Artificial Intelligence and Foreign Language Learning: ChatGPT’s Limitations in Summarizing Texts

during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access).

Foreign language learning anxiety is a topic that has long been scrutinized, and the anxiety present in foreign language learning has been studied to guide foreign language teaching as well as the setting of foreign language education scenarios. Today we have entered an artificial intelligence era, with the release of ChatGPT and the wide application of Big Language Modeling AI technology, in which a wide variety of Generative Artificial Intelligence (GAI) oral tutoring APPs have emerged. Due to the relatively short development time of the technology of Great Language Modeling, the foreign language training model for AI foreign language oral coaches built on the basis of this technology is still in the preliminary development stage, and there is less research on the improvement of foreign language learning sentiments brought about by GAI.What is the foreign language learning method provided by AI coaches helpful for foreign language learning? Does it alleviate foreign language learning anxiety? It has become a topic worthy of research. This study adopts the research method of semi-structured interviews to interview 20 Chinese college students and analyze the data text of the interviews. This study is based on the control value theory for questionnaire design and research, and in terms of control factors, it is found that using GAI Oral Coach to learn a foreign language is more controllable, and it can alleviate the learners' anxiety in many aspects of foreign language communication and exchange, such as: communicative anxiety, situational anxiety, oral anxiety, listening anxiety, and cognitive anxiety. In terms of value factors, due to the prerequisites of the control factors, the use of GAI oral coaching helped learners to significantly increase their sense of self-efficacy and emotional comfort, be more likely to feel a mood of accomplishment, and students to give high expectations in terms of future performance improvement. The role of emotional accompaniment and support found in this study was unanticipated before the study, and GAI oral coaches, in addition to helping to alleviate anxiety, can also provide positive emotional value through caring, companionship, and communication. Of course, there are two sides to everything, especially at the stage when GAI is just emerging, and there are still many imperfections to be explored, such as ethics, cognitive-emotional, and value bias. The findings in this paper help to understand how AI technology helps foreign language learning sentiments, and provide support for subsequent related research on AI accompanying foreign language learning.

As generative artificial intelligence (AI) tools and large language models (LLMs)-powered applications develop rapidly in the era of algorithms, it should be integrated thoughtfully to enhance English as a Foreign Language (EFL) teaching and learning without replacing learners’ critical thinking (CT). This study systematically analyzes the impact of generative AI tools and LLMs on language learners’ CT in EFL education using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to identify, evaluate, and synthesize relevant studies from 2022 to 2025. A thorough review of 15 selected studies focuses on generative AI tools and LLMs’ dual nature, research methods, main focuses, theory and models, limitations and challenges, and future directions in the field based on Web of Science (WoS), SCOPUS, ERIC, ProQuest, and Google Scholar. The findings identified generative AI tools and LLMs possessed both the potential to nurture and the risk of hindering CT in EFL education. 66.67% of studies reported generative AI tools and LLMs’ positive role in CT, while 33.33% of studies reported its negative role in CT. Furthermore, 3 types of research methods, 3 key themes of research focus, and 4 groups of theoretical perspectives were examined. However, 4 kinds of limitations in this field remain, including research scope, user dependency, generative AI reliability, and pedagogical integration. Future research can focus on assessing long-term effects, broadening research scope, promoting responsible AI use, and refining pedagogical strategies. Finally, Limitations, implications and future direction of this study were discussed.

As artificial intelligence (AI) tools become increasingly accessible in classrooms and homes, educators face a critical question: Can AI be used to enhance rather than replace deep cognitive and metacognitive engagement with text? In this chapter, we explore how AI technologies such as large language models (LLMs), intelligent tutoring systems, and adaptive reading platforms can support students in developing metacognitive reading comprehension strategies before, during, and after reading. Consistent with our goal of advocating for framing AI as a partner rather than a cognitive shortcut, we examine how students can be taught to think with machines, using AI to prompt reflection, monitor understanding, and regulate their reading processes. Drawing on theories of metacognition, self-regulated learning (SRL), and digital literacy, we share practical models and instructional approaches for integrating AI into metacognitive reading instruction. These approaches highlight strategy use in relation to the metacognitive awareness of reading inventories, such as the Metacognitive Awareness of Reading Strategies Inventory-Revised (MARSI-R), which assesses the awareness and use of global, problem-solving, and support reading strategies. We address both the affordances and limitations of AI, emphasizing ethical use, student agency, and equitable access, and include a set of reflective prompts and a four-point rubric for assessing metacognitive engagement with AI. By positioning AI as a metacognitive partner rather than a passive content provider, we offer a framework for educators seeking to prepare students for responsive, critical, and reflective reading in the age of intelligent technology.

The modern stage of technological advancement is characterized by the dynamic development of artificial intelligence (AI) technologies and their integration into education. Of the several dozen artificial intelligence technologies used in various spheres of human activity, the most widely used in education are: a) machine learning, b) natural language processing, c) data science and d) intelligent tutoring system. On their basis, artificial intelligence tools are created, which have significant language teaching potential and in many ways change the traditional roles of the teacher and learners in the educational process. However, it should be noted that the integration of artificial intelligence technologies into education in general and foreign language teaching in particular is currently at the initial stage. Educators and learning designers conduct pilot studies investigating the abilities of specific artificial intelligence tools in the formation of foreign language aspects or the development of learners' foreign language communication skills. At the same time, the limited number of empirical research studies does not allow us to talk about the systematicity and comprehensiveness of foreign language teaching based on artificial intelligence technologies. One of the key differences between artificial intelligence technologies and modern information and communication technologies is their AI’s ability to provide a much wider range of feedback. It is owing to this advantage of artificial intelligence that innovative methods of teaching a foreign language will be based, creating new additional conditions for students to master a foreign language and raising the learning process to a new level in terms of the quality of solving learning tasks. However, the consideration of the types of feedback provided by AI tools has not been the subject of separate research, which determined the importance of this study. The aim of the study is to identify the types of feedback provided to learners by artificial intelligence technologies for the subsequent development of teaching methods (teaching technologies and/or typologies of tasks and assignments) based on them. The definition of the types of feedback provided to users by artificial intelligence tools was based on a review and analysis of research in the field of pedagogy and foreign language teaching methods. The sample of sources included research articles and reviews published in academic journals indexed in Scopus and Web of Science (Q1 and Q2), as well as Russian academic journals, included in the list of the Higher Attestation Commission of the Russian Federation (Categories 1 and 2) (pedagogical sciences). The following aspects of teaching methods were the subject of study in the review and analysis of academic papers: a) the artificial intelligence tool used for receiving feedback; b) the target audience of learners; c) the purpose of interaction with artificial intelligence; d) the form of activities; e) the type of feedback used. As a result, the following six types of feedback provided by artificial intelligence tools were identified in this study: a) educational and social; b) information and reference; c) methodological; d) analytical; e) evaluative; f) conditionally creative feedback.

Artificial intelligence (AI) technologies have spread throughout the world and changed the way that many social functions are conducted, including health care. Future large-scale combat missions will likely require health care professionals to utilize AI tools among other tools in providing care for the Warfighter. Despite the need for an AI-capable health care force, medical education lacks an integration of medical AI knowledge. The purpose of this manuscript was to review ways that military health care education can be improved with an understanding of and using AI technologies. This article is a review of the literature regarding the integration of AI technologies in medicine and medical education. We do provide examples of quotes and images from a larger USU study on a Faculty Development program centered on learning about AI technologies in health care education. The study is not complete and is not the focus of this article, but was approved by the USU IRB. Effective integration of AI technologies in military health care education requires military health care educators that are willing to learn how to safely, effectively, and ethically use AI technologies in their own administrative, educational, research, and clinical roles. Together with health care trainees, these faculties can help to build and co-create AI-integrated curricula that will accelerate and enhance the military health care curriculum of tomorrow. Trainees can begin to use generative AI tools, like large language models, to begin to develop their skills and practice the art of generating high-quality AI tools that will improve their studies and prepare them to improve military health care. Integration of AI technologies in the military health care environment requires close military-industry collaborations with AI and security experts to ensure personal and health care information security. Through secure cloud computing, blockchain technologies, and Application Programming Interfaces, among other technologies, military health care facilities and systems can safely integrate AI technologies to enhance patient care, clinical research, and health care education. AI technologies are not a dream of the future, they are here, and they are being integrated and implemented in military health care systems. To best prepare the military health care professionals of the future for the reality of medical AI, we must reform military health care education through a combined effort of faculty, students, and industry partners.

The acceptance of technology at the higher educational level has been a significant discussion, with little attention on the gender dynamics on the acceptance of artificial intelligence (AI ) tools by senior lecturers. This study delved into a detailed analysis of the gender dynamics in the discussion of technology acceptance mainly AI tools, in foreign language (FL) education. Quantitative study approach was adopted in the process, and survey design was implemented. Data was collected using structured digital questionnaire, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of ninety-five (95) male senior lecturers and one hundred and three (103) female senior lecturers participated in the study. Analysis was conducted using relevant statistical measures. The results showed disparities in the attitudes and views of senior lecturers towards artificial intelligence (AI) technologies in the context of FL education, greatly influenced by gender. In relation to usage, male senior lecturers have higher positive reactions (61.06%) in comparison to their female counterparts (46.6%). However, in relation to the assumption that AI technologies improve the performance of learners, 69% of male senior lecturers agree with this notion, but a substantially greater percentage of 72.81% of female senior lecturers hold the same perspective. Moreover, there exists a little disparity in the level of proficiency in using AI technologies across genders. Specifically, 56.84% of male senior lecturers see it as uncomplicated, while 61.16% of their female counterparts share the same sentiment. The gender discrepancy that is most notable pertains to the perceived level of ease in using artificial intelligence (AI) technologies during foreign language (FL) lessons. The data reveals that a majority of male senior lecturers, calculated as 69.48%, see the use of these tools very easy. In contrast, a much higher proportion of female senior lecturers, 86.41%, share the same perception. This discrepancy highlights a notable disparity in confidence levels between the two genders. These results together emphasise the changing gender dynamics in the acceptance of technology, interrogating conventional assumptions and underscoring the need for customised support systems to guarantee fair and efficient integration of artificial intelligence (AI) technologies in foreign language instruction among senior lecturers.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

The socio-emotional dangers of using Artificial Intelligence (AI) technologies in second language (L2) education: Unveiling Chinese EFL teachers' perceptions and experiences.

The continuous advancements in artificial intelligence technology bring about changes in foreign language learning. One of the artificial intelligence applications, ChatGPT, developed by OpenAI, draws attention for its applicability in foreign language education. This study explores the opportunities provided by ChatGPT for the learning of four fundamental language skills (listening, reading, speaking, writing) in the context of German language education. The advantages and disadvantages of ChatGPT in foreign language education are examined. Recommendations are provided regarding the points to be considered when utilizing artificial intelligence for the acquisition of the four language skills. Additionally, the disadvantages and limitations of artificial intelligence are discussed. In conclusion, artificial intelligence offers new opportunities in foreign language education. Artificial intelligence applications like ChatGPT can assist students in developing communication skills in a foreign language. Particularly, ChatGPT is believed to have a significant role in enhancing reading and writing skills. However, the disadvantages and limitations of artificial intelligence should not be overlooked. Careful utilization of artificial intelligence in foreign language learning can play a crucial role in students' skill development and improvement of their communication abilities.

This study explores the lived experiences of English as a Foreign Language (EFL) learners engaging with Artificial Intelligence (AI) tools to support their academic writing development. Using a narrative inquiry approach, the research investigates how learners describe their interactions with AI technologies such as ChatGPT and Grammarly, and what challenges and opportunities they perceive. Eighteen university-level EFL students participated in in-depth interviews, revealing that AI tools are widely appreciated for enhancing linguistic accuracy, improving efficiency, boosting writing confidence, and supporting idea generation. These benefits align with the Technology Acceptance Model and Sociocultural Theory, suggesting that AI acts as both a facilitator of ease and a scaffold within learners’ Zones of Proximal Development. However, the findings also highlight substantial concerns related to academic integrity, over-reliance, and AI’s limitations in generating culturally nuanced or critically engaging content. This duality reflects the "Paradox of Assistance," where the same features that make AI valuable can also inhibit deeper learning if uncritically used. The study emphasizes the need for intentional, pedagogically guided integration of AI in EFL writing instruction, promoting a balanced Human-AI collaboration that empowers learners as autonomous and reflective writers.

Machine learning promises versatile help in the creation of systematic reviews (SRs). Recently, further developments in the form of large language models (LLMs) and their application in SR conduct attracted attention. We aimed at providing an overview of LLM applications in SR conduct in health research. We systematically searched MEDLINE, Web of Science, IEEEXplore, ACM Digital Library, Europe PMC (preprints), Google Scholar, and conducted an additional hand search (last search: February 26, 2024). We included scientific articles in English or German, published from April 2021 onwards, building upon the results of a mapping review that has not yet identified LLM applications to support SRs. Two reviewers independently screened studies for eligibility; after piloting, 1 reviewer extracted data, checked by another. Our database search yielded 8054 hits, and we identified 33 articles from our hand search. We finally included 37 articles on LLM support. LLM approaches covered 10 of 13 defined SR steps, most frequently literature search (n = 15, 41%), study selection (n = 14, 38%), and data extraction (n = 11, 30%). The mostly recurring LLM was Generative Pretrained Transformer (GPT) (n = 33, 89%). Validation studies were predominant (n = 21, 57%). In half of the studies, authors evaluated LLM use as promising (n = 20, 54%), one-quarter as neutral (n = 9, 24%) and one-fifth as nonpromising (n = 8, 22%). Although LLMs show promise in supporting SR creation, fully established or validated applications are often lacking. The rapid increase in research on LLMs for evidence synthesis production highlights their growing relevance. Systematic reviews are a crucial tool in health research where experts carefully collect and analyze all available evidence on a specific research question. Creating these reviews is typically time- and resource-intensive, often taking months or even years to complete, as researchers must thoroughly search, evaluate, and synthesize an immense number of scientific studies. For the present article, we conducted a review to understand how new artificial intelligence (AI) tools, specifically large language models (LLMs) like Generative Pretrained Transformer (GPT), can be used to help create systematic reviews in health research. We searched multiple scientific databases and finally found 37 relevant articles. We found that LLMs have been tested to help with various parts of the systematic review process, particularly in 3 main areas: searching scientific literature (41% of studies), selecting relevant studies (38%), and extracting important information from these studies (30%). GPT was the most commonly used LLM, appearing in 89% of the studies. Most of the research (57%) focused on testing whether these AI tools actually work as intended in this context of systematic review production. The results were mixed: about half of the studies found LLMs promising, a quarter were neutral, and one-fifth found them not promising. While LLMs show potential for making the systematic review process more efficient, there is still a lack of fully tested and validated applications. However, the increasing number of studies in this field suggests that these AI tools are becoming increasingly important in creating systematic reviews.

The the aim of the article is to study the way of optimizing the process of learning a foreign language, in particular English, using artificial intelligence (AI) technologies. The focus is on analyzing the functionality of AI applications that help improve key language capabilities, including writing, speaking, pronunciation, listening, and vocabulary. The study is aimed at identifying effective strategies for integrating AI technologies into the educational process to improve language learning. The study uses a comprehensive approach that includes an analysis of the functionality of AI applications such as Grammarly, Hemingway Editor, BoldVoice, Duolingo, as well as a comparative method to assess their effectiveness. The originality of the research lies in the examination of modern applications based on AI technology, which are rapidly developing and are increasingly used in education, but have not yet been sufficiently studied in terms of their impact on the process of learning foreign languages. The study highlights the necessity of further study of AI technologies in the context of education, as their potential remains insufficiently unlocked due to the rapid development and novelty of such applications. According to the results of the analysis, the use of artificial intelligence (AI) tools demonstrates the potential to optimize the process of language skills development. It is important to note that the use of AI requires a critical approach to evaluating the information provided. The individualized nature of AI applications limits their use in group practice, but they can be effective for independent study. At the same time, it is necessary to take into account the possibility of technical failures and errors in the operation of AI tools. It is advised to integrate AI applications into the educational process as additional tools, not as the leading ones. Also, professional development for teachers should be organized to ensure effective work with AI technologies and regular evaluation of their effectiveness. The findings can be used to improve the methods of teaching foreign languages in educational institutions.

Response to M. Trengove & coll regarding "Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine".