensive literature reviews ( Dashti et al., 2023 ; Liu et al., 2023 ). The inability of AI to verify the accuracy of references and the potential for generating fabricated or non-existent citations are major drawbacks ( Dashti et al., 2023 ). These issues highlight the need for continuous improvements in AI algorithms and the incorporation of more sophisticated validation mechanisms ( Liu et al., 2023 ). Moreover, the current state of AI technology does not fully support the nuanced and context-specific requirements of academic writing. While AI tools can assist in generating text and providing language feedback, they lack the depth of understanding and contextual awareness necessary for high-level scholarly work ( Liu et al., 2023 ; Zhu et al., 2023 ). For example, AI-generated content often fails to capture the subtleties of academic discourse, and the rhetorical strategies employed by proficient human writers ( Zhu et al., 2023 ). This limitation underscores the necessity for human expertise in refining and contextualizing AI-generated outputs. Figure’s 2 prompts highlight potential weaknesses in AI by requesting tasks that require nuanced understanding, accurate sourcing, and original thought, areas where AI can falter. They expose potential ethical issues by encouraging the omission of citations and prompting the AI to generate content intended for direct submission without attribution. Figure 2. Three Gemini-1.5-Pro prompts demonstrating weaknesses of using LLMs in applied linguistics. Opportunities of using AI in applied linguistics The integration of AI into applied linguistics presents a multitude of opportunities that can significantly enhance the field. These opportunities span across educational innovation and pedagogical integration, research and development, and global collaboration and access. This essay explores these three primary opportunities, highlighting their potential to transform language education and research. Educational innovation and pedagogical integration One of the most promising opportunities of AI in linguistics is the potential for educational innovation and pedagogical integration. AI tools like ChatGPT can be leveraged to create highly personalized learning experiences that cater to individual student needs ( Bin-Hady et al., 2023 ; Hatmanto & Sari, 2023 ). For instance, AI can facilitate differentiated instruction by adapting content to match the proficiency levels and learning styles of different students, thereby enhancing engagement, and learning outcomes ( Hatmanto & Sari, 2023 ). The ability to simulate human-like conversations enables students to practice language skills in realistic contexts, promoting active learning and improving fluency ( Hatmanto & Sari, 2023 ). Furthermore, AI can support innovative instructional strategies that align with contemporary pedagogical frameworks. For example, AI-driven platforms can facilitate virtual Socratic dialogues, interactive narrative construction, and simulated discourse, which are effective in fostering critical thinking and collaborative learning ( Hatmanto & Sari, 2023 ). These strategies not only enrich the learning experience but also align with theories such as Communicative Language Teaching and Constructivist Learning Theory, which emphasize the importance of interaction and practical application in language learning ( Hatmanto & Sari, 2023 ). By integrating AI into the classroom, educators can create more dynamic and effective learning environments that support student autonomy and engagement. Research and development Research and development represent another significant opportunity offered by AI in applied linguistics. AI technologies, particularly LLMs like GPT-4, can enhance research methodologies by providing sophisticated tools for linguistic analysis ( Le Mens et al., 2023 ). For instance, AI can be used to construct measures of semantic similarity and typicality that align closely with human judgment, thereby improving the accuracy and reliability of linguistic research ( Le Mens et al., 2023 ). These capabilities enable researchers to conduct complex analyses of language use, metapragmatics, and metadiscourse, offering deeper insights into linguistic phenomena ( Dynel, 2023 ). Additionally, AI tools can facilitate new research paradigms by automating data collection and analysis processes, thus increasing efficiency, and reducing the time required for research ( Le Mens et al., 2023 ). For example, AI can automatically generate large corpora of text for analysis, identify patterns and trends in language use, and provide real-time feedback on research hypotheses ( Le Mens et al., 2023 ). This automation allows researchers to focus on higher-order analytical tasks and theoretical development, potentially leading to significant advancements in the field of linguistics ( Le Mens et al., 2023 ). Moreover, the continuous improvement of AI algorithms promises to further enhance the capabilities and applications of AI in linguistic research. Global collaboration and access Global collaboration and access are also greatly enhanced by the integration of AI in linguistics. AI tools like ChatGPT can facilitate cross-cultural and multilingual collaboration by providing accessible language support to researchers and educators worldwide ( Li, Bonk, et al., 2023a ; Li, Kou, et al., 2023b ). This democratization of access to language resources enables non-native English speakers to participate more fully in global academic discourse, contributing to a more inclusive and diverse research community ( Hwang et al., 2023 ). For instance, AI can assist researchers in refining their academic writing, translating scholarly work, and providing real-time language support during international collaborations ( Hwang et al., 2023 ). Moreover, AI technologies can support the development of language learning resources in multiple languages, addressing the needs of diverse linguistic communities ( Li, Bonk, et al., 2023a ). By generating educational content in various languages and adapting it to diverse cultural contexts, AI can help bridge language barriers and promote cross-cultural understanding ( Li, Bonk, et al., 2023a ). This capability is particularly valuable in multilingual societies and international educational environments, where the ability to access and share knowledge across languages is crucial ( Li, Bonk, et al., 2023a ). As AI continues to evolve, its potential to facilitate global collaboration and access in linguistics will expand, offering even greater opportunities for innovation and inclusivity. Figure’s 3 prompts highlight opportunities for AI to revolutionize language learning through personalized instruction, advance linguistic research through sophisticated analysis, and bridge global communication gaps through accessible translation tools. They encourage the development of AI tools that cater to the diverse needs of language learners and researchers, fostering innovation and collaboration in the field of linguistics. Figure 3. Three Gemini-1.5-Pro prompts demonstrating opportunities of using LLMs in applied linguistics. Threats of using AI in applied linguistics While the integration of AI into applied linguistics offers numerous opportunities, it also presents several significant threats that must be carefully managed. These threats include concerns about academic integrity and misuse, technological and implementation challenges, and privacy and security issues. This essay explores these three primary threats, highlighting their potential implications for the field of linguistics. Academic integrity and misuse One of the most pressing threats posed by AI in linguistics is the risk to academic integrity and the potential for misuse. The rise of AI-generated content has led to increased concerns about plagiarism and the authenticity of academic work. AI tools like ChatGPT can produce sophisticated texts that are difficult to distinguish from human-written content, raising the risk of students and researchers passing off AI-generated work as their own ( Dergaa et al., 2023 ; Jarrah et al., 2023 ). This threat to academic integrity undermines the credibility of scholarly work and can lead to widespread ethical violations if not effectively managed ( Jarrah et al., 2023 ). Moreover, the lack of clear guidelines and ethical standards for the use of AI in educational and research contexts exacerbates these concerns. There is an urgent need for institutions to develop robust policies that govern the use of AI tools to prevent misuse and ensure transparency ( Doyal et al., 2023 ; Mondal et al., 2023 ; Mondal & Mondal, 2023 ). Without such regulations, the integrity of academic work is at risk, as AI-generated content can blur the lines of authorship and lead to significant ethical dilemmas ( Jarrah et al., 2023 ). Establishing clear guidelines will help maintain trust in academic institutions and uphold the principles of scholarly integrity. Technological and implementation challenges Technological and implementation challenges represent another significant threat to the effective use of AI in applied linguistics. Implementing AI tools in educational settings requires substantial investment in infrastructure, training, and ongoing support, which can be challenging for institutions with limited resources ( Özdemir-Çağatay, 2023 ). The rapid pace of AI development necessitates continuous updates and adaptations to educational practices, posing difficulties for educators and institutions to keep up with the latest advancements ( Le Mens et al., 2023 ). Additionally, the current state of AI technology does not fully support the nuanced and context-specific requirements of academic writing. While AI tools can assist in generating text and providing language feedback, they lack the depth of understanding and contextual awareness necessary for high-level scholarly work ( Liu et al., 2023 ). For instance, AI-generated content often fails to capture the subtleties of academic discourse, and the rhetorical strategies employed by proficient human writers ( Zhu et al., 2023 ). These limitations highlight the need for ongoing human oversight and intervention to ensure the quality and relevance of AI-generated outputs ( Dashti et al., 2023 ). Privacy and security concerns The third major threat posed by AI in linguistics relates to privacy and security issues. The use of AI tools involves managing substantial amounts of sensitive data, raising concerns about data privacy and security ( Vaccino-Salvadore, 2023 ). Ensuring compliance with privacy regulations and protecting user data from breaches and misuse is a critical challenge that must be addressed to safeguard users’ trust and safety ( Doyal et al., 2023 ). For instance, AI platforms like ChatGPT require access to user inputs to function effectively, which can lead to the collection and storage of personal information ( Vaccino-Salvadore, 2023 ). Furthermore, the potential for AI tools to generate biased or misleading information poses a significant risk to the accuracy and reliability of educational content ( Silva et al., 2023 ). AI models are trained on vast datasets that may contain inherent biases, which can be reflected in the outputs generated by these tools ( Silva et al., 2023 ). This can perpetuate stereotypes and misinformation, undermining the educational value of AI-generated content ( Silva et al., 2023 ). Addressing these privacy and security concerns requires robust data protection measures and continuous monitoring to ensure the ethical use of AI in linguistics ( Doyal et al., 2023 ). Figure 4 below demonstrates the misuse of AI in three contexts: direct submission of AI-generated text without attribution, fabrication of references, and misuse of AI for exam answers. Figure 4. Three GPT-4o-128k prompts demonstrating threats of using LLMs in applied linguistics. Applied scenarios for generative AI in applied linguistics Figure 5 demonstrates a scenario of a team of literary translators is translating a novel from a lesser-known indigenous language into English. They utilize an AI-powered translation tool specifically trained on a corpus of similar texts in both languages. The AI tool can provide high-quality initial translations, capturing nuances of language and style that might be missed by traditional machine translation systems. This significantly speeds up the translation process, allowing translators to focus on refining the text and ensuring cultural accuracy. For under-resourced languages with limited translation resources, AI can be a valuable tool for preserving and revitalizing these languages by making their literature accessible to a wider audience. Figure 5. A demonstration of the (Mis)use of generative AI for a scenario of AI-Powered Literary Translation Tool for Under-Resourced Languages. While AI can manage grammar and vocabulary effectively, it might struggle with idiomatic expressions, cultural metaphors, and the overall tone of the original text. Human translators are essential to ensure the translation accurately reflects the cultural context and artistic intent of the source material. The quality of the AI’s output depends heavily on the training data. If the corpus is limited or biased, the AI might produce translations that are inaccurate or perpetuate harmful stereotypes. AI-powered translation tools can make literature from under-represented cultures more accessible to a global audience, fostering cross-cultural understanding and appreciation. The use of AI can free up human translators from tedious tasks, allowing them to focus on higher-level aspects of translation such as style, tone, and cultural adaptation. This can lead to more creative and nuanced translations. There is a concern that AI might replace human translators altogether, leading to job losses in the translation industry. However, a more likely scenario is that AI will augment human capabilities, leading to a collaborative approach to translation. Over-reliance on AI for translation could lead to the homogenization of language, as AI systems tend to favour dominant languages and standardized forms of expression. It is important to use AI in a way that preserves linguistic diversity and supports the richness of different languages and cultures. Figure 6 shows a scenario for a sociolinguist wants to study language variation and change in a specific online community. They use a generative AI tool to analyse a vast corpus of text data from that community. Generative AI can process and analyse massive datasets of text and speech, far exceeding human capabilities. This allows researchers to study language use in a more comprehensive and nuanced way, uncovering patterns and trends that might not be visible through traditional methods. AI can automate time-consuming tasks such as data cleaning, annotation, and analysis, freeing up researchers to focus on interpretation and theory building. This makes large-scale sociolinguistic research more feasible and efficient. Figure 6. A demonstration of the (Mis)use of generative AI for a scenario of AI-Driven Corpus Analysis for Sociolinguistic Research. AI models are only as good as the data they are trained on. If the training data is biased or incomplete, the AI’s analysis might be inaccurate or misleading. Human oversight is crucial to validate the AI’s findings and ensure their reliability. Over-reliance on AI for data analysis might limit researchers’ ability to engage critically with the data and develop their own analytical skills. There is also a risk of perpetuating existing biases if the AI models are not carefully designed and evaluated. AI-driven corpus analysis opens new avenues for sociolinguistic research, allowing for the exploration of complex social phenomena through the lens of language. This can foster collaboration between linguists, computer scientists, and social scientists. The insights gained from AI-powered analysis can lead to the development of new theories about language variation and change, as well as more sophisticated methods for studying language in social contexts. AI-generated results can be easily misinterpreted or misused, especially by those unfamiliar with the limitations of the technology. It is important to communicate findings clearly and responsibly, acknowledging the potential for bias and error. Sociolinguistic research often involves sensitive personal data. Researchers must be mindful of data privacy concerns and ensure that data is collected, stored, and analysed ethically and securely. Discussion A strategy for using AI in applied linguistics The SWOT analysis in the findings above and Figure 7 presented below highlight a complex structure for AI integration in applied linguistics. While the potential benefits are immense, ranging from enhanced educational tools to innovative research applications, significant challenges exist concerning academic integrity, technological limitations, and privacy concerns. This strategy proposes a multi-pronged approach to leverage AI’s strengths and opportunities while proactively mitigating its weaknesses and threats. Figure 7. SWOT analysis of using LLMs in applied linguistics. Prioritising ethical and responsible AI implementation Given the significant threats to academic integrity posed by AI-generated content ( Dergaa et al., 2023 ; Jarrah et al., 2023 ), the cornerstone of this strategy is the development and implementation of robust ethical guidelines. These guidelines should clearly define acceptable AI use in educational and research contexts, emphasizing transparency and proper attribution ( Doyal et al., 2023 ; Mondal & Mondal, 2023 ). Educational institutions must invest in training programs for both educators and students, fostering critical thinking skills and emphasizing the importance of original thought ( Özdemir-Çağatay, 2023 ). Leveraging AI for personalised learning and enhanced efficiency Capitalizing on AI’s strengths, this strategy advocates for the development and integration of AI-powered educational tools that support diverse learning styles and personalize learning experiences ( Bin-Hady et al., 2023 ; Hatmanto & Sari, 2023 ; Kohnke et al., 2023 ; Özdemir-Çağatay, 2023 ). AI can streamline the creation of educational materials, reducing educator workload and facilitating differentiated instruction ( Zografos & Moussiades, 2023 ; Zuckerman et al., 2023 ). However, it is crucial to address the weaknesses related to quality and accuracy concerns. Human oversight and validation of AI-generated content remain essential to ensure accuracy and mitigate potential biases (( Alkaissi & McFarlane, 2023 ; Silva et al., 2023 ). For example, in the context of language teaching, AI tools like ChatGPT have been shown to enhance learner engagement through interactive narrative construction and simulated discourse ( Hatmanto & Sari, 2023 ). However, challenges such as the generation of inaccurate or biased content highlight the need for human oversight and robust ethical guidelines ( Alkaissi & McFarlane, 2023 ). These case studies illustrate the nuanced impact of AI in specific subfields of applied linguistics. Fostering innovation in research and global collaboration This strategy encourages the exploration of AI’s potential to enhance research methodologies and facilitate new research paradigms in applied linguistics ( Le Mens et al., 2023 ). AI can automate data collection and analysis, increasing research efficiency and enabling large-scale studies ( Le Mens et al., 2023 ). Furthermore, AI can facilitate cross-cultural and multilingual collaboration, democratizing access to language resources and promoting cross-cultural understanding ( Hwang et al., 2023 ; Li, Bonk, et al., 2023a ). However, it is crucial to acknowledge the technical limitations of current AI tools. Continuous development and refinement are necessary to address the limitations in performing complex academic tasks and enhance the depth of understanding and contextual awareness exhibited by AI ( Dashti et al., 2023 ; Liu et al., 2023 ; Zhu et al., 2023 ). Addressing privacy and security concerns Recognizing the significant threats posed by privacy and security concerns, this strategy advocates for the implementation of stringent data protection measures and continuous monitoring of AI tools ( Doyal et al., 2023 ; Vaccino-Salvadore, 2023 ). Transparency regarding data collection and usage is crucial, and users must be informed about the potential for AI to generate biased or misleading information ( Silva et al., 2023 ). Ongoing research and development are necessary to mitigate these risks and ensure the ethical and responsible use of AI in applied linguistics. This strategy emphasizes a balanced approach to AI integration in applied linguistics. By prioritizing ethical considerations, leveraging AI’s strengths for educational and research advancements, and proactively addressing its weaknesses and threats, we can harness the transformative potential of AI while mitigating its risks. This strategy calls for collaboration among researchers, educators, policymakers, and technology developers to ensure the responsible and beneficial integration of AI in the field of applied linguistics. The strengths of AI in applied linguistics, such as enhanced educational tools and innovative research applications, align closely with the goals of modern language education. For instance, AI-driven platforms like ChatGPT support Communicative Language Teaching by enabling interactive and personalized learning experiences ( Hatmanto & Sari, 2023 ). However, the weaknesses, including quality concerns and ethical issues, highlight the need for human oversight and robust guidelines to ensure the reliability of AI-generated content ( Alkaissi & McFarlane, 2023 ). These factors underscore the importance of balancing innovation with ethical considerations in the integration of AI in applied linguistics. Conclusions This research aimed at assessing the role of AI in applied linguistics through a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Using a sample from common databases (Scopus and Web of Science) we recognized relevant studies by applying specific search terms of the disciplines. The study was based of qualitative research design that making use of the SWOT framework to reach productive findings. The results of our SWOT analysis indicated that the strengths lied more in the enhancement of educational tools and resources through AI-driven programs, models, the collaborative learning and educational materials and innovative research applications impact and advanced linguistic analyses. The study highlighted those weaknesses involved in quality and accuracy concerns in AI-generated content, potential over-dependence on AI tools that influence students’ creativity and ethical issues, and technical limitations in handling complex academic writing tasks. The research highlighted that opportunities contain the potential for educational innovation and pedagogical integration, advancements in AI technology to support linguistic research, and fostering global collaboration and access through AI tools. Moreover, the findings of the study revealed that Threats include risks to academic integrity due to AI-generated content, technological and implementation challenges, and privacy and security concerns regarding data handling. Based on the SWOT analysis, this research proposed a strategic plan to increase strengths and opportunities while addressing weaknesses and threats. The strategic plan aimed at promoting personalised learning through AI tools, streamlining the creation of educational materials, fostering innovative research applications, ensuring human oversight to maintain content quality, improving ethical guidelines and procedures. This was to prevent misuse of tools and help in training and implementing robust data protection measures and steps. AI has become inevitable in academia and research and this research has several implications that helps in highlighting the different opportunities of the use of AI in applied linguistics fields. It also gives awareness on the different practices and uses of AI in the fields of applied linguistics. Providing the researchers in the field of applied linguistics of the weaknesses and threats helps in building academic measurements and perquisitions to minimize the dangers of misusing AI and ensuring the ethical procedures that save the rights of researchers and original works. Limitations While this study provides a comprehensive SWOT analysis of the integration of generative AI in applied linguistics, several limitations should be acknowledged. The sample of studies was limited to those indexed in Scopus and Web of Science, potentially missing relevant literature from other sources. The qualitative nature of the analysis introduces the potential for subjective interpretation and bias, despite efforts to cross-verify findings. The rapidly evolving nature of AI technology also poses challenges for the timeliness and applicability of the findings, necessitating continuous updates. Additionally, the SWOT framework does not quantify the relative importance of the identified factors, suggesting a need for future research using quantitative methods. Lastly, the study did not delve into specific subfields within applied linguistics, indicating a direction for future research to explore unique implications in areas like language teaching, acquisition, and disorders. Despite these limitations, the study lays a robust foundation for understanding the impacts of generative AI in applied linguistics and offers valuable insights for stakeholders. Theoretical implications The integration of AI, particularly through LLMs like ChatGPT and Gemini, into the field of applied linguistics presents significant theoretical implications. The SWOT analysis conducted in this study reveals how AI can transform linguistic theories and frameworks by providing new tools and methodologies for analysing language use and development. For instance, AI’s ability to measure semantic similarity and linguistic patterns aligns closely with cognitive and sociolinguistic theories, offering deeper insights into language processing and social interactions. Additionally, the use of AI in language education supports constructivist and communicative language teaching theories by enabling more personalized and interactive learning experiences. These theoretical advancements can lead to the refinement of existing models and the development of new theories that better account for the complexities of language learning and use in the digital age. Practical implications From a practical perspective, the findings of this SWOT analysis suggest several actionable strategies for leveraging AI in applied linguistics to enhance educational and research practices. Educational institutions can utilize AI tools to create more efficient and accessible language learning resources, thereby reducing the workload on educators, and providing students with personalized learning experiences. The development of AI-driven platforms for automatic generation of educational materials, such as vocabulary lists and writing prompts, can streamline classroom instruction and support differentiated teaching approaches. Moreover, the integration of AI in research methodologies can enhance data collection and analysis processes, enabling more robust and scalable linguistic studies. However, it is crucial to address the identified weaknesses and threats, such as ensuring the accuracy of AI-generated content and establishing ethical guidelines to prevent misuse. By implementing these practical strategies, stakeholders in the field can maximize the benefits of AI while mitigating potential risks. To maximize the benefits of AI in applied linguistics, we propose the following actionable strategies: 1. Develop Ethical Guidelines: Institutions should establish clear policies on AI use, emphasizing transparency and proper attribution ( Doyal et al., 2023 ). 2. Enhance AI Literacy: Training programs for educators and students should focus on critical thinking and the responsible use of AI tools ( Özdemir-Çağatay, 2023 ). 3. Invest in Infrastructure: Continuous development of AI algorithms and validation mechanisms is necessary to address technical limitations ( Liu et al., 2023 ). 4. Promote Global Collaboration: AI tools should be leveraged to facilitate cross-cultural and multilingual collaboration, fostering inclusivity in academic research ( Hwang et al., 2023 ). Ethical approval This research did not require IRB approval. Informed consent statement Neither human nor non-human subjects were involved directly in this research. Therefore, informed consent was not required. Data availability statement No primary data is associated with this article. Extended data Open Science Framework (OSF): A SWOT Analysis of Generative AI in Applied Linguistics: Leveraging Strengths, Addressing Weaknesses, Seizing Opportunities, and Mitigating Threats, https://doi.org/10.17605/OSF.IO/8AGCZ . This project contains the following underlying data: • PRISMA Flowchart SWOT Applied Linguistics.jpg • Data extraction AI and Linguistics.docx • SRQR_Checklist ready.pdf • SWOT Analysis outline.docx Software availability statement The provided examples and illustrations in this paper were based on LLMs available at https://chatgpt.com/ and https://poe.com/ . Both require subscription to get access to them. Acknowledgments The authors extend their appreciation to the Deanship of Graduate Studies and Scientific Research at the University of Bisha for funding this research through the promising program under grant number (UB-Promising-29-1445). References Alaqlobi O, Alduais A, Qasem F, et al. : A SWOT Analysis of Generative AI in Applied Linguistics: Leveraging Strengths, Addressing Weaknesses, Seizing Opportunities, and Mitigating Threats.2024, August 21. Publisher Full Text Alkaissi H, McFarlane SI: Artificial Hallucinations in ChatGPT: Implications in Scientific Writing. 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Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 10 Sep 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department of English Language & Literature, College of Arts of Letters, University of Bisha, Bisha, Aseer Province, Saudi Arabia 2 Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Veneto, Italy Obied Alaqlobi Roles: Conceptualization, Funding Acquisition, Investigation, Project Administration, Resources, Writing – Original Draft Preparation Ahmed Alduais Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Fawaz Qasem Roles: Conceptualization, Data Curation, Investigation, Resources, Validation, Writing – Original Draft Preparation Muhammad Alasmari Roles: Conceptualization, Data Curation, Investigation, Resources, Validation, Writing – Original Draft Preparation Competing interests No competing interests were disclosed. Grant information The Promising initiative Project under grant number (UB-Promising-29-1445). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 17 Feb 2025, 13:1040 https://doi.org/10.12688/f1000research.155378.2 version 1 Published: 10 Sep 2024, 13:1040 https://doi.org/10.12688/f1000research.155378.1 Copyright © 2025 Alaqlobi O et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Alaqlobi O, Alduais A, Qasem F and Alasmari M. A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.12688/f1000research.155378.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 17 Feb 2025 Revised Views 0 Cite How to cite this report: Mohammed Alyasiri O. Reviewer Report For: A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.5256/f1000research.178064.r367036 ) The direct URL for this report is: https://f1000research.com/articles/13-1040/v2#referee-response-367036 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 18 Feb 2025 Osamah Mohammed Alyasiri , Al-Furat Al-Awsat Technical University, Karbala, Iraq Approved VIEWS 0 https://doi.org/10.5256/f1000research.178064.r367036 The revised version addresses ... Continue reading READ ALL The revised version addresses the previous concerns well. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Artificial Intelligence, NLP, Machine Learning, Optimization, AI Chatbots I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mohammed Alyasiri O. Reviewer Report For: A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.5256/f1000research.178064.r367036 ) The direct URL for this report is: https://f1000research.com/articles/13-1040/v2#referee-response-367036 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 19 Feb 2025 Ahmed Alduais , Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Italy 19 Feb 2025 Author Response Thank you so much for your approval and we are glad that our revisions matched your expectations. Competing Interests: No competing interests were disclosed. Thank you so much for your approval and we are glad that our revisions matched your expectations. Thank you so much for your approval and we are glad that our revisions matched your expectations. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 19 Feb 2025 Ahmed Alduais , Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Italy 19 Feb 2025 Author Response Thank you so much for your approval and we are glad that our revisions matched your expectations. Competing Interests: No competing interests were disclosed. Thank you so much for your approval and we are glad that our revisions matched your expectations. Thank you so much for your approval and we are glad that our revisions matched your expectations. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 10 Sep 2024 Views 0 Cite How to cite this report: Mohammed Alyasiri O. Reviewer Report For: A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.5256/f1000research.170550.r357207 ) The direct URL for this report is: https://f1000research.com/articles/13-1040/v1#referee-response-357207 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Jan 2025 Osamah Mohammed Alyasiri , Al-Furat Al-Awsat Technical University, Karbala, Iraq Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.170550.r357207 1. The article addresses the integration of Generative AI (GenAI) tools in applied linguistics, employing a SWOT analysis framework. This approach aligns with existing literature and addresses a timely topic. However, the execution lacks depth and critical engagement, which limits ... Continue reading READ ALL 1. The article addresses the integration of Generative AI (GenAI) tools in applied linguistics, employing a SWOT analysis framework. This approach aligns with existing literature and addresses a timely topic. However, the execution lacks depth and critical engagement, which limits its contribution to the field. 2. The review of existing literature is incomplete. Seminal works and foundational studies on GenAI's implications in applied linguistics are either overlooked or insufficiently integrated. Expanding the literature review to include these studies will strengthen the theoretical grounding. 3. While the qualitative design and SWOT framework are appropriate, the methodology section lacks sufficient details to ensure replicability. The inclusion of the PRISMA flowchart and data extraction templates is helpful, but more specifics about how themes were identified and synthesized are necessary. 4. The SWOT analysis, while structured, reads superficially. The identified strengths, weaknesses, opportunities, and threats are presented without adequately linking them to the context of applied linguistics. In particular: a. The discussion section lacks critical examination of the identified factors. b.The strategic recommendations are generic and need concrete, actionable steps tailored to the applied linguistics domain. 5. The manuscript includes descriptions of GenAI applications, but some sections read as though they were generated by AI tools. This raises concerns about originality and depth. A more critical and reflective narrative is necessary, particularly in evaluating the role of these tools in fostering innovation versus their potential drawbacks. 6. The writing quality is uneven, with some sections lacking coherence and depth. Enhanced editorial refinement and restructuring are needed to improve readability and ensure alignment with academic standards. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Artificial Intelligence, NLP, Machine Learning, Optimization, AI Chatbots I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mohammed Alyasiri O. Reviewer Report For: A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.5256/f1000research.170550.r357207 ) The direct URL for this report is: https://f1000research.com/articles/13-1040/v1#referee-response-357207 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 17 Feb 2025 Ahmed Alduais , Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Italy 17 Feb 2025 Author Response Dear Colleague, Thank you for your thoughtful and constructive feedback on our manuscript. We appreciate your insights and have made revisions to address your concerns. Below, we outline the changes ... Continue reading Dear Colleague, Thank you for your thoughtful and constructive feedback on our manuscript. We appreciate your insights and have made revisions to address your concerns. Below, we outline the changes made in response to your comments. Authors 1. The article addresses the integration of Generative AI (GenAI) tools in applied linguistics, employing a SWOT analysis framework. This approach aligns with existing literature and addresses a timely topic. However, the execution lacks depth and critical engagement, which limits its contribution to the field. We have expanded the Discussion section to include a more critical examination of the SWOT factors, linking them to the specific context of applied linguistics. We’ve also added a reflective analysis of the limitations and implications of GenAI tools in fostering innovation versus their potential drawbacks. Our paper is a systematic review of previous studies, not an empirical study measuring the impact of a specific tool. This approach allows us to synthesize findings from diverse research and provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. 2. The review of existing literature is incomplete. Seminal works and foundational studies on GenAI's implications in applied linguistics are either overlooked or insufficiently integrated. Expanding the literature review to include these studies will strengthen the theoretical grounding. We have expanded the Introduction to include seminal works and foundational studies on GenAI’s implications in applied linguistics, strengthening the theoretical grounding of the paper. 3. While the qualitative design and SWOT framework are appropriate, the methodology section lacks sufficient details to ensure replicability. The inclusion of the PRISMA flowchart and data extraction templates is helpful, but more specifics about how themes were identified and synthesized are necessary. We have added more specifics to the Methods section, including details on how themes were identified and synthesized, ensuring replicability. 4. The SWOT analysis, while structured, reads superficially. The identified strengths, weaknesses, opportunities, and threats are presented without adequately linking them to the context of applied linguistics. In particular: a. The discussion section lacks critical examination of the identified factors. b.The strategic recommendations are generic and need concrete, actionable steps tailored to the applied linguistics domain. We have deepened the SWOT analysis by linking identified factors more explicitly to the context of applied linguistics. We have also refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. The SWOT analysis is structured to align with the scope of our study, which is a systematic review of existing research on generative AI in applied linguistics. This approach ensures a comprehensive examination of the field while acknowledging the limitations inherent in synthesizing diverse studies. 5. The manuscript includes descriptions of GenAI applications, but some sections read as though they were generated by AI tools. This raises concerns about originality and depth. A more critical and reflective narrative is necessary, particularly in evaluating the role of these tools in fostering innovation versus their potential drawbacks. We have revised the manuscript to ensure a more critical and reflective narrative, particularly in evaluating the role of GenAI tools in fostering innovation versus their potential drawbacks. 6. The writing quality is uneven, with some sections lacking coherence and depth. Enhanced editorial refinement and restructuring are needed to improve readability and ensure alignment with academic standards. The manuscript is structured logically, following the standard academic paper format: Introduction, Methods, Results, Discussion, and Conclusion. At approximately 9000 words, the paper provides a comprehensive review of existing studies on the use of generative AI in applied linguistics, ensuring depth and breadth in its analysis. The Results are presented systematically within the SWOT framework, and the Discussion critically examines these findings in the context of applied linguistics. We acknowledge the reviewer’s concern about coherence and depth in some sections. However, it is important to note that our paper is a systematic review of previous studies, not an empirical study measuring the impact or influence of a specific tool. This distinction is crucial: while empirical studies focus on evaluating a particular tool’s effectiveness, our study synthesizes findings from diverse research to provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. That said, we remain committed to improving the manuscript’s readability and alignment with academic standards. If the reviewer has specific suggestions for restructuring or enhancing certain sections, we would be more than happy to implement them. For example, we can further refine transitions between sections or provide additional clarifications where needed. We appreciate the reviewer’s feedback and are dedicated to ensuring the manuscript meets the highest academic standards. Dear Colleague, Thank you for your thoughtful and constructive feedback on our manuscript. We appreciate your insights and have made revisions to address your concerns. Below, we outline the changes made in response to your comments. Authors 1. The article addresses the integration of Generative AI (GenAI) tools in applied linguistics, employing a SWOT analysis framework. This approach aligns with existing literature and addresses a timely topic. However, the execution lacks depth and critical engagement, which limits its contribution to the field. We have expanded the Discussion section to include a more critical examination of the SWOT factors, linking them to the specific context of applied linguistics. We’ve also added a reflective analysis of the limitations and implications of GenAI tools in fostering innovation versus their potential drawbacks. Our paper is a systematic review of previous studies, not an empirical study measuring the impact of a specific tool. This approach allows us to synthesize findings from diverse research and provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. 2. The review of existing literature is incomplete. Seminal works and foundational studies on GenAI's implications in applied linguistics are either overlooked or insufficiently integrated. Expanding the literature review to include these studies will strengthen the theoretical grounding. We have expanded the Introduction to include seminal works and foundational studies on GenAI’s implications in applied linguistics, strengthening the theoretical grounding of the paper. 3. While the qualitative design and SWOT framework are appropriate, the methodology section lacks sufficient details to ensure replicability. The inclusion of the PRISMA flowchart and data extraction templates is helpful, but more specifics about how themes were identified and synthesized are necessary. We have added more specifics to the Methods section, including details on how themes were identified and synthesized, ensuring replicability. 4. The SWOT analysis, while structured, reads superficially. The identified strengths, weaknesses, opportunities, and threats are presented without adequately linking them to the context of applied linguistics. In particular: a. The discussion section lacks critical examination of the identified factors. b.The strategic recommendations are generic and need concrete, actionable steps tailored to the applied linguistics domain. We have deepened the SWOT analysis by linking identified factors more explicitly to the context of applied linguistics. We have also refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. The SWOT analysis is structured to align with the scope of our study, which is a systematic review of existing research on generative AI in applied linguistics. This approach ensures a comprehensive examination of the field while acknowledging the limitations inherent in synthesizing diverse studies. 5. The manuscript includes descriptions of GenAI applications, but some sections read as though they were generated by AI tools. This raises concerns about originality and depth. A more critical and reflective narrative is necessary, particularly in evaluating the role of these tools in fostering innovation versus their potential drawbacks. We have revised the manuscript to ensure a more critical and reflective narrative, particularly in evaluating the role of GenAI tools in fostering innovation versus their potential drawbacks. 6. The writing quality is uneven, with some sections lacking coherence and depth. Enhanced editorial refinement and restructuring are needed to improve readability and ensure alignment with academic standards. The manuscript is structured logically, following the standard academic paper format: Introduction, Methods, Results, Discussion, and Conclusion. At approximately 9000 words, the paper provides a comprehensive review of existing studies on the use of generative AI in applied linguistics, ensuring depth and breadth in its analysis. The Results are presented systematically within the SWOT framework, and the Discussion critically examines these findings in the context of applied linguistics. We acknowledge the reviewer’s concern about coherence and depth in some sections. However, it is important to note that our paper is a systematic review of previous studies, not an empirical study measuring the impact or influence of a specific tool. This distinction is crucial: while empirical studies focus on evaluating a particular tool’s effectiveness, our study synthesizes findings from diverse research to provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. That said, we remain committed to improving the manuscript’s readability and alignment with academic standards. If the reviewer has specific suggestions for restructuring or enhancing certain sections, we would be more than happy to implement them. For example, we can further refine transitions between sections or provide additional clarifications where needed. We appreciate the reviewer’s feedback and are dedicated to ensuring the manuscript meets the highest academic standards. Competing Interests: We, the of the paper, present our response to the reviewer's comments. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 17 Feb 2025 Ahmed Alduais , Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Italy 17 Feb 2025 Author Response Dear Colleague, Thank you for your thoughtful and constructive feedback on our manuscript. We appreciate your insights and have made revisions to address your concerns. Below, we outline the changes ... Continue reading Dear Colleague, Thank you for your thoughtful and constructive feedback on our manuscript. We appreciate your insights and have made revisions to address your concerns. Below, we outline the changes made in response to your comments. Authors 1. The article addresses the integration of Generative AI (GenAI) tools in applied linguistics, employing a SWOT analysis framework. This approach aligns with existing literature and addresses a timely topic. However, the execution lacks depth and critical engagement, which limits its contribution to the field. We have expanded the Discussion section to include a more critical examination of the SWOT factors, linking them to the specific context of applied linguistics. We’ve also added a reflective analysis of the limitations and implications of GenAI tools in fostering innovation versus their potential drawbacks. Our paper is a systematic review of previous studies, not an empirical study measuring the impact of a specific tool. This approach allows us to synthesize findings from diverse research and provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. 2. The review of existing literature is incomplete. Seminal works and foundational studies on GenAI's implications in applied linguistics are either overlooked or insufficiently integrated. Expanding the literature review to include these studies will strengthen the theoretical grounding. We have expanded the Introduction to include seminal works and foundational studies on GenAI’s implications in applied linguistics, strengthening the theoretical grounding of the paper. 3. While the qualitative design and SWOT framework are appropriate, the methodology section lacks sufficient details to ensure replicability. The inclusion of the PRISMA flowchart and data extraction templates is helpful, but more specifics about how themes were identified and synthesized are necessary. We have added more specifics to the Methods section, including details on how themes were identified and synthesized, ensuring replicability. 4. The SWOT analysis, while structured, reads superficially. The identified strengths, weaknesses, opportunities, and threats are presented without adequately linking them to the context of applied linguistics. In particular: a. The discussion section lacks critical examination of the identified factors. b.The strategic recommendations are generic and need concrete, actionable steps tailored to the applied linguistics domain. We have deepened the SWOT analysis by linking identified factors more explicitly to the context of applied linguistics. We have also refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. The SWOT analysis is structured to align with the scope of our study, which is a systematic review of existing research on generative AI in applied linguistics. This approach ensures a comprehensive examination of the field while acknowledging the limitations inherent in synthesizing diverse studies. 5. The manuscript includes descriptions of GenAI applications, but some sections read as though they were generated by AI tools. This raises concerns about originality and depth. A more critical and reflective narrative is necessary, particularly in evaluating the role of these tools in fostering innovation versus their potential drawbacks. We have revised the manuscript to ensure a more critical and reflective narrative, particularly in evaluating the role of GenAI tools in fostering innovation versus their potential drawbacks. 6. The writing quality is uneven, with some sections lacking coherence and depth. Enhanced editorial refinement and restructuring are needed to improve readability and ensure alignment with academic standards. The manuscript is structured logically, following the standard academic paper format: Introduction, Methods, Results, Discussion, and Conclusion. At approximately 9000 words, the paper provides a comprehensive review of existing studies on the use of generative AI in applied linguistics, ensuring depth and breadth in its analysis. The Results are presented systematically within the SWOT framework, and the Discussion critically examines these findings in the context of applied linguistics. We acknowledge the reviewer’s concern about coherence and depth in some sections. However, it is important to note that our paper is a systematic review of previous studies, not an empirical study measuring the impact or influence of a specific tool. This distinction is crucial: while empirical studies focus on evaluating a particular tool’s effectiveness, our study synthesizes findings from diverse research to provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. That said, we remain committed to improving the manuscript’s readability and alignment with academic standards. If the reviewer has specific suggestions for restructuring or enhancing certain sections, we would be more than happy to implement them. For example, we can further refine transitions between sections or provide additional clarifications where needed. We appreciate the reviewer’s feedback and are dedicated to ensuring the manuscript meets the highest academic standards. Dear Colleague, Thank you for your thoughtful and constructive feedback on our manuscript. We appreciate your insights and have made revisions to address your concerns. Below, we outline the changes made in response to your comments. Authors 1. The article addresses the integration of Generative AI (GenAI) tools in applied linguistics, employing a SWOT analysis framework. This approach aligns with existing literature and addresses a timely topic. However, the execution lacks depth and critical engagement, which limits its contribution to the field. We have expanded the Discussion section to include a more critical examination of the SWOT factors, linking them to the specific context of applied linguistics. We’ve also added a reflective analysis of the limitations and implications of GenAI tools in fostering innovation versus their potential drawbacks. Our paper is a systematic review of previous studies, not an empirical study measuring the impact of a specific tool. This approach allows us to synthesize findings from diverse research and provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. 2. The review of existing literature is incomplete. Seminal works and foundational studies on GenAI's implications in applied linguistics are either overlooked or insufficiently integrated. Expanding the literature review to include these studies will strengthen the theoretical grounding. We have expanded the Introduction to include seminal works and foundational studies on GenAI’s implications in applied linguistics, strengthening the theoretical grounding of the paper. 3. While the qualitative design and SWOT framework are appropriate, the methodology section lacks sufficient details to ensure replicability. The inclusion of the PRISMA flowchart and data extraction templates is helpful, but more specifics about how themes were identified and synthesized are necessary. We have added more specifics to the Methods section, including details on how themes were identified and synthesized, ensuring replicability. 4. The SWOT analysis, while structured, reads superficially. The identified strengths, weaknesses, opportunities, and threats are presented without adequately linking them to the context of applied linguistics. In particular: a. The discussion section lacks critical examination of the identified factors. b.The strategic recommendations are generic and need concrete, actionable steps tailored to the applied linguistics domain. We have deepened the SWOT analysis by linking identified factors more explicitly to the context of applied linguistics. We have also refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. The SWOT analysis is structured to align with the scope of our study, which is a systematic review of existing research on generative AI in applied linguistics. This approach ensures a comprehensive examination of the field while acknowledging the limitations inherent in synthesizing diverse studies. 5. The manuscript includes descriptions of GenAI applications, but some sections read as though they were generated by AI tools. This raises concerns about originality and depth. A more critical and reflective narrative is necessary, particularly in evaluating the role of these tools in fostering innovation versus their potential drawbacks. We have revised the manuscript to ensure a more critical and reflective narrative, particularly in evaluating the role of GenAI tools in fostering innovation versus their potential drawbacks. 6. The writing quality is uneven, with some sections lacking coherence and depth. Enhanced editorial refinement and restructuring are needed to improve readability and ensure alignment with academic standards. The manuscript is structured logically, following the standard academic paper format: Introduction, Methods, Results, Discussion, and Conclusion. At approximately 9000 words, the paper provides a comprehensive review of existing studies on the use of generative AI in applied linguistics, ensuring depth and breadth in its analysis. The Results are presented systematically within the SWOT framework, and the Discussion critically examines these findings in the context of applied linguistics. We acknowledge the reviewer’s concern about coherence and depth in some sections. However, it is important to note that our paper is a systematic review of previous studies, not an empirical study measuring the impact or influence of a specific tool. This distinction is crucial: while empirical studies focus on evaluating a particular tool’s effectiveness, our study synthesizes findings from diverse research to provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. That said, we remain committed to improving the manuscript’s readability and alignment with academic standards. If the reviewer has specific suggestions for restructuring or enhancing certain sections, we would be more than happy to implement them. For example, we can further refine transitions between sections or provide additional clarifications where needed. We appreciate the reviewer’s feedback and are dedicated to ensuring the manuscript meets the highest academic standards. Competing Interests: We, the of the paper, present our response to the reviewer's comments. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Bannister P. Reviewer Report For: A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.5256/f1000research.170550.r325264 ) The direct URL for this report is: https://f1000research.com/articles/13-1040/v1#referee-response-325264 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 01 Oct 2024 Peter Bannister , International University of La Rioja, Logroño, Spain Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.170550.r325264 The idea of the article itself is highly relevant to the state of play in the current literature by addressing the bespoke impact of GenAI tools to the field of Applied Linguistics. The SWOT analysis approach taken links to cognate ... Continue reading READ ALL The idea of the article itself is highly relevant to the state of play in the current literature by addressing the bespoke impact of GenAI tools to the field of Applied Linguistics. The SWOT analysis approach taken links to cognate studies and has the potential to be a valuable contribution to the field, however I'm afraid to say that the execution of this has been somewhat disappointing. There are a multitude of studies which address GenAI opportunities and challenges in a generic way, much of which is covered by what is offered here. It has been further surprising to see that many of the most seminal works produced thus far have been largely ignored. Many sections read as if they were generated by an AI tool and with this I am getting at that ideas hang on the page superficially, resulting in a lack of depth of analysis. This is for instance particularly of note in the discussion section where strategies are put forward without due consideration. I have included in-text comments which I would strongly encourage the authors to address. Furthermore, in order for a study of these characteristics to be truly impactful it is important that popular discourse promoted by tech company marketing departments is handled with greater criticality. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Generative AI; English Medium Instruction; Assessment; Academic Integrity; HEI Policy Development: Social Justice I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bannister P. Reviewer Report For: A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.5256/f1000research.170550.r325264 ) The direct URL for this report is: https://f1000research.com/articles/13-1040/v1#referee-response-325264 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 17 Feb 2025 Ahmed Alduais , Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Italy 17 Feb 2025 Author Response Dear Reviewer, Thank you for your feedback on our manuscript. We appreciate your time and effort in reviewing our work and acknowledge your concerns regarding the depth and execution of ... Continue reading Dear Reviewer, Thank you for your feedback on our manuscript. We appreciate your time and effort in reviewing our work and acknowledge your concerns regarding the depth and execution of the SWOT analysis. Below, we address your comments and outline the revisions we have made to improve the manuscript. Authors The idea of the article itself is highly relevant to the state of play in the current literature by addressing the bespoke impact of GenAI tools to the field of Applied Linguistics. The SWOT analysis approach taken links to cognate studies and has the potential to be a valuable contribution to the field, however I'm afraid to say that the execution of this has been somewhat disappointing. There are a multitude of studies which address GenAI opportunities and challenges in a generic way, much of which is covered by what is offered here. Author response: We agree that the integration of Generative AI (GenAI) tools in applied linguistics is a highly relevant topic, and our SWOT analysis aims to provide a structured and actionable approach to understanding its implications. While many studies address GenAI’s opportunities and challenges in a generic way, our study specifically focuses on the field of applied linguistics, offering a nuanced analysis tailored to this context. It has been further surprising to see that many of the most seminal works produced thus far have been largely ignored. Many sections read as if they were generated by an AI tool and with this I am getting at that ideas hang on the page superficially, resulting in a lack of depth of analysis. This is for instance particularly of note in the discussion section where strategies are put forward without due consideration. Author response: We have expanded the Introduction and Literature Review sections to include seminal works and foundational studies on GenAI’s implications in applied linguistics. This includes references to Kaplan (2016), Birhane et al. (2023), and Farrokhnia et al. (2024), which provide a robust theoretical grounding for our analysis. We have deepened the Discussion section to critically examine the identified strengths, weaknesses, opportunities, and threats. For example, we’ve added case studies and examples from the literature to illustrate how specific factors manifest in applied linguistics, ensuring a more reflective and analytical narrative. We have refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. For instance, we propose the development of ethical guidelines, enhancement of AI literacy, investment in infrastructure, and promotion of global collaboration as specific strategies to maximize the benefits of GenAI while mitigating its risks. We have critically examined the popular discourse promoted by tech company marketing departments, particularly in the Discussion and Limitations sections. For example, we highlight the ethical challenges and technical limitations of AI tools like ChatGPT and Gemini, ensuring a balanced and critical perspective. I have included in-text comments which I would strongly encourage the authors to address. Furthermore, in order for a study of these characteristics to be truly impactful it is important that popular discourse promoted by tech company marketing departments is handled with greater criticality. Author response: We did not receive the in-text comments mentioned in your review. However, we are committed to addressing any specific concerns you may have. If you could kindly share these comments, we would be more than happy to incorporate your feedback. We hope these revisions address your concerns and enhance the quality and impact of our manuscript. Thank you again for your valuable feedback and support in refining our work. Dear Reviewer, Thank you for your feedback on our manuscript. We appreciate your time and effort in reviewing our work and acknowledge your concerns regarding the depth and execution of the SWOT analysis. Below, we address your comments and outline the revisions we have made to improve the manuscript. Authors The idea of the article itself is highly relevant to the state of play in the current literature by addressing the bespoke impact of GenAI tools to the field of Applied Linguistics. The SWOT analysis approach taken links to cognate studies and has the potential to be a valuable contribution to the field, however I'm afraid to say that the execution of this has been somewhat disappointing. There are a multitude of studies which address GenAI opportunities and challenges in a generic way, much of which is covered by what is offered here. Author response: We agree that the integration of Generative AI (GenAI) tools in applied linguistics is a highly relevant topic, and our SWOT analysis aims to provide a structured and actionable approach to understanding its implications. While many studies address GenAI’s opportunities and challenges in a generic way, our study specifically focuses on the field of applied linguistics, offering a nuanced analysis tailored to this context. It has been further surprising to see that many of the most seminal works produced thus far have been largely ignored. Many sections read as if they were generated by an AI tool and with this I am getting at that ideas hang on the page superficially, resulting in a lack of depth of analysis. This is for instance particularly of note in the discussion section where strategies are put forward without due consideration. Author response: We have expanded the Introduction and Literature Review sections to include seminal works and foundational studies on GenAI’s implications in applied linguistics. This includes references to Kaplan (2016), Birhane et al. (2023), and Farrokhnia et al. (2024), which provide a robust theoretical grounding for our analysis. We have deepened the Discussion section to critically examine the identified strengths, weaknesses, opportunities, and threats. For example, we’ve added case studies and examples from the literature to illustrate how specific factors manifest in applied linguistics, ensuring a more reflective and analytical narrative. We have refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. For instance, we propose the development of ethical guidelines, enhancement of AI literacy, investment in infrastructure, and promotion of global collaboration as specific strategies to maximize the benefits of GenAI while mitigating its risks. We have critically examined the popular discourse promoted by tech company marketing departments, particularly in the Discussion and Limitations sections. For example, we highlight the ethical challenges and technical limitations of AI tools like ChatGPT and Gemini, ensuring a balanced and critical perspective. I have included in-text comments which I would strongly encourage the authors to address. Furthermore, in order for a study of these characteristics to be truly impactful it is important that popular discourse promoted by tech company marketing departments is handled with greater criticality. Author response: We did not receive the in-text comments mentioned in your review. However, we are committed to addressing any specific concerns you may have. If you could kindly share these comments, we would be more than happy to incorporate your feedback. We hope these revisions address your concerns and enhance the quality and impact of our manuscript. Thank you again for your valuable feedback and support in refining our work. Competing Interests: We, the authors of this paper, are submitting a response to Reviewer 1. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 17 Feb 2025 Ahmed Alduais , Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Italy 17 Feb 2025 Author Response Dear Reviewer, Thank you for your feedback on our manuscript. We appreciate your time and effort in reviewing our work and acknowledge your concerns regarding the depth and execution of ... Continue reading Dear Reviewer, Thank you for your feedback on our manuscript. We appreciate your time and effort in reviewing our work and acknowledge your concerns regarding the depth and execution of the SWOT analysis. Below, we address your comments and outline the revisions we have made to improve the manuscript. Authors The idea of the article itself is highly relevant to the state of play in the current literature by addressing the bespoke impact of GenAI tools to the field of Applied Linguistics. The SWOT analysis approach taken links to cognate studies and has the potential to be a valuable contribution to the field, however I'm afraid to say that the execution of this has been somewhat disappointing. There are a multitude of studies which address GenAI opportunities and challenges in a generic way, much of which is covered by what is offered here. Author response: We agree that the integration of Generative AI (GenAI) tools in applied linguistics is a highly relevant topic, and our SWOT analysis aims to provide a structured and actionable approach to understanding its implications. While many studies address GenAI’s opportunities and challenges in a generic way, our study specifically focuses on the field of applied linguistics, offering a nuanced analysis tailored to this context. It has been further surprising to see that many of the most seminal works produced thus far have been largely ignored. Many sections read as if they were generated by an AI tool and with this I am getting at that ideas hang on the page superficially, resulting in a lack of depth of analysis. This is for instance particularly of note in the discussion section where strategies are put forward without due consideration. Author response: We have expanded the Introduction and Literature Review sections to include seminal works and foundational studies on GenAI’s implications in applied linguistics. This includes references to Kaplan (2016), Birhane et al. (2023), and Farrokhnia et al. (2024), which provide a robust theoretical grounding for our analysis. We have deepened the Discussion section to critically examine the identified strengths, weaknesses, opportunities, and threats. For example, we’ve added case studies and examples from the literature to illustrate how specific factors manifest in applied linguistics, ensuring a more reflective and analytical narrative. We have refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. For instance, we propose the development of ethical guidelines, enhancement of AI literacy, investment in infrastructure, and promotion of global collaboration as specific strategies to maximize the benefits of GenAI while mitigating its risks. We have critically examined the popular discourse promoted by tech company marketing departments, particularly in the Discussion and Limitations sections. For example, we highlight the ethical challenges and technical limitations of AI tools like ChatGPT and Gemini, ensuring a balanced and critical perspective. I have included in-text comments which I would strongly encourage the authors to address. Furthermore, in order for a study of these characteristics to be truly impactful it is important that popular discourse promoted by tech company marketing departments is handled with greater criticality. Author response: We did not receive the in-text comments mentioned in your review. However, we are committed to addressing any specific concerns you may have. If you could kindly share these comments, we would be more than happy to incorporate your feedback. We hope these revisions address your concerns and enhance the quality and impact of our manuscript. Thank you again for your valuable feedback and support in refining our work. Dear Reviewer, Thank you for your feedback on our manuscript. We appreciate your time and effort in reviewing our work and acknowledge your concerns regarding the depth and execution of the SWOT analysis. Below, we address your comments and outline the revisions we have made to improve the manuscript. Authors The idea of the article itself is highly relevant to the state of play in the current literature by addressing the bespoke impact of GenAI tools to the field of Applied Linguistics. The SWOT analysis approach taken links to cognate studies and has the potential to be a valuable contribution to the field, however I'm afraid to say that the execution of this has been somewhat disappointing. There are a multitude of studies which address GenAI opportunities and challenges in a generic way, much of which is covered by what is offered here. Author response: We agree that the integration of Generative AI (GenAI) tools in applied linguistics is a highly relevant topic, and our SWOT analysis aims to provide a structured and actionable approach to understanding its implications. While many studies address GenAI’s opportunities and challenges in a generic way, our study specifically focuses on the field of applied linguistics, offering a nuanced analysis tailored to this context. It has been further surprising to see that many of the most seminal works produced thus far have been largely ignored. Many sections read as if they were generated by an AI tool and with this I am getting at that ideas hang on the page superficially, resulting in a lack of depth of analysis. This is for instance particularly of note in the discussion section where strategies are put forward without due consideration. Author response: We have expanded the Introduction and Literature Review sections to include seminal works and foundational studies on GenAI’s implications in applied linguistics. This includes references to Kaplan (2016), Birhane et al. (2023), and Farrokhnia et al. (2024), which provide a robust theoretical grounding for our analysis. We have deepened the Discussion section to critically examine the identified strengths, weaknesses, opportunities, and threats. For example, we’ve added case studies and examples from the literature to illustrate how specific factors manifest in applied linguistics, ensuring a more reflective and analytical narrative. We have refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. For instance, we propose the development of ethical guidelines, enhancement of AI literacy, investment in infrastructure, and promotion of global collaboration as specific strategies to maximize the benefits of GenAI while mitigating its risks. We have critically examined the popular discourse promoted by tech company marketing departments, particularly in the Discussion and Limitations sections. For example, we highlight the ethical challenges and technical limitations of AI tools like ChatGPT and Gemini, ensuring a balanced and critical perspective. I have included in-text comments which I would strongly encourage the authors to address. Furthermore, in order for a study of these characteristics to be truly impactful it is important that popular discourse promoted by tech company marketing departments is handled with greater criticality. Author response: We did not receive the in-text comments mentioned in your review. However, we are committed to addressing any specific concerns you may have. If you could kindly share these comments, we would be more than happy to incorporate your feedback. We hope these revisions address your concerns and enhance the quality and impact of our manuscript. Thank you again for your valuable feedback and support in refining our work. Competing Interests: We, the authors of this paper, are submitting a response to Reviewer 1. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 10 Sep 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 17 Feb 25 read Version 1 10 Sep 24 read read Peter Bannister , International University of La Rioja, Logroño, Spain Osamah Mohammed Alyasiri , Al-Furat Al-Awsat Technical University, Karbala, Iraq Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Mohammed Alyasiri O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 18 Feb 2025 | for Version 2 Osamah Mohammed Alyasiri , Al-Furat Al-Awsat Technical University, Karbala, Iraq 0 Views copyright © 2025 Mohammed Alyasiri O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The revised version addresses the previous concerns well. Competing Interests No competing interests were disclosed. Reviewer Expertise Artificial Intelligence, NLP, Machine Learning, Optimization, AI Chatbots I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 19 Feb 2025 Ahmed Alduais, Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Italy Thank you so much for your approval and we are glad that our revisions matched your expectations. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Mohammed Alyasiri O. Peer Review Report For: A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.5256/f1000research.178064.r367036) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1040/v2#referee-response-367036 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Mohammed Alyasiri O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Jan 2025 | for Version 1 Osamah Mohammed Alyasiri , Al-Furat Al-Awsat Technical University, Karbala, Iraq 0 Views copyright © 2025 Mohammed Alyasiri O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. The article addresses the integration of Generative AI (GenAI) tools in applied linguistics, employing a SWOT analysis framework. This approach aligns with existing literature and addresses a timely topic. However, the execution lacks depth and critical engagement, which limits its contribution to the field. 2. The review of existing literature is incomplete. Seminal works and foundational studies on GenAI's implications in applied linguistics are either overlooked or insufficiently integrated. Expanding the literature review to include these studies will strengthen the theoretical grounding. 3. While the qualitative design and SWOT framework are appropriate, the methodology section lacks sufficient details to ensure replicability. The inclusion of the PRISMA flowchart and data extraction templates is helpful, but more specifics about how themes were identified and synthesized are necessary. 4. The SWOT analysis, while structured, reads superficially. The identified strengths, weaknesses, opportunities, and threats are presented without adequately linking them to the context of applied linguistics. In particular: a. The discussion section lacks critical examination of the identified factors. b.The strategic recommendations are generic and need concrete, actionable steps tailored to the applied linguistics domain. 5. The manuscript includes descriptions of GenAI applications, but some sections read as though they were generated by AI tools. This raises concerns about originality and depth. A more critical and reflective narrative is necessary, particularly in evaluating the role of these tools in fostering innovation versus their potential drawbacks. 6. The writing quality is uneven, with some sections lacking coherence and depth. Enhanced editorial refinement and restructuring are needed to improve readability and ensure alignment with academic standards. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Artificial Intelligence, NLP, Machine Learning, Optimization, AI Chatbots I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 17 Feb 2025 Ahmed Alduais, Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Italy Dear Colleague, Thank you for your thoughtful and constructive feedback on our manuscript. We appreciate your insights and have made revisions to address your concerns. Below, we outline the changes made in response to your comments. Authors 1. The article addresses the integration of Generative AI (GenAI) tools in applied linguistics, employing a SWOT analysis framework. This approach aligns with existing literature and addresses a timely topic. However, the execution lacks depth and critical engagement, which limits its contribution to the field. We have expanded the Discussion section to include a more critical examination of the SWOT factors, linking them to the specific context of applied linguistics. We’ve also added a reflective analysis of the limitations and implications of GenAI tools in fostering innovation versus their potential drawbacks. Our paper is a systematic review of previous studies, not an empirical study measuring the impact of a specific tool. This approach allows us to synthesize findings from diverse research and provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. 2. The review of existing literature is incomplete. Seminal works and foundational studies on GenAI's implications in applied linguistics are either overlooked or insufficiently integrated. Expanding the literature review to include these studies will strengthen the theoretical grounding. We have expanded the Introduction to include seminal works and foundational studies on GenAI’s implications in applied linguistics, strengthening the theoretical grounding of the paper. 3. While the qualitative design and SWOT framework are appropriate, the methodology section lacks sufficient details to ensure replicability. The inclusion of the PRISMA flowchart and data extraction templates is helpful, but more specifics about how themes were identified and synthesized are necessary. We have added more specifics to the Methods section, including details on how themes were identified and synthesized, ensuring replicability. 4. The SWOT analysis, while structured, reads superficially. The identified strengths, weaknesses, opportunities, and threats are presented without adequately linking them to the context of applied linguistics. In particular: a. The discussion section lacks critical examination of the identified factors. b.The strategic recommendations are generic and need concrete, actionable steps tailored to the applied linguistics domain. We have deepened the SWOT analysis by linking identified factors more explicitly to the context of applied linguistics. We have also refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. The SWOT analysis is structured to align with the scope of our study, which is a systematic review of existing research on generative AI in applied linguistics. This approach ensures a comprehensive examination of the field while acknowledging the limitations inherent in synthesizing diverse studies. 5. The manuscript includes descriptions of GenAI applications, but some sections read as though they were generated by AI tools. This raises concerns about originality and depth. A more critical and reflective narrative is necessary, particularly in evaluating the role of these tools in fostering innovation versus their potential drawbacks. We have revised the manuscript to ensure a more critical and reflective narrative, particularly in evaluating the role of GenAI tools in fostering innovation versus their potential drawbacks. 6. The writing quality is uneven, with some sections lacking coherence and depth. Enhanced editorial refinement and restructuring are needed to improve readability and ensure alignment with academic standards. The manuscript is structured logically, following the standard academic paper format: Introduction, Methods, Results, Discussion, and Conclusion. At approximately 9000 words, the paper provides a comprehensive review of existing studies on the use of generative AI in applied linguistics, ensuring depth and breadth in its analysis. The Results are presented systematically within the SWOT framework, and the Discussion critically examines these findings in the context of applied linguistics. We acknowledge the reviewer’s concern about coherence and depth in some sections. However, it is important to note that our paper is a systematic review of previous studies, not an empirical study measuring the impact or influence of a specific tool. This distinction is crucial: while empirical studies focus on evaluating a particular tool’s effectiveness, our study synthesizes findings from diverse research to provide a holistic overview of the strengths, weaknesses, opportunities, and threats of generative AI in applied linguistics. That said, we remain committed to improving the manuscript’s readability and alignment with academic standards. If the reviewer has specific suggestions for restructuring or enhancing certain sections, we would be more than happy to implement them. For example, we can further refine transitions between sections or provide additional clarifications where needed. We appreciate the reviewer’s feedback and are dedicated to ensuring the manuscript meets the highest academic standards. View more View less Competing Interests We, the of the paper, present our response to the reviewer's comments. reply Respond Report a concern Mohammed Alyasiri O. Peer Review Report For: A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.5256/f1000research.170550.r357207) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1040/v1#referee-response-357207 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Bannister P. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 01 Oct 2024 | for Version 1 Peter Bannister , International University of La Rioja, Logroño, Spain 0 Views copyright © 2024 Bannister P. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The idea of the article itself is highly relevant to the state of play in the current literature by addressing the bespoke impact of GenAI tools to the field of Applied Linguistics. The SWOT analysis approach taken links to cognate studies and has the potential to be a valuable contribution to the field, however I'm afraid to say that the execution of this has been somewhat disappointing. There are a multitude of studies which address GenAI opportunities and challenges in a generic way, much of which is covered by what is offered here. It has been further surprising to see that many of the most seminal works produced thus far have been largely ignored. Many sections read as if they were generated by an AI tool and with this I am getting at that ideas hang on the page superficially, resulting in a lack of depth of analysis. This is for instance particularly of note in the discussion section where strategies are put forward without due consideration. I have included in-text comments which I would strongly encourage the authors to address. Furthermore, in order for a study of these characteristics to be truly impactful it is important that popular discourse promoted by tech company marketing departments is handled with greater criticality. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Generative AI; English Medium Instruction; Assessment; Academic Integrity; HEI Policy Development: Social Justice I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 17 Feb 2025 Ahmed Alduais, Human Sciences (Psychology), Universita degli Studi di Verona, Verona, Italy Dear Reviewer, Thank you for your feedback on our manuscript. We appreciate your time and effort in reviewing our work and acknowledge your concerns regarding the depth and execution of the SWOT analysis. Below, we address your comments and outline the revisions we have made to improve the manuscript. Authors The idea of the article itself is highly relevant to the state of play in the current literature by addressing the bespoke impact of GenAI tools to the field of Applied Linguistics. The SWOT analysis approach taken links to cognate studies and has the potential to be a valuable contribution to the field, however I'm afraid to say that the execution of this has been somewhat disappointing. There are a multitude of studies which address GenAI opportunities and challenges in a generic way, much of which is covered by what is offered here. Author response: We agree that the integration of Generative AI (GenAI) tools in applied linguistics is a highly relevant topic, and our SWOT analysis aims to provide a structured and actionable approach to understanding its implications. While many studies address GenAI’s opportunities and challenges in a generic way, our study specifically focuses on the field of applied linguistics, offering a nuanced analysis tailored to this context. It has been further surprising to see that many of the most seminal works produced thus far have been largely ignored. Many sections read as if they were generated by an AI tool and with this I am getting at that ideas hang on the page superficially, resulting in a lack of depth of analysis. This is for instance particularly of note in the discussion section where strategies are put forward without due consideration. Author response: We have expanded the Introduction and Literature Review sections to include seminal works and foundational studies on GenAI’s implications in applied linguistics. This includes references to Kaplan (2016), Birhane et al. (2023), and Farrokhnia et al. (2024), which provide a robust theoretical grounding for our analysis. We have deepened the Discussion section to critically examine the identified strengths, weaknesses, opportunities, and threats. For example, we’ve added case studies and examples from the literature to illustrate how specific factors manifest in applied linguistics, ensuring a more reflective and analytical narrative. We have refined the strategic recommendations to include concrete, actionable steps tailored to the applied linguistics domain. For instance, we propose the development of ethical guidelines, enhancement of AI literacy, investment in infrastructure, and promotion of global collaboration as specific strategies to maximize the benefits of GenAI while mitigating its risks. We have critically examined the popular discourse promoted by tech company marketing departments, particularly in the Discussion and Limitations sections. For example, we highlight the ethical challenges and technical limitations of AI tools like ChatGPT and Gemini, ensuring a balanced and critical perspective. I have included in-text comments which I would strongly encourage the authors to address. Furthermore, in order for a study of these characteristics to be truly impactful it is important that popular discourse promoted by tech company marketing departments is handled with greater criticality. Author response: We did not receive the in-text comments mentioned in your review. However, we are committed to addressing any specific concerns you may have. If you could kindly share these comments, we would be more than happy to incorporate your feedback. We hope these revisions address your concerns and enhance the quality and impact of our manuscript. Thank you again for your valuable feedback and support in refining our work. View more View less Competing Interests We, the authors of this paper, are submitting a response to Reviewer 1. reply Respond Report a concern Bannister P. Peer Review Report For: A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats [version 2; peer review: 1 approved, 1 not approved] . F1000Research 2025, 13 :1040 ( https://doi.org/10.5256/f1000research.170550.r325264) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1040/v1#referee-response-325264 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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