Artificial Intelligence in a foreign language education (AIFLED) has been gaining special attention globally. The emergence of Intelligent Tutoring Systems, AI conversational agents, ChatGPT, robots and other AI tools in foreign language learning has prompted a surge in research and recommendations. The previous systematic literature reviews include a focus on the integration and impact of advanced technologies, with an emphasis on Artificial Intelligence in Language Education and a broader examination of new technologies (Liang et al., 2021; Zhang & Zou 2020; Tobing et al. 2023). Pedagogical applications, such as mobile learning, multimedia tools, and digital game elements, are highlighted as effective tools for enhancing language learning experiences and motivation. Furthermore, cognitive aspects in language education has received specific attention. Positive outcomes, including improved language skills and increased learner motivation, have been consistently reported. The studies also recognize challenges and limitations related to technology integration, emphasizing the need for ongoing research to address issues like short intervention periods and the effectiveness of emerging technologies. 

The swift technological progress in the field of AI raises a multitude of inquiries and challenges related to utilization of AI in schools, encompassing its impact on language acquisition, affective or psychological states, or assessment methods. There have been several systematic literature reviews on AI in foreign language teaching, human-computer collaboration in language education, and technology-enhanced language learning; however, the research objects are mainly university or college levels and higher education learners (Ji et al., 2022; Zhang & Zou, 2020; Liang et al., 2021; Sharadgah & Sadi, 2022; Tobing et al., 2023).  This study aims to add to the current research by focusing on studies in schools settings (K-12).

The objective of this systematic literature review is to investigate and synthesize the applications of AIFLED within school settings. The review aims to provide an understanding of the current state of research and identify emerging trends and gaps in the literature during the period between 2019 and 2023.

The following research questions guided our study:

• What AI tools have been employed in Foreign Language (FL) teaching in schools between 2019 and 2023?
• What pedagogical or foreign language aspects have been researched regarding the AI applications?
• What challenges and opportunities are associated with the integration of AI tools in FL education within school environments?

The conceptual framework for this review is grounded in the intersection of three main pillars:

-       Pedagogical Integration: Examining how AI tools are integrated into pedagogical practices in FL teaching. This includes exploring theoretical framework, instructional design and the adaptability of AI tools.

-       Learning Outcomes: Evaluating the impact of AI tools on language learning outcomes, including but not limited to linguistic proficiency, cultural understanding, student engagement and perceptions of AI. 

-       Challenges and Opportunities: Investigating the challenges faced and opportunities presented by the integration of AI tools in FL education. This involves exploring issues such as student acceptance, ethical considerations, and potential enhancements in language learning experiences.

The conceptual framework will guide the systematic analysis of literature, providing a structured approach to understanding application of AI tools in FL teaching in schools from 2019 to 2023.

The systematic literature review adhered to PRISMA (2020) guidelines, encompassing three phases: Identification of papers, screening, and inclusion. The criteria for article eligibility included language (English), relevance to foreign language learning, utilization of AI tools, school setting context, empirical data inclusion (qualitative, quantitative, or mixed), publication within the last five years (2019-2023), and publication in scientific papers through peer-reviewed journals. Exclusion criteria comprised other educational settings like college/university, various types of studies/theoretical descriptions (e.g., descriptive papers, conference papers). Studies related to first language, sign language, or computer language learning were excluded, along with those solely involving teachers and teacher education, as well as studies focused on development, or description of AI tools. Databases Scopus, Google Scholar, and Web of Science were systematically searched between October and December 2023. Keywords and search strings included terms such as "Foreign language," "Artificial Intelligence," "AI tools," "Machine learning," "Deep learning," "Chatbots," "Speech recognition," "Secondary education," and "Primary/Elementary/Middle/High Schools." Initially retrieving 16,800 papers on Google Scholar, 13,783 on Web of Science, and 85 on Scopus, the search was refined using keywords and filters, yielding 344 references. These were uploaded to Rayyan.ai and subjected to screening based on titles and abstracts. 280 papers were excluded at this stage; 206 papers were on AI tools at the university/college level, 17 on AI application in translation or linguistics, and 15 offering theoretical reviews of AI tools. Further examination of full texts of 42 papers revealed only 16 empirical studies describing AI tool applications in foreign language classes within a school context. Data extraction process consisted of specific information extracted from each included study: publication year, school level, study participants, target foreign language, language level, utilized AI tool, procedure, research methods, key findings, and challenges which will be elaborated in our presentation.

The research findings across various studies underscore the transformative impact of integrating AI, particularly through the utilization of chatbots and virtual agents, into FL educational settings. A recurring theme across these studies is the substantial improvement in FL learning outcomes. The incorporation of AI has demonstrated notable enhancements in oral English proficiency, vocabulary acquisition, pronunciation, fluency, and language use. Furthermore, AI-supported activities, such as chatbot-assisted dynamic assessment and virtual interactions, have positively influenced speaking competence, listening comprehension, and overall language acquisition. A significant aspect of AI's role in foreign language education revolves around personalized learning and adaptability. AI tools, particularly chatbots, have been instrumental in providing tailored learning experiences that adapt to individual proficiency levels. The incorporation of adaptive learning paths, facilitated by tailored chatbot features, has been recognized as valuable for refining teaching methods and fostering adaptive learning environments that cater to diverse learner needs. The studies consistently report positive learner experiences, with participants expressing sustained interest, motivation, and enjoyment when engaging with AI technologies. Additionally, AI chatbots has been associated with a reduction in foreign language anxiety among students. The creation of a supportive and non-critical practice environment by AI has contributed to increased confidence in language use. However, challenges such as technical issues, the need for human supervision, and potential biases in algorithms are also acknowledged. Common limitations include small-scale designs, variability in experiences, and perceived scenario relevance. Recommendations focus on enhancing realism, addressing technical issues, personalizing learning, providing more feedback, and aligning with national curricula. Future research should explore individual factors, conduct efficacy studies across proficiency levels, implement user suggestions, consider long-term impacts, incorporate diverse participants, explore proficiency-related preferences, and address cognitive load. Implications emphasize the positive impact of AI chatbots on foreign language learning, but variability in experiences calls for continuous improvement.

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