Key dimensions in which AI ChatGPT supports second language teaching and learning

Key dimensions in which AI ChatGPT supports second language teaching and learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Key dimensions in which AI ChatGPT supports second language teaching and learning Guowei Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7063248/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, artificial intelligence (AI) has developed at a remarkable pace, bringing unprecedented opportunities and challenges to a wide range of fields, including healthcare, finance, and particularly education. Its application in foreign language teaching and learning has shown immense potential. However, as an emerging technology, the integration of AI into education remains in an exploratory stage. Existing studies often focus on isolated functionalities or preliminary outcomes, lacking a systematic and comprehensive theoretical and practical framework. Consequently, there are still significant gaps and controversies regarding the mechanisms, impact pathways, and sustainability of AI in second language acquisition. To address these research gaps, this study takes ChatGPT a representative generative AI tool as its central focus. It adopts the Systematic Literature Review (SLR) methodology, adhering to rigorous procedures of planning, execution, and reporting of the review. The goal is to systematically examine and synthesize the current body of domestic and international research on the application of ChatGPT in second language teaching and learning. Specifically, the study aims to identify the key dimensions in which AI can significantly support language instruction such as personalized learning, real-time feedback, motivation enhancement, and productive language output and to explore the mechanisms through which it facilitates language development. Additionally, the review seeks to pinpoint contradictions, limitations, and underexplored areas in the existing literature. Based on these findings, the paper offers targeted recommendations for future research and instructional practice to advance both theoretical understanding and educational effectiveness in the field of second language education. Artificial Intelligence (AI) ChatGPT Systematic Literature Review (SLR) Second language Figures Figure 1 1 Introduction The term AI was coined by John McCarthy in 1956. Over the following decades, AI technology reached several important milestones. In the 1970s and 1980s, the emergence of expert systems and probabilistic expert systems marked the first major advances in rule-based reasoning and the handling of uncertainty. Later, the introduction of the backpropagation algorithm spurred the resurgence of artificial neural networks (Wexell Machado & Canese, 2024 ). With the launch of ChatGPT by OpenAI in November 2022, the potential of AI-powered learning tools in language education attracted significant attention from researchers and education professionals. ChatGPT, a chatbot developed by OpenAI, leverages a large language model to generate human-like text (Xiao & Zhi, 2023 ; Karataş et al., 2024 ). Its applications range from speech recognition and conversational assistants to search engines. ChatGPT can answer questions, drafting texts, reasoning logically, debugging code, and translating languages, and has demonstrated undeniable value in educational contexts (Sanz Manzanedo, 2025 ). The integration of AI in language teaching not only enhances linguistic competence but also increases students’ motivation and interest, becoming an innovative pedagogical strategy to address the educational challenges of the 21st century (Changoluisa Santacruz et al., 2024 ). Introducing AI into the language classroom is not only a key step toward optimizing teaching effectiveness but also an essential way to renew teaching practices (Wexell Machado & Canese, 2024 ). Today, in the era of classroom digitalization, AI stands as a transformative tool that is redefining the methods of language teaching and learning (Gibert et al., 2024 ). Although its adoption is expanding across all sectors, including education, there are still existing gaps. While the value of AI in language learning has been documented (Chicaiza et al., 2023 ), Mah et al. ( 2025 ) points out that there is a lack of prospective studies analyzing its future impact on teaching practices. The literature review shows that ChatGPT can support foreign language learning; however, each study tends to focus on partial contributions. This raises the question: In which specific dimensions can AI most significantly support the teaching and learning of a second language? The objective of this paper is to employ a SLR to identify and analyze the most recent and cutting‑edge studies published between 2023 and 2025, with the aim of addressing the central research question. By doing so, it seeks to uncover existing gaps and contradictions in the current body of work and to offer concrete recommendations and an outlook for future investigations in this field. 2 Methods The SLR approach adopted in this study follows the methodology proposed by Barbara Kitchenham ( 2004 ), which was designed to guide rigorous reviews in Software Engineering and the Humanities. This model is structured into three stages: planning, execution, and reporting of the review. 2.1 Planning Stage In this stage, the review protocol is developed with the goal of minimizing bias and ensuring transparency throughout the process (Changoluisa Santacruz, 2024). The protocol includes the following components: 2.2 Research Questions Formulating precise questions is essential to guide the search and selection of sources. The central question of this study is: In which specific dimensions can AI most significantly support the teaching and learning of a second language? 2.3 Search Strategy To retrieve relevant literature, three academic repositories were consulted: Google Scholar, CNKI, and Dialnet. Search queries were conducted using combinations of key terms in both English and Spanish, as well as their synonyms. The main search strings used were: C1: “Artificial Intelligence ChatGPT as a Second Foreign Language” C2: Spanish equivalents Table 1 provides a detailed account of the number of records retrieved from each database using these search queries. Table 1 The articles obtained from scientific databases Database C1 C2 Total Google Scholar 200 100 300 CNKI 50 20 70 Dialnet 100 200 300 Total 350 320 670 2.4 Primary Study Selection Procedure In the initial phase, all documents whose titles and abstracts explicitly align with the study's scope were compiled in a spreadsheet. Duplicate records, resulting from their appearance across multiple databases, were immediately removed. The refined list then underwent a second round of evaluation based on the strict application of the inclusion and exclusion criteria described in Table 2 . This screening process considered factors such as publication period, type of media, language, document category, and source of origin ensuring that only those studies meeting the established criteria remained for analysis. Once this processing was completed, the selected articles constituted the final corpus of primary studies that would inform the systematic review, ensuring a coherent approach aligned with the research objectives. Table 2 Inclusion and Exclusion Criteria Category Inclusion Criteria Exclusion Criteria Research type Detailed descriptions of AI-involved teaching processes Articles that are not accessible in full text Research subjects Formal educational settings Informal learning environments (e.g., self-study at home, non-accredited institutions) Language Written in English or Spanish Written in other languages Intervention AI tools like ChatGPT Non-AI technological interventions (e.g., traditional multimedia teaching) Outcome measures Focused on improving personalization learning No specific teaching outcomes or irrelevant to the research objectives Time range Published from January 2023 to June 2025 Published before January 2023 In the third stage of review, these articles were assessed for their relevance to the study topic using the relevance criteria shown in Table 3 . Each article was rated on a Likert scale based on its degree of relevance. Finally, the relevant information from the selected studies was recorded in a summary matrix. Table 3 Relevance Criteria Relevance Criteria Personalization Autonomy Immediate feedback Critical thinking During the first screening, I identified 670 preliminary records pertinent to my research questions. In the second screening, I applied my predefined inclusion and exclusion criteria, yielding 64 articles eligible for further consideration. During the third screening, each of these 64 articles was assessed using a fivepoint Likert scale applied to the relevance dimensions listed. The response options were a) Strongly Disagree; b) Disagree; c) Neutral; d) Agree; e) Strongly Agree. Each relevance criterion received a score from 1 (Strongly Disagree) to 5 (Strongly Agree). I then partitioned these scores into two aggregates: \(\:\varSigma\:\:(a\:+\:b)\:\varSigma\:\:(c\:+\:d\:+\:e)\) An article was retained for indepth analysis if \(\:\varSigma\:\:(c\:+\:d\:+\:e)\:\) exceeded \(\:\varSigma\:\:(a\:+\:b)\) , as illustrated in Table 4 . Articles meeting the basic eligibility requirements but for which \(\:\varSigma\:\:(a\:+\:b)\:\ge\:\:\varSigma\:\:(c\:+\:d\:+\:e)\) were noted as eligible yet not advanced to full analysis (Changoluisa Santacruz, 2024). Table 4 Evaluation of Articles Using the Likert Scale Relevance Criteria a b c d e Personalization 1 Autonomy 1 Immediate feedback 1 Critical thinking 1 Total 1 3 As a result of the evaluation, 28 documents that meet the established criteria and are relevant to the research were selected Fig. 1 . 3 Discussion To address the research question outlined above and to gain a deeper understanding of the topic, it is necessary to first present the most salient findings from the literature. Consequently, Consequently, I will answer the following: In which specific dimensions can AI most significantly support the teaching and learning of a second language? Mouta et al. ( 2024 ), using a SLR method, explored the personalization of the language learning experience. Their study demonstrates that artificial intelligence can design bespoke learning pathways, allowing students to adjust the pace according to their individual needs and thereby improve outcomes, advantages that are particularly pronounced in the personalization of the language teaching experience. According to Son et al. ( 2023 ), a literature analysis reveals that AI tools can provide students with personalization, interactive guidance. In the context of language teaching, AI facilitates individualized learning without requiring direct teacher intervention, thanks to flexible environments that combine immediate feedback with modes of autonomy, thus enhancing learners’ capacity for personalization and enabling them to meet the challenges of foreign language acquisition more successfully. García Fernández ( 2023 ) emphasizes that integrating AI into language instruction, through personalization and immediate feedback, it establishes AI as a highly effective pedagogical resource. This approach drives forward personalization in teaching, promoting students’ assumption of an autonomy, leading role in their own learning process while notably enhancing their proficiency with technological tools. Román Mendoza ( 2024 ) reports that deploying ChatGPT for outofclass learning focused on autonomy facilitates personalization adjustments to the educational process, adapting to the specific personalization needs of diverse student profiles. This dynamic promotes the development of strategies for autonomy learning among secondlanguage learners, strengthening their ability to progress in language mastery without direct teacher oversight. Fuertes Gutiérrez et al. ( 2025 ) highlight ChatGPT’s potential in foreign language teaching, especially for advancing personalization, as it allows the learning experience to be precisely tailored to each student’s individual requirements. Al Mughairi & Bhaskar ( 2024 ), employing Interpretative Phenomenological Analysis (IPA), delve into how ChatGPT meets the demands of personalization instruction, identifying its individualized adaptation capabilities as a key factor for implementing personalization practices in educational settings. Changoluisa Santacruz et al. ( 2024 ), through a SLR, analyzed various AI tools applied to language learning and found that these systems can indeed provide genuinely personalization feedback. Tools like ChatGPT have demonstrated, thanks to their personalization algorithms, the ability to deliver tailored feedback to each learner, thereby optimizing the languageteaching environment. Arvelo ( 2024 ), based on a systematic qualitative analysis of available AI tools, reveals that even though this learning model may seem informal it enables students to assumption of an autonomous leading role outside the classroom, creating a Personal Learning Environment (PLE) that emphasizes personalization. This approach fosters critical thinking and independent learning capacity while extending the reach of language instruction across varied contexts. ChatGPT stands out for its ability to generate comprehension questions, design personalization teaching materials, and provide immediate feedback to both teachers and students, thereby driving the development of critical skills and achieving deep levels of selfdirected learning. Sanz Manzanedo ( 2025 ), using a mixedmethods approach that combined a SLR and a quantitative survey, demonstrated that artificial intelligence can continuously provide personalization conversational practice, where immediate feedback is the key element of adaptive learning. This capability allows instructional activities to be adjusted in real time according to each student’s progress and thereby improving educational outcomes. ChatGPT, as a chatbot capable of delivering immediate feedback, offers students essential opportunities for live conversational practice, facilitating direct interaction and immediate feedback, which translates into notable improvements both in the quality of oral exercises and in the effectiveness of immediate feedback. Xiao & Zhi ( 2023 ), through a smallscale exploratory approach, demonstrated ChatGPT’s potential in language education, especially its capacity to deliver immediate feedback, personalization, promote critical thinking, and foster autonomy. ChatGPT can provide students with personalization guidance and develop their autonomy by adapting to the learner’s language level and engaging them in highly interactive, personalization dialogue. Moreover, it can generate customized content tailored to students’ specific needs, providing immediate feedback and personalization on specific queries, thus reinforcing learner autonomy. In terms of quality, ChatGPT’s outputs exhibit critical thinking by offering reasoned judgments, which helps mitigate potential threats to academic integrity. In this way, through immediate feedback, it promotes the development of critical thinking and autonomy. Iqbal et al. ( 2025 ), employing an exploratory sequential mixedmethods design, identified multiple advantages of generative AI in secondlanguage acquisition: it not only highly delivers personalization learning materials to students but also, provides immediate feedback via interactive exercises and adaptive learning environments, which significantly boosting learners’ motivation and engagement. The study highlights that integrating AI technologies can optimize teaching efficiency and support the creation of personalization learning pathways, thereby advancing more precise and effective secondlanguage instruction. Additionally, AI, through interactive practice and adaptive environments, facilitates immediate feedback that promotes continuous learning. Bond et al. ( 2024 ), in a metareview of reviews, systematically synthesized prior assessments of AI’s application in education. Their findings show that adaptive learning systems exemplified by chatbots significantly enhance autonomy and critical thinking by personalization learning content to meet individual needs. Furthermore, through detailed immediate feedback mechanisms, they effectively improve writing quality and overall academic performance. Rugaiyah ( 2023 ), via a Systematic Review, demonstrated that AIdriven languagelearning tools can substantially expand learners’ vocabulary and grammatical knowledge, improve comprehension, and strengthen speaking skills through personalization and adaptive learning pathways and immediate feedback, which is one of their primary strengths. Whether by flagging grammatical errors, using conversational chatbots, or speechrecognition–based pronunciation correction, these tools help learners quickly detect and correct errors, thereby improving linguistic accuracy and fluency. Additionally, they track learning progress in real time and dynamically adjust difficulty and content, allowing learners to advance at their own pace of autonomy, face continuous challenges without cognitive overload, and maximize learning effectiveness. Hong ( 2023 ) reports that research has shown students can receive highly personalization tutoring via ChatGPT. This system not only generates discussion topics and creativewriting prompts but also effectively fosters the development of the four language skills listening, speaking, reading, and writing. It improves grammatical accuracy and increases vocabulary while boosting learner motivation and confidence in language use. ChatGPT can automatically correct essays, provide improvement suggestions, design lesson plans, and generate exercises, questions, and situational dialogues tailored to individual needs, thus driving learning experience with personalization in which immediate feedback plays a crucial role. Román Mendoza ( 2023 ) argues that AIgenerated texts can supply language learners with authentic, varied, and highly personalization input. Integrating approaches such as Critical Digital Pedagogy or Critical Technological Pedagogy into language instruction is fundamental for constructing a pedagogical paradigm centered on critical thinking. Almelhes ( 2023 ), via a SLR, explored in depth AI’s immediate feedback mechanisms and offered practical guidance on how to design diverse feedback modalities to enhance autonomy learning experiences on both mobile devices and computers. Chicaiza et al. ( 2023 ), using a quantitative, nonexperimental, crosssectional, descriptive, analytical, and interpretive design, showed that AI can accurately identify learners’ specific areas for improvement and provide precise personalization feedback, dynamically adjusting learning content. AI not only simulates nativespeaker conversations and offers continuous, ondemand practice but also instantly corrects grammatical errors, optimizes vocabulary use, and improves pronunciation through a highly interactive, adaptive model that allows students to progress at their own pace and focus on individual improvement. Cisneros Vásquez et al. ( 2024 ) employed a bibliographic review method, focusing on AI integration in education, particularly in personalization. They concluded that AI’s adaptive personalization and immediate feedback capabilities hold great potential to significantly enhance educational outcomes. However, the effectiveness of these applications will depend greatly on how these systems are designed, implemented, and managed within educational contexts. Ghafar et al. ( 2023 ) through data collection, focused scientific writing, literature review, and problem analysis, explain how AI can support learner autonomy by continuously providing personalization instruction and immediate feedback. AI delivers constant personalization teaching, supplying students with extensive immediate feedback and scaffolded activities. AIbased immediate feedback systems can be used effectively in foreign language learning to obtain realtime correction. Liu ( 2023 ) predicts that AI’s future development in foreign language teaching will be broad and innovative. Leveraging speech recognition and adaptive technologies, AI systems will rapidly and precisely adjust instructional content according to individual learner needs, enabling true personalization. Moreover, AI’s error correction and immediate feedback play an irreplaceable role in promoting learners’ selfrevision and strengthening their Autonomy. Menacho et al. ( 2024 ) employed a quantitative design and empirically verified that systematically introducing AI technologies into the classroom environment can effectively guide students toward autonomy and foster their willingness to collaborate. Through digital terminals and intelligent platforms, this model dynamically tailors’ educational content to individual learner needs, resulting in a personalization learning experience. Simultaneously, AIdriven automated assessment and immediate feedback effectively support collaborative discussion among students and promote their autonomy in higher education contexts. Thus, as a datadriven teaching aid, AI is crucial for enhancing both learning outcomes and learner autonomy in university students. Yunina ( 2023 ) argues that AI provides students with personalization learning methods focused on autonomy and aimed at achieving better outcomes in foreign language acquisition. Currently, its application in language teaching enables the creation of personalization courses and flexible learning pathways. Such personalization learning fosters more effective study by optimally adapting to student needs and promoting greater success in language learning. AIdriven automated scoring applications, using natural language processing and deep learning algorithms, offer students immediate feedback. Zou et al. ( 2023 ), using questionnaires, semistructured interviews, and pre/posttest data, demonstrated the motivational effect of socialnetwork–driven interaction on AIassisted speaking practice: learners not only maintained high engagement with ongoing interaction but also received personalization corrections in pronunciation, fluency, and lexical accuracy through teachers’ immediate feedback, resulting in significant improvements in their speaking performance. Wexell Machado & Canese ( 2024 ), via a surveybased study, concluded that AI can personalize learning content and adjust pedagogical strategies to meet student needs. The immediate feedback and personalization services AI provides allow teachers to devote more time to creative and complex tasks. Because AI can deliver immediate feedback and support autonomy, it significantly enhances both learner efficiency and motivation. Gibert et al. ( 2024 ) an exploratory research design was adopted to demonstrate how artificial intelligence tools can enhance language competencies by offering adaptive, personalization learning experiences. AI has the potential to become increasingly personalization and efficient, and it could radically transform the way contemporary students engage with language and literature. Strategically integrating technology into instructional design enables more accessible, personalization, and effective language teaching. Within the framework of personalized instruction, ChatGPT can serve as a catalyst to stimulate thinking and refine ideas, offering immediate feedback and promoting self-assessment. The constructive contrast process with this external intelligence represented by ChatGPT fosters deeper learning, allowing students to explore new perspectives and consolidate knowledge through a lens of critical thinking. Its application actively contributes to the educational field, standing out particularly in the development of critical thinking and the consolidation of learning. The use of artificial intelligence significantly increases students’ interest, as well as their sense of achievement and autonomy in the learning process. Learner autonomy is strengthened, and its implementation in university classrooms has led to a marked increase in motivation and satisfaction with the learning experience. Karataş et al. ( 2024 ), in a qualitative case study, demonstrates that ChatGPT plays an important role in developing language learners’ critical thinking, intercultural competence, and communication skills abilities essential for secondlanguage acquisition. Chan & Hu ( 2023 ), via a questionnaire, found that learners consider generative AI technology to have great potential for providing immediate feedback and learning support tailored to individual needs, which can revolutionize traditional teaching methods, improve the efficiency of personalization learning, and foster autonomy inquiry. Sasikumar & Sunil ( 2023 ), applying AHP–TOPSIS multicriteria decision analysis, revealed how AI tools, through personalization interactions and immediate feedback content analysis, help students generate and revise writing drafts, driving a shift toward dialogic and autonomy learning methods. 4 Conclusion This study employed a SLR structured in three phases: planning, execution, and reporting. In the planning phase, I formulated the central question: “In which specific dimensions can AI most significantly support the teaching and learning of a second language?” To address this, I crafted multiple search strings across three databases, for example, Google Scholar, CNKI, Dialnet, yielding 670 references. After two rounds of screening, 64 articles met our inclusion criteria. To ensure source relevance, I applied a Likert scale and a doubleblind review process, ultimately selecting 28 studies for indepth analysis. My findings indicate that ChatGPT delivers significant benefits in four core dimensions: personalization; autonomy; immediate feedback; critical thinking. However, this work has certain limitations. First, although the systematic review provides a comprehensive overview of the state of the art, it lacks firsthand empirical data there are no qualitative interviews or quantitative surveys to probe in detail educators’ and learners’ experiences in real teaching contexts. Second, the investigation focused primarily on ChatGPT, without broadly covering other AI technologies in language learning. Third, despite using rating scales and a doubleblind procedure to mitigate bias, the literature selection and scoring process still partly relies on subjective judgments. Finally, my search was limited to publications in English, and Spanish, excluding relevant studies in other languages or cultural contexts. 5 Gaps and Contradictions in Current Research Despite growing interest in integrating AI into language teaching, the literature specifically on its application to secondlanguage teaching and learning remains scarce (Al Mughairi & Bhaskar, 2024 ; Sanz Manzanedo, 2025 ; Mah & Groß, 2024 ). Many studies (Son et al., 2023 ) note gaps in detailed descriptions of AIdriven materials and insufficient attention to nonverbal communication, for example, gestures, expressions, intonation, or culturally localized tasks. Uncertainty persists about which dimensions AI ChatGPT can most effectively support in L2 learning and teaching. Moreover, recent findings challenge the optimistic narrative of AI benefits. Al Mughairi & Bhaskar ( 2024 ) warn that excessive teacher reliance on AI ChatGPT may erode their problemsolving and critical thinking skills; Iqbal et al. ( 2025 ) caution that students’ overdependence can stifle their independent reflection; Karataş et al. ( 2024 ) highlights the risk of AI ChatGPT producing errors or perpetuating biases, potentially weakening learners’ autonomy and critical thinking. Wexell Machado & Canese ( 2024 ) emphasize that, although AI dialogues simulate real contexts, their lack of authenticity can limit learner creativity and critical thinking. Ethically and regarding privacy, the accumulation of sensitive data in AI (Yunina, 2023 ), the inability of antiplagiarism tools to detect AIgenerated content (Chan & Hu, 2023 ), and the potential for misuse or academic dishonesty (Iqbal et al., 2025 ; Al Mughairi & Bhaskar, 2024 ) demand robust regulatory and pedagogical frameworks to safeguard integrity and equity in the classroom (Gibert et al., 2024 ; Bond et al., 2024 ; García Fernández, 2023 ). 6 Future Directions To address these gaps and tensions, it is essential to develop pedagogical strategies that foster meaningful written and oral interactions with AI ChatGPT, aligned with clear communicative competence goals (Gibert et al., 2024 ). Chan & Hu ( 2023 ) recommend expanding sample sizes, employing longitudinal designs to track learners’ evolving perceptions of AI, and exploring integration across disciplines, cultures, and generations. Combining quantitative surveys with indepth interviews will capture both teachers’ and students’ attitudes and practices and will enable the creation of valid assessment instruments linking AI use to learning outcomes. Predicts that AI will reinforce educational personalization by continuously analyzing each student’s progress to dynamically adjust content and methods, enrich contextualized teaching through realworld and gamified simulations, and automate routine tasks so that educators can focus on developing students’ critical thinking and creative skills. An integrated approach that combines AI, projectbased pedagogies, and collaborative learning will make language learning increasingly efficient, inclusive, and motivating for all learners (Liu, 2023 ). Declarations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not‑for‑profit sectors. Conflict of interest/Competing interests (check journal-specific guidelines for which heading to use) The authors declare no competing interests. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Data availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Materials availability Not applicable. Code availability Not applicable. Author contribution Wu Guowei conceived and designed the study, developed the methodology, performed data curation and formal analysis, wrote the original draft, and reviewed and approved the final manuscript. References Al Mughairi, H. & Bhaskar, P. (2024). Exploring the factors affecting the adoption AI techniques in higher education: insights from teachers' perspectives on ChatGPT. Journal of Research in Innovative Teaching & Learning . https://doi.org/10.1108/jrit-09-2023-0129 Almelhes, S. A. (2023). 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(2024). Inteligencia artificial como herramienta en el aprendizaje autónomo de los estudiantes de educación superior. Invecom, 4 (2). https://doi.org/10.5281/zenodo.10693945 Mouta, A., Torrecilla-Sánchez, E. M. & Pinto-Llorente, A. M. (2024). Comprehensive professional learning for teacher agency in addressing ethical challenges of AIED: Insights from educational design research. Education and Information Technologies: Official Journal of the IFIP technical committee on Education . https://doi.org/10.1007/s10639-024-12946-y Román Mendoza, E. (2024). Inteligencia artificial generativa: lo posible y lo imposible en el aprendizaje del español en entornos educativos mixtos. Doblele. Revista de Lengua y Literatura, 10 , 15–29. https://revistes.uab.cat/doblele/article/view/v10-roman Román Mendoza, E. R. (2023). 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Journal of China Computer-Assisted Language Learning , (4), 1-19. https://doi.org/10.1515/jccall-2023-0015 Wexell Machado, L. E., & Canese, V. (2024). Métodos de apropiación de la inteligencia artificial en la enseñanza de idiomas y sus consideraciones éticas. Lengua Y Sociedad , 23 (2), 1021-1045. https://doi.org/10.15381/lengsoc.v23i2.29268 Xiao, Y., & Zhi, Y. (2023). An Exploratory Study of EFL Learners’ Use of ChatGPT for Language Learning Tasks: Experience and Perceptions. Languages , 8 (3), 212. https://doi.org/10.3390/languages8030212 Yunina, O. (2023). Artificial Intelligence Tools In Foreign Language Teaching In Higher Education Institutions. The Modern Higher Education Review , 8, 77–90. https://doi.org/10.28925/2617-5266.2023.85 Zou, B., Guan, X., Shao, Y., & Chen, P. (2023). Supporting Speaking Practice by Social Network-Based Interaction in Artificial Intelligence (AI)-Assisted Language Learning. Sustainability, 15 (4), 2872. https://doi.org/10.3390/su15042872 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7063248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":503246215,"identity":"d34079f2-ce5d-4b82-a74b-8af3119572d9","order_by":0,"name":"Guowei Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYNACAyBmb2A8QKIWngMMpGgBAYkEIrXIRyQfk/hQwJC44ebjB4d5cxgS+9sPMD78gUeL4Y20NMkZBkAtt9MMDvNuY0iccSaB2ZgHn5YZOWa3eQwYcjfcToBo2cCQwCaNz2FgLX9AWm4e/wDRwv+A/Sc+h8lLALUwgLTc4IHaIpHAxoDPYQY8z9J/9hhI1M88k1NwcO42CeMZNx42S+PTIt+efNjgxx8bY77jxzc+eLvNRra/P/ngR3wOMzgApiRgfBCDsQGPBqAt+KVHwSgYBaNgFAABANmPUPzPRniYAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Guowei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-07-07 08:53:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7063248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7063248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89687128,"identity":"7bba639c-a506-4fae-ab3e-52330e04d77d","added_by":"auto","created_at":"2025-08-22 15:49:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19075,"visible":true,"origin":"","legend":"\u003cp\u003eArticles analyzed for Systematic Literature Review (SLR)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7063248/v1/2ec104849b6e6a50b40ade08.png"},{"id":91945158,"identity":"1f4544e9-bb5a-4402-b263-217db4231cba","added_by":"auto","created_at":"2025-09-23 05:08:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":618485,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7063248/v1/8ad1153d-2aaa-459f-b74b-e41ca12016d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Key dimensions in which AI ChatGPT supports second language teaching and learning","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe term AI was coined by John McCarthy in 1956. Over the following decades, AI technology reached several important milestones. In the 1970s and 1980s, the emergence of expert systems and probabilistic expert systems marked the first major advances in rule-based reasoning and the handling of uncertainty. Later, the introduction of the backpropagation algorithm spurred the resurgence of artificial neural networks (Wexell Machado \u0026amp; Canese, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith the launch of ChatGPT by OpenAI in November 2022, the potential of AI-powered learning tools in language education attracted significant attention from researchers and education professionals. ChatGPT, a chatbot developed by OpenAI, leverages a large language model to generate human-like text (Xiao \u0026amp; Zhi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Karataş et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Its applications range from speech recognition and conversational assistants to search engines. ChatGPT can answer questions, drafting texts, reasoning logically, debugging code, and translating languages, and has demonstrated undeniable value in educational contexts (Sanz Manzanedo, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe integration of AI in language teaching not only enhances linguistic competence but also increases students\u0026rsquo; motivation and interest, becoming an innovative pedagogical strategy to address the educational challenges of the 21st century (Changoluisa Santacruz et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Introducing AI into the language classroom is not only a key step toward optimizing teaching effectiveness but also an essential way to renew teaching practices (Wexell Machado \u0026amp; Canese, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Today, in the era of classroom digitalization, AI stands as a transformative tool that is redefining the methods of language teaching and learning (Gibert et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough its adoption is expanding across all sectors, including education, there are still existing gaps. While the value of AI in language learning has been documented (Chicaiza et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Mah et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) points out that there is a lack of prospective studies analyzing its future impact on teaching practices. The literature review shows that ChatGPT can support foreign language learning; however, each study tends to focus on partial contributions. This raises the question: In which specific dimensions can AI most significantly support the teaching and learning of a second language? The objective of this paper is to employ a SLR to identify and analyze the most recent and cutting‑edge studies published between 2023 and 2025, with the aim of addressing the central research question. By doing so, it seeks to uncover existing gaps and contradictions in the current body of work and to offer concrete recommendations and an outlook for future investigations in this field.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eThe SLR approach adopted in this study follows the methodology proposed by Barbara Kitchenham (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which was designed to guide rigorous reviews in Software Engineering and the Humanities. This model is structured into three stages: planning, execution, and reporting of the review.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Planning Stage\u003c/h2\u003e\u003cp\u003eIn this stage, the review protocol is developed with the goal of minimizing bias and ensuring transparency throughout the process (Changoluisa Santacruz, 2024). The protocol includes the following components:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Research Questions\u003c/h2\u003e\u003cp\u003eFormulating precise questions is essential to guide the search and selection of sources. The central question of this study is: In which specific dimensions can AI most significantly support the teaching and learning of a second language?\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Search Strategy\u003c/h2\u003e\u003cp\u003eTo retrieve relevant literature, three academic repositories were consulted: Google Scholar, CNKI, and Dialnet. Search queries were conducted using combinations of key terms in both English and Spanish, as well as their synonyms. The main search strings used were:\u003c/p\u003e\u003cp\u003eC1: \u0026ldquo;Artificial Intelligence ChatGPT as a Second Foreign Language\u0026rdquo;\u003c/p\u003e\u003cp\u003eC2: Spanish equivalents\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a detailed account of the number of records retrieved from each database using these search queries.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe articles obtained from scientific databases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDatabase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Scholar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCNKI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDialnet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Primary Study Selection Procedure\u003c/h2\u003e\u003cp\u003eIn the initial phase, all documents whose titles and abstracts explicitly align with the study's scope were compiled in a spreadsheet. Duplicate records, resulting from their appearance across multiple databases, were immediately removed. The refined list then underwent a second round of evaluation based on the strict application of the inclusion and exclusion criteria described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This screening process considered factors such as publication period, type of media, language, document category, and source of origin ensuring that only those studies meeting the established criteria remained for analysis.\u003c/p\u003e\u003cp\u003eOnce this processing was completed, the selected articles constituted the final corpus of primary studies that would inform the systematic review, ensuring a coherent approach aligned with the research objectives.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInclusion and Exclusion Criteria\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInclusion Criteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExclusion Criteria\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetailed descriptions of AI-involved teaching processes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArticles that are not accessible in full text\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch subjects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFormal educational settings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInformal learning environments (e.g., self-study at home, non-accredited institutions)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLanguage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWritten in English or Spanish\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWritten in other languages\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI tools like ChatGPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-AI technological interventions (e.g., traditional multimedia teaching)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome measures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFocused on improving personalization learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo specific teaching outcomes or irrelevant to the research objectives\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublished from January 2023 to June 2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePublished before January 2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the third stage of review, these articles were assessed for their relevance to the study topic using the relevance criteria shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Each article was rated on a Likert scale based on its degree of relevance. Finally, the relevant information from the selected studies was recorded in a summary matrix.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelevance Criteria\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelevance Criteria\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonalization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutonomy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmediate feedback\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCritical thinking\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDuring the first screening, I identified 670 preliminary records pertinent to my research questions. In the second screening, I applied my predefined inclusion and exclusion criteria, yielding 64 articles eligible for further consideration.\u003c/p\u003e\u003cp\u003eDuring the third screening, each of these 64 articles was assessed using a fivepoint Likert scale applied to the relevance dimensions listed. The response options were a) Strongly Disagree; b) Disagree; c) Neutral; d) Agree; e) Strongly Agree. Each relevance criterion received a score from 1 (Strongly Disagree) to 5 (Strongly Agree). I then partitioned these scores into two aggregates: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varSigma\\:\\:(a\\:+\\:b)\\:\\varSigma\\:\\:(c\\:+\\:d\\:+\\:e)\\)\u003c/span\u003e\u003c/span\u003e An article was retained for indepth analysis if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varSigma\\:\\:(c\\:+\\:d\\:+\\:e)\\:\\)\u003c/span\u003e\u003c/span\u003eexceeded \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varSigma\\:\\:(a\\:+\\:b)\\)\u003c/span\u003e\u003c/span\u003e, as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Articles meeting the basic eligibility requirements but for which \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varSigma\\:\\:(a\\:+\\:b)\\:\\ge\\:\\:\\varSigma\\:\\:(c\\:+\\:d\\:+\\:e)\\)\u003c/span\u003e\u003c/span\u003e were noted as eligible yet not advanced to full analysis (Changoluisa Santacruz, 2024).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation of Articles Using the Likert Scale\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelevance Criteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ea\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ec\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ee\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmediate feedback\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCritical thinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs a result of the evaluation, 28 documents that meet the established criteria and are relevant to the research were selected Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eTo address the research question outlined above and to gain a deeper understanding of the topic, it is necessary to first present the most salient findings from the literature. Consequently, Consequently, I will answer the following: In which specific dimensions can AI most significantly support the teaching and learning of a second language?\u003c/p\u003e\u003cp\u003eMouta et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), using a SLR method, explored the personalization of the language learning experience. Their study demonstrates that artificial intelligence can design bespoke learning pathways, allowing students to adjust the pace according to their individual needs and thereby improve outcomes, advantages that are particularly pronounced in the personalization of the language teaching experience.\u003c/p\u003e\u003cp\u003eAccording to Son et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), a literature analysis reveals that AI tools can provide students with personalization, interactive guidance. In the context of language teaching, AI facilitates individualized learning without requiring direct teacher intervention, thanks to flexible environments that combine immediate feedback with modes of autonomy, thus enhancing learners\u0026rsquo; capacity for personalization and enabling them to meet the challenges of foreign language acquisition more successfully.\u003c/p\u003e\u003cp\u003eGarc\u0026iacute;a Fern\u0026aacute;ndez (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasizes that integrating AI into language instruction, through personalization and immediate feedback, it establishes AI as a highly effective pedagogical resource. This approach drives forward personalization in teaching, promoting students\u0026rsquo; assumption of an autonomy, leading role in their own learning process while notably enhancing their proficiency with technological tools.\u003c/p\u003e\u003cp\u003eRom\u0026aacute;n Mendoza (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reports that deploying ChatGPT for outofclass learning focused on autonomy facilitates personalization adjustments to the educational process, adapting to the specific personalization needs of diverse student profiles. This dynamic promotes the development of strategies for autonomy learning among secondlanguage learners, strengthening their ability to progress in language mastery without direct teacher oversight.\u003c/p\u003e\u003cp\u003eFuertes Guti\u0026eacute;rrez et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlight ChatGPT\u0026rsquo;s potential in foreign language teaching, especially for advancing personalization, as it allows the learning experience to be precisely tailored to each student\u0026rsquo;s individual requirements.\u003c/p\u003e\u003cp\u003eAl Mughairi \u0026amp; Bhaskar (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), employing Interpretative Phenomenological Analysis (IPA), delve into how ChatGPT meets the demands of personalization instruction, identifying its individualized adaptation capabilities as a key factor for implementing personalization practices in educational settings.\u003c/p\u003e\u003cp\u003eChangoluisa Santacruz et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), through a SLR, analyzed various AI tools applied to language learning and found that these systems can indeed provide genuinely personalization feedback. Tools like ChatGPT have demonstrated, thanks to their personalization algorithms, the ability to deliver tailored feedback to each learner, thereby optimizing the languageteaching environment.\u003c/p\u003e\u003cp\u003eArvelo (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), based on a systematic qualitative analysis of available AI tools, reveals that even though this learning model may seem informal it enables students to assumption of an autonomous leading role outside the classroom, creating a Personal Learning Environment (PLE) that emphasizes personalization. This approach fosters critical thinking and independent learning capacity while extending the reach of language instruction across varied contexts. ChatGPT stands out for its ability to generate comprehension questions, design personalization teaching materials, and provide immediate feedback to both teachers and students, thereby driving the development of critical skills and achieving deep levels of selfdirected learning.\u003c/p\u003e\u003cp\u003eSanz Manzanedo (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), using a mixedmethods approach that combined a SLR and a quantitative survey, demonstrated that artificial intelligence can continuously provide personalization conversational practice, where immediate feedback is the key element of adaptive learning. This capability allows instructional activities to be adjusted in real time according to each student\u0026rsquo;s progress and thereby improving educational outcomes. ChatGPT, as a chatbot capable of delivering immediate feedback, offers students essential opportunities for live conversational practice, facilitating direct interaction and immediate feedback, which translates into notable improvements both in the quality of oral exercises and in the effectiveness of immediate feedback.\u003c/p\u003e\u003cp\u003eXiao \u0026amp; Zhi (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), through a smallscale exploratory approach, demonstrated ChatGPT\u0026rsquo;s potential in language education, especially its capacity to deliver immediate feedback, personalization, promote critical thinking, and foster autonomy. ChatGPT can provide students with personalization guidance and develop their autonomy by adapting to the learner\u0026rsquo;s language level and engaging them in highly interactive, personalization dialogue. Moreover, it can generate customized content tailored to students\u0026rsquo; specific needs, providing immediate feedback and personalization on specific queries, thus reinforcing learner autonomy. In terms of quality, ChatGPT\u0026rsquo;s outputs exhibit critical thinking by offering reasoned judgments, which helps mitigate potential threats to academic integrity. In this way, through immediate feedback, it promotes the development of critical thinking and autonomy.\u003c/p\u003e\u003cp\u003eIqbal et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), employing an exploratory sequential mixedmethods design, identified multiple advantages of generative AI in secondlanguage acquisition: it not only highly delivers personalization learning materials to students but also, provides immediate feedback via interactive exercises and adaptive learning environments, which significantly boosting learners\u0026rsquo; motivation and engagement. The study highlights that integrating AI technologies can optimize teaching efficiency and support the creation of personalization learning pathways, thereby advancing more precise and effective secondlanguage instruction. Additionally, AI, through interactive practice and adaptive environments, facilitates immediate feedback that promotes continuous learning.\u003c/p\u003e\u003cp\u003eBond et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), in a metareview of reviews, systematically synthesized prior assessments of AI\u0026rsquo;s application in education. Their findings show that adaptive learning systems exemplified by chatbots significantly enhance autonomy and critical thinking by personalization learning content to meet individual needs. Furthermore, through detailed immediate feedback mechanisms, they effectively improve writing quality and overall academic performance.\u003c/p\u003e\u003cp\u003eRugaiyah (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), via a Systematic Review, demonstrated that AIdriven languagelearning tools can substantially expand learners\u0026rsquo; vocabulary and grammatical knowledge, improve comprehension, and strengthen speaking skills through personalization and adaptive learning pathways and immediate feedback, which is one of their primary strengths. Whether by flagging grammatical errors, using conversational chatbots, or speechrecognition\u0026ndash;based pronunciation correction, these tools help learners quickly detect and correct errors, thereby improving linguistic accuracy and fluency. Additionally, they track learning progress in real time and dynamically adjust difficulty and content, allowing learners to advance at their own pace of autonomy, face continuous challenges without cognitive overload, and maximize learning effectiveness.\u003c/p\u003e\u003cp\u003eHong (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reports that research has shown students can receive highly personalization tutoring via ChatGPT. This system not only generates discussion topics and creativewriting prompts but also effectively fosters the development of the four language skills listening, speaking, reading, and writing. It improves grammatical accuracy and increases vocabulary while boosting learner motivation and confidence in language use. ChatGPT can automatically correct essays, provide improvement suggestions, design lesson plans, and generate exercises, questions, and situational dialogues tailored to individual needs, thus driving learning experience with personalization in which immediate feedback plays a crucial role.\u003c/p\u003e\u003cp\u003eRom\u0026aacute;n Mendoza (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argues that AIgenerated texts can supply language learners with authentic, varied, and highly personalization input. Integrating approaches such as Critical Digital Pedagogy or Critical Technological Pedagogy into language instruction is fundamental for constructing a pedagogical paradigm centered on critical thinking.\u003c/p\u003e\u003cp\u003eAlmelhes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), via a SLR, explored in depth AI\u0026rsquo;s immediate feedback mechanisms and offered practical guidance on how to design diverse feedback modalities to enhance autonomy learning experiences on both mobile devices and computers.\u003c/p\u003e\u003cp\u003eChicaiza et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), using a quantitative, nonexperimental, crosssectional, descriptive, analytical, and interpretive design, showed that AI can accurately identify learners\u0026rsquo; specific areas for improvement and provide precise personalization feedback, dynamically adjusting learning content. AI not only simulates nativespeaker conversations and offers continuous, ondemand practice but also instantly corrects grammatical errors, optimizes vocabulary use, and improves pronunciation through a highly interactive, adaptive model that allows students to progress at their own pace and focus on individual improvement.\u003c/p\u003e\u003cp\u003eCisneros V\u0026aacute;squez et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) employed a bibliographic review method, focusing on AI integration in education, particularly in personalization. They concluded that AI\u0026rsquo;s adaptive personalization and immediate feedback capabilities hold great potential to significantly enhance educational outcomes. However, the effectiveness of these applications will depend greatly on how these systems are designed, implemented, and managed within educational contexts.\u003c/p\u003e\u003cp\u003eGhafar et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) through data collection, focused scientific writing, literature review, and problem analysis, explain how AI can support learner autonomy by continuously providing personalization instruction and immediate feedback. AI delivers constant personalization teaching, supplying students with extensive immediate feedback and scaffolded activities. AIbased immediate feedback systems can be used effectively in foreign language learning to obtain realtime correction.\u003c/p\u003e\u003cp\u003eLiu (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) predicts that AI\u0026rsquo;s future development in foreign language teaching will be broad and innovative. Leveraging speech recognition and adaptive technologies, AI systems will rapidly and precisely adjust instructional content according to individual learner needs, enabling true personalization. Moreover, AI\u0026rsquo;s error correction and immediate feedback play an irreplaceable role in promoting learners\u0026rsquo; selfrevision and strengthening their Autonomy.\u003c/p\u003e\u003cp\u003eMenacho et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) employed a quantitative design and empirically verified that systematically introducing AI technologies into the classroom environment can effectively guide students toward autonomy and foster their willingness to collaborate. Through digital terminals and intelligent platforms, this model dynamically tailors\u0026rsquo; educational content to individual learner needs, resulting in a personalization learning experience. Simultaneously, AIdriven automated assessment and immediate feedback effectively support collaborative discussion among students and promote their autonomy in higher education contexts. Thus, as a datadriven teaching aid, AI is crucial for enhancing both learning outcomes and learner autonomy in university students.\u003c/p\u003e\u003cp\u003eYunina (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argues that AI provides students with personalization learning methods focused on autonomy and aimed at achieving better outcomes in foreign language acquisition. Currently, its application in language teaching enables the creation of personalization courses and flexible learning pathways. Such personalization learning fosters more effective study by optimally adapting to student needs and promoting greater success in language learning. AIdriven automated scoring applications, using natural language processing and deep learning algorithms, offer students immediate feedback.\u003c/p\u003e\u003cp\u003eZou et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), using questionnaires, semistructured interviews, and pre/posttest data, demonstrated the motivational effect of socialnetwork\u0026ndash;driven interaction on AIassisted speaking practice: learners not only maintained high engagement with ongoing interaction but also received personalization corrections in pronunciation, fluency, and lexical accuracy through teachers\u0026rsquo; immediate feedback, resulting in significant improvements in their speaking performance.\u003c/p\u003e\u003cp\u003eWexell Machado \u0026amp; Canese (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), via a surveybased study, concluded that AI can personalize learning content and adjust pedagogical strategies to meet student needs. The immediate feedback and personalization services AI provides allow teachers to devote more time to creative and complex tasks. Because AI can deliver immediate feedback and support autonomy, it significantly enhances both learner efficiency and motivation.\u003c/p\u003e\u003cp\u003eGibert et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) an exploratory research design was adopted to demonstrate how artificial intelligence tools can enhance language competencies by offering adaptive, personalization learning experiences. AI has the potential to become increasingly personalization and efficient, and it could radically transform the way contemporary students engage with language and literature. Strategically integrating technology into instructional design enables more accessible, personalization, and effective language teaching. Within the framework of personalized instruction, ChatGPT can serve as a catalyst to stimulate thinking and refine ideas, offering immediate feedback and promoting self-assessment. The constructive contrast process with this external intelligence represented by ChatGPT fosters deeper learning, allowing students to explore new perspectives and consolidate knowledge through a lens of critical thinking. Its application actively contributes to the educational field, standing out particularly in the development of critical thinking and the consolidation of learning. The use of artificial intelligence significantly increases students\u0026rsquo; interest, as well as their sense of achievement and autonomy in the learning process. Learner autonomy is strengthened, and its implementation in university classrooms has led to a marked increase in motivation and satisfaction with the learning experience.\u003c/p\u003e\u003cp\u003eKarataş et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), in a qualitative case study, demonstrates that ChatGPT plays an important role in developing language learners\u0026rsquo; critical thinking, intercultural competence, and communication skills abilities essential for secondlanguage acquisition.\u003c/p\u003e\u003cp\u003eChan \u0026amp; Hu (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), via a questionnaire, found that learners consider generative AI technology to have great potential for providing immediate feedback and learning support tailored to individual needs, which can revolutionize traditional teaching methods, improve the efficiency of personalization learning, and foster autonomy inquiry.\u003c/p\u003e\u003cp\u003eSasikumar \u0026amp; Sunil (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), applying AHP\u0026ndash;TOPSIS multicriteria decision analysis, revealed how AI tools, through personalization interactions and immediate feedback content analysis, help students generate and revise writing drafts, driving a shift toward dialogic and autonomy learning methods.\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThis study employed a SLR structured in three phases: planning, execution, and reporting. In the planning phase, I formulated the central question: \u0026ldquo;In which specific dimensions can AI most significantly support the teaching and learning of a second language?\u0026rdquo; To address this, I crafted multiple search strings across three databases, for example, Google Scholar, CNKI, Dialnet, yielding 670 references. After two rounds of screening, 64 articles met our inclusion criteria. To ensure source relevance, I applied a Likert scale and a doubleblind review process, ultimately selecting 28 studies for indepth analysis. My findings indicate that ChatGPT delivers significant benefits in four core dimensions: personalization; autonomy; immediate feedback; critical thinking.\u003c/p\u003e\u003cp\u003eHowever, this work has certain limitations. First, although the systematic review provides a comprehensive overview of the state of the art, it lacks firsthand empirical data there are no qualitative interviews or quantitative surveys to probe in detail educators\u0026rsquo; and learners\u0026rsquo; experiences in real teaching contexts. Second, the investigation focused primarily on ChatGPT, without broadly covering other AI technologies in language learning. Third, despite using rating scales and a doubleblind procedure to mitigate bias, the literature selection and scoring process still partly relies on subjective judgments. Finally, my search was limited to publications in English, and Spanish, excluding relevant studies in other languages or cultural contexts.\u003c/p\u003e"},{"header":"5 Gaps and Contradictions in Current Research","content":"\u003cp\u003eDespite growing interest in integrating AI into language teaching, the literature specifically on its application to secondlanguage teaching and learning remains scarce (Al Mughairi \u0026amp; Bhaskar, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sanz Manzanedo, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mah \u0026amp; Gro\u0026szlig;, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Many studies (Son et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) note gaps in detailed descriptions of AIdriven materials and insufficient attention to nonverbal communication, for example, gestures, expressions, intonation, or culturally localized tasks. Uncertainty persists about which dimensions AI ChatGPT can most effectively support in L2 learning and teaching.\u003c/p\u003e\u003cp\u003eMoreover, recent findings challenge the optimistic narrative of AI benefits. Al Mughairi \u0026amp; Bhaskar (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) warn that excessive teacher reliance on AI ChatGPT may erode their problemsolving and critical thinking skills; Iqbal et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) caution that students\u0026rsquo; overdependence can stifle their independent reflection; Karataş et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlights the risk of AI ChatGPT producing errors or perpetuating biases, potentially weakening learners\u0026rsquo; autonomy and critical thinking. Wexell Machado \u0026amp; Canese (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasize that, although AI dialogues simulate real contexts, their lack of authenticity can limit learner creativity and critical thinking. Ethically and regarding privacy, the accumulation of sensitive data in AI (Yunina, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the inability of antiplagiarism tools to detect AIgenerated content (Chan \u0026amp; Hu, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the potential for misuse or academic dishonesty (Iqbal et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Al Mughairi \u0026amp; Bhaskar, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demand robust regulatory and pedagogical frameworks to safeguard integrity and equity in the classroom (Gibert et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bond et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Garc\u0026iacute;a Fern\u0026aacute;ndez, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"6 Future Directions","content":"\u003cp\u003eTo address these gaps and tensions, it is essential to develop pedagogical strategies that foster meaningful written and oral interactions with AI ChatGPT, aligned with clear communicative competence goals (Gibert et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Chan \u0026amp; Hu (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) recommend expanding sample sizes, employing longitudinal designs to track learners\u0026rsquo; evolving perceptions of AI, and exploring integration across disciplines, cultures, and generations. Combining quantitative surveys with indepth interviews will capture both teachers\u0026rsquo; and students\u0026rsquo; attitudes and practices and will enable the creation of valid assessment instruments linking AI use to learning outcomes.\u003c/p\u003e\u003cp\u003ePredicts that AI will reinforce educational personalization by continuously analyzing each student\u0026rsquo;s progress to dynamically adjust content and methods, enrich contextualized teaching through realworld and gamified simulations, and automate routine tasks so that educators can focus on developing students\u0026rsquo; critical thinking and creative skills. An integrated approach that combines AI, projectbased pedagogies, and collaborative learning will make language learning increasingly efficient, inclusive, and motivating for all learners (Liu, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not‑for‑profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/Competing interests (check journal-specific guidelines for which\u003c/strong\u003e\u003cstrong\u003eheading to use)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWu Guowei conceived and designed the study, developed the methodology, performed data curation and formal analysis, wrote the original draft, and reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl Mughairi, H. \u0026amp; Bhaskar, P. 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M\u0026eacute;todos de apropiaci\u0026oacute;n de la inteligencia artificial en la ense\u0026ntilde;anza de idiomas y sus consideraciones \u0026eacute;ticas. \u003cem\u003eLengua Y Sociedad\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(2), 1021-1045. https://doi.org/10.15381/lengsoc.v23i2.29268\u003c/li\u003e\n\u003cli\u003eXiao, Y., \u0026amp; Zhi, Y. (2023). An Exploratory Study of EFL Learners\u0026rsquo; Use of ChatGPT for Language Learning Tasks: Experience and Perceptions. \u003cem\u003eLanguages\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(3), 212. https://doi.org/10.3390/languages8030212 \u003c/li\u003e\n\u003cli\u003eYunina, O. (2023). Artificial Intelligence Tools In Foreign Language Teaching In Higher Education Institutions. \u003cem\u003eThe Modern Higher Education Review\u003c/em\u003e, 8, 77\u0026ndash;90. https://doi.org/10.28925/2617-5266.2023.85\u003c/li\u003e\n\u003cli\u003eZou, B., Guan, X., Shao, Y., \u0026amp; Chen, P. (2023). Supporting Speaking Practice by Social Network-Based Interaction in Artificial Intelligence (AI)-Assisted Language Learning. \u003cem\u003eSustainability, 15\u003c/em\u003e(4), 2872. https://doi.org/10.3390/su15042872\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence (AI), ChatGPT, Systematic Literature Review (SLR), Second language","lastPublishedDoi":"10.21203/rs.3.rs-7063248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7063248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, artificial intelligence (AI) has developed at a remarkable pace, bringing unprecedented opportunities and challenges to a wide range of fields, including healthcare, finance, and particularly education. Its application in foreign language teaching and learning has shown immense potential. However, as an emerging technology, the integration of AI into education remains in an exploratory stage. Existing studies often focus on isolated functionalities or preliminary outcomes, lacking a systematic and comprehensive theoretical and practical framework. Consequently, there are still significant gaps and controversies regarding the mechanisms, impact pathways, and sustainability of AI in second language acquisition.\u003c/p\u003e\u003cp\u003eTo address these research gaps, this study takes ChatGPT a representative generative AI tool as its central focus. It adopts the Systematic Literature Review (SLR) methodology, adhering to rigorous procedures of planning, execution, and reporting of the review. The goal is to systematically examine and synthesize the current body of domestic and international research on the application of ChatGPT in second language teaching and learning. Specifically, the study aims to identify the key dimensions in which AI can significantly support language instruction such as personalized learning, real-time feedback, motivation enhancement, and productive language output and to explore the mechanisms through which it facilitates language development. Additionally, the review seeks to pinpoint contradictions, limitations, and underexplored areas in the existing literature. Based on these findings, the paper offers targeted recommendations for future research and instructional practice to advance both theoretical understanding and educational effectiveness in the field of second language education.\u003c/p\u003e","manuscriptTitle":"Key dimensions in which AI ChatGPT supports second language teaching and learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 15:41:21","doi":"10.21203/rs.3.rs-7063248/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a19f836-3503-40e6-bc82-773d50614f8e","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-23T05:08:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 15:41:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7063248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7063248","identity":"rs-7063248","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}