AI기계번역을 활용한 외국어교육의 방향성 - 포스트에디팅 수업 사례를 통해 -

Globalization is a trend in the future, and the exchanges among countries in the world in economy, politics, and culture are becoming more and more frequent. In order to overcome the increasingly prominent language barriers, translation has become an indispensable “bridge” for communication between countries. Manual translation has long been unable to meet the growing needs of translation. So, machine translation came into being, especially in recent years, machine translation has developed rapidly. Compared with manual translation, machine translation has unique advantages, that is, fast translation speed and free access. With the gradual improvement of the quality of machine translation, the quality of translation has basically met the requirements of rough reading, which makes machine translation one of the hotspots in recent years. However, to achieve the level of intensive reading, the quality of machine translation needs to be further improved. A thorough study of translation error analysis is an important step to improve the quality of machine translation. At present, there are few studies on error analysis of business English in academic circles, and most of them are just simple classification of translation errors, lacking in in-depth empirical research. This study aims to study the advantages and disadvantages of machine translation in translation practice based on wireless sensor network communication algorithms, analyze the typical errors in machine translation, put forward the idea that posttranslation editing can make up for the shortcomings of machine translation, treat English machine translation more objectively, make full use of machine translation tools, combine machine translation with post-manual editing perfectly, improve translation speed, and guarantee translation quality at the same time.

Foreign language anxiety (FLA) has been a perennial concern in language learning, as foreign language (FL) learners often communicate feelings of anxiety, stress, or nervousness. This study explored the role of artificial intelligence (AI) applications in speaking practice for FLA management of 48 undergraduate participants in an EFL class in Egypt. An eight‐week, quasi‐experimental pretest–posttest design examined learner anxiety levels using a 33‐item FLA questionnaire. Their oral proficiency was assessed via roleplaying using an interaction‐enhanced public version of the IELTS speaking evaluation rubric. The results confirmed that FL learners experienced FLA pre‐ and post‐intervention. The identified anxiety levels played a facilitative role in FL learning with several ensuing gains. The use of conversationally enhanced AI chatbots in interactive activities slightly intensified learners' FLA, thus meriting further investigation of these objectives. Overall, subject to further development, AI chatbots are promising for significantly improving linguistic output gains; however, this study found that learners' speech‐related anxieties were not reduced following the interactions with the chatbots. It is concluded that FLA plays an underexplored facilitative role in sharpening learner cognitive faculties and linguistic capacities. Moreover, AI chatbots may be beneficial in advancing FL learning with significant potential in EFL contexts, facilitating improved interaction and oral communication. These findings support the integration of AI technologies as effective tools in FL education, providing flexible, interactive, and learner‐centred learning. This study is expected to be of considerable interest to FL educators and learners.

Machine Translation (MT) which has been championed as an effective technology for knowledge transfer from English to languages with less digital content. An example of such efforts is the automatic translation of English Wikipedia to languages with smaller collections, such as Arabic. However, MT quality is still far from ideal for many of the languages and text genres. While translating a document, many sentences are poorly translated which can provide an incorrect text, and confuse the reader. Moreover, some of these sentences are not as informative and could be summarized to make a more cohesive document. Thus, for tasks in which complete translation is not mandatory, MT can be effective if the system can provide an informative subset of the content with higher translation quality. For this scenario, text summarization can provide effective support for MT by keeping only the most important and informative parts of a given document to translate. In this work, we demonstrate a framework of MT and text summarization which replaces the baseline translation with a proper summary that has higher translation quality than the full translation. For this, we combine the state of the art English summarization system and a novel framework for prediction of MT quality without references. Our framework is composed of the following major components: (a) a standard machine translation system, (b) a reference-free MT quality estimation system, (c) an MT-aware summarization system, and (d) an English-Arabic sentence matcher. More specifically, our English-Arabic system reads in an English document along with its baseline Arabic translation and outputs, as a summary, a subset of the Arabic sentences based on their informativeness and their translation quality. We demonstrate the utility of our system by evaluating it with respect to its translation and summarization quality and demonstrate that we can balance between improving MT quality and maintaining a decent summarization quality. For summarization, we conduct both reference-based and reference-free evaluations and observe a performance in the range of the state of the art system. Moreover, the translation quality of the summaries shows an important improvement against the baseline translation of the entire documents. This MT-aware summarization approach can be applied to translation of texts such as Wikipedia articles. For such domain-rich articles, there is a large variation of translation quality across different sections. An intelligent reduction of the translation tasks results in improved final outcome. Finally, the framework is mostly language independent and can be easily customized for different target languages and domains.

PurposeHow closely does the translation match the meaning of the reference has always been a key aspect of any machine translation (MT) service. Therefore, the primary goal of this research is to assess and compare translation adequacy in machine vs human translation (HT) from Arabic to English. The study looks into whether the MT product is adequate and more reliable than the HT. It also seeks to determine whether MT poses a real threat to professional Arabic–English translators.Design/methodology/approachSix different texts were chosen and translated from Arabic to English by two nonexpert undergraduate translation students as well as MT services, including Google Translate and Babylon Translation. The first system is free, whereas the second system is a fee-based service. Additionally, two expert translators developed a reference translation (RT) against which human and machine translations were compared and analyzed. Furthermore, the Sketch Engine software was utilized to examine the translations to determine if there is a significant difference between human and machine translations against the RT.FindingsThe findings indicated that when compared to the RT, there was no statistically significant difference between human and machine translations and that MTs were adequate translations. The human–machine relationship is mutually beneficial. However, MT will never be able to completely automated; rather, it will benefit rather than endanger humans. A translator who knows how to use MT will have an opportunity over those who are unfamiliar with the most up-to-date translation technology. As MTs improve, human translators may no longer be accurate translators, but rather editors and editing materials previously translated by machines.Practical implicationsThe findings of this study provide valuable and practical implications for research in the field of MTs and for anyone interested in conducting MT research.Originality/valueIn general, this study is significant as it is a serious attempt at getting a better understanding of the efficiency of MT vs HT in translating the Arabic–English texts, and it will be beneficial for translators, students, educators as well as scholars in the field of translation.

Family factors significantly influence the development of critical thinking (CT) dispositions. Yet, the dynamics of this influence among foreign language (FL) learners remain underexplored, with potential moderating factors still unclear. Building on social capital theory, social support theory, and ecological systems theory, this study explored this relationship among FL learners and further investigated whether teacher support moderates this connection. A total of 16,006 secondary students from China participated in the study, spanning grades 7 through 12 (M age = 14.842 years, SD = 1.416). Data were collected using student-reported CT disposition scale, FL teacher support scale, and SES questionnaire. Multiple regression analysis revealed a positive relationship between SES and CT dispositions of FL learners. Furthermore, moderation analysis showed that teacher support positively moderated this relationship. That is, higher levels of teacher support enhanced the positive effect of SES on CT dispositions among FL learners. This research has both theoretical and practical implications for the development of CT dispositions among FL learners.

Machine translation based on artificial intelligence has many commercial applications, such as Google translation, Baidu translation, and Youdao translation. More artificial intelligence and its translation are still used in all aspects of life. Therefore, we should reexamine its impact on the relationship between human translation and machine translation. Therefore, based on this background, this paper discusses the impact of the development of artificial intelligence machine translation on the relationship between human translation and machine translation. Although the translation accuracy and overall situation of machine translation based on artificial intelligence are similar to that of human translation, the basic algorithm of machine translation is still a program that judges right and wrong through computer code. It cannot simulate the “faithfulness, expressiveness, and elegance” of human translation in combination with social background and human culture. However, for some mechanical operations, such as business translation, scenes with low requirements, such as common vocabulary in daily tourism, can still meet the needs. Therefore, under the influence of artificial intelligence machine translation, the relationship between the two is that machine translation can replace human translation in some aspects, but it cannot replace human translation.

This special issue of NJLTL comprises contributions from researchers and practitioners engaged in foreign language teaching and learning in higher education – both within a Nordic context and beyond. Due to the large number of contributions received, the special issue will be published in two parts. The first part is scheduled for publication at the end of 2025, with the second part due to appear in Spring 2026. The first part includes 6 research contributions in the main section and 8 articles in the forum section. The articles, which focus on teaching and/or learning English, French, German, and Spanish as foreign languages, are written in Danish, English, Norwegian, and German.

In AI in Foreign Language Learning and Teaching: Theory and Practice, Marcel Danesi underscores the ramifications of Artificial Intelligence (AI), including the use of tools like ChatGPT, on Foreign Language Teaching (FLT) and its critical components: language acquisition and teaching. Danesi covers several aspects of FLT to speak, write and assess, voicing their greatest concerns on the impact of AI. Danesi provides a balanced view, considering both the advantages and challenges of dependence on AI in language education. He articulates important ethical arguments on the impact of AI in education, particularly its failure to address students’ emotional and contextual concerns, while highlighting the gap human instructors fill. In addition, the book provides synthesis of the frameworks on the use AI technologies and pedagogical tools to improve FLT while maintaining the human dimension of teaching.

The recent advent of more powerful machine translation (MT) tools has significant implications for foreign language teaching and learning. While current research and pedagogical guidelines on MT in the foreign and second language classroom have focused primarily on survey data, less is known about how students actually use MT tools. The current article chronicles a computer tracking study of novice learners of French as a Foreign Language (n=26) as a way to inform this field of research. Drawing on diverse data sources (screen-recorded observations, retrospective recalls, post-interviews) and a Critical Incident Technique analytical approach, the article showcases the actions and cognitive processes related to MT that both supported and hindered student participants in their foreign language writing. Supportive behaviors and mindsets centered on a specific awareness of MT tool limitations paired with appropriate action while detrimental behaviors and mindsets centered on inappropriate input, lack of output analysis, and time. Suggestions for how these use-based findings might inform how MT is discussed in the language classroom conclude the article.

Nowadays foreign language is not just a part of nation's culture, it is also the guarantee of student’s successful career in the future. The achievement of high level proficiency of foreign language is not possible without fundamental language training in higher education institution. Most of them in our country provide students with at least two or even three foreign languages. In the context of higher school reforming, important changes in the educational technologies of foreign language teaching are envisaged. Modern language education is also gradually being modernized, introducing a modular rating system for teaching foreign languages. Interdisciplinary integration, democratization and economization of education determine the manifestation of innovative components in the teaching of the foreign languages. All this requires the formulation of the new claims for teaching and a foreign language teacher in the Free Economic Zone. At the current stage of society development, the purpose of foreign language teaching in higher education institutions is students' mastering of communicative competencies that will allow them to implement their knowledge, skills and abilities to solve specific communicative tasks in real life situations. The introduction of the new effective methods of foreign language teaching in the process of preparing students of humanities and technical specialties has become not only desirable but also necessary. Innovative orientation requires teachers to master and apply the pedagogical innovations, as well as stimulate the search for new forms and methods of educational process organization. High-quality language training of students is impossible without the use of modern educational technologies. The most effective are the professionally-oriented foreign language learning, the project work in teaching, the application of information and telecommunication technologies, the work with educational computer programs in foreign languages (multimedia system), the distance technologies in foreign language learning, the use of Internet resources, the foreign language teaching in the computer environment (forums, blogs, e-mail, etc.). Nowadays, the methodology of the use of computer programs is being developed actively getting more used. Computer programs offer ample opportunities to improve the process of foreign language learning, increasing its efficiency. The computer program provides the perception of information through auditory and visual channels, so it also allows you to organize teaching and control the foreign language acquisition in different modes of independent search and at different levels of complexity. Foreign language teaching means are important to ensure the full and effective organization of students' learning in the classroom to master foreign language activities.

Este trabalho tem o objetivo de identificar o uso de estratégias de tradução por alunos da habilitação em língua inglesa, em nível intermediário, do Curso de Letras da UFCG, de acordo com as Estratégias de Tradução de Chesterman (1997), as categorias de tradução de Jakobson (1958/2000) e a visão de Gutt (2000) sobre a tradução como uso interpretativo interlingual. Tal investigação apresenta um estudo comparativo entre as línguas inglesa e portuguesa buscando contribuir para um maior desenvolvimento do conhecimento sintático, semântico e pragmático dos alunos em ambas as línguas. Este estudo segue a orientação de Albir (1998), que acredita que o uso da tradução em sala de aula inclui dois aspectos: i) tradução interiorizada – feita por todo aprendiz de línguas estrangeiras; e ii) tradução pedagógica – utilizada em sala de aula como ferramenta pedagógica para reforçar e verificar a aprendizagem utilizando textos, análise contrastiva e reflexão. A tradução será considerada uma atividade comunicativa que auxilia no ensino tanto de línguas estrangeiras quanto de tradução, bem como no aprimoramento do conhecimento da língua materna e da língua inglesa através do uso de interlíngua.

ABSTRACTAs artificial intelligence (AI) has been integrated into foreign language (FL) education, learners' well‐being is influenced by various factors, including technological, personal and contextual elements. However, few studies explored how external and internal factors jointly shape FL learners' well‐being in the era of generative AI. To fill this gap, this study explores the effects of generative AI acceptance, perceived teachers' enthusiasm and self‐efficacy on FL learners' well‐being by investigating 613 university learners of English as a foreign language (EFL). The structural equation modelling results reveal that (1) generative AI acceptance positively predicts EFL learners' well‐being and self‐efficacy; (2) perceived teachers' enthusiasm does not predict learners' well‐being and positively predicts EFL learners' self‐efficacy; and (3) the self‐efficacy for receptive skills mediates the relationship between generative AI acceptance/perceived teachers' enthusiasm and EFL learners' well‐being, whereas self‐efficacy for productive skills does not play the mediation role. This research broadens the understanding of the antecedents of EFL learners' well‐being and extends the application of self‐efficacy theory in the AI‐driven educational environment, providing significant pedagogical implications.

Currently, information technology is rapidly developing and one of its branches can be called machine translation. The use of machine translation in the process of understanding each other by people from different countries is increasing every year. At the moment, Google and Yandex machine translations are among the best machine translations. The quality of machine translation from Yandex and Google is improving every year. However, according to the results of the experiment, when translating from English or Russian into Kazakh and Turkic languages, the quality of the translation decreases. This was shown by the translation result obtained from these two machine translations in March 2024. After all, translation has also shown that it is directly related to the structure of language. Since 2000, scientists from the state of Kazakhstan have been actively studying translations into the Kazakh language. The goal of the work is to improve the quality of translation from English into Kazakh. For this purpose, a transforming model was created for the Kazakh and Turkic languages for learning translation in neural machine translation OpenNMT(). The created model studied and learned an English-Kazakh parallel corpus of 180,000 words. Later, the document with a structure of 20,000 different English sentences was translated into Kazakh. The result is measured using the Blue() metric. The translation result showed a high level. It is shown that in order to improve the results of the experiment carried out in the work during model training, it is necessary to increase the number of parallel corpora created from the English-Kazakh language pair.

There are a large number of foreign language learners in China. Lots of research has been done on how to improve foreign language learning efficiency, but in reviewing the foreign language learning researches, it can be easily seen that the cultural effects on foreign language learning have not been given its due attention. Much of the research has been done within the scope of particular theories. This thesis attempts to explore the science of foreign language learning from the perspective of cultural effects on language learners, especially the effects of culture on foreign language learning beliefs. The efficiency of foreign language learning can be improved by improving learning strategies, which in turn are determined by language learning beliefs. The language learning beliefs are adversely affected by such cultural factors in Chinese culture as patterns of social hierarchy, face, collectivism and past oriented time concept. These cultural patterns help explain the existing language learning beliefs in China. Understanding the effects of these cultural patterns on foreign language learning beliefs, foreign language teachers can better understand students' learning beliefs and help students in turn with the evaluation of their learning beliefs; students of foreign languages can develop their own independent understanding of the foreign language, avoiding some false language learning beliefs.

This study focused on instructors teaching German as a foreign language at two UK universities and examined their experiences with and attitudes toward free online neural machine translation (FONMT). In September 2022, eight semi-structured interviews were conducted to gather data. The results indicate a general openness toward FONMT, with instructors making varying assumptions regarding their learners’ reasons for utilizing FONMT. In addition, the instructors’ underlying conceptions of language learning in relation to FONMT became apparent and influenced their perspectives on assessment. The author presents FONMT from an instructor's perspective as a triangular relationship between assumptions (about students’ rationales), expectations (in light of language learning), and assessment (of language learning). Consequently, it appeared essential to consider potential reasons for using FONMT and the objectives of language learning when implementing FONMT in foreign language teaching and learning. By doing so, strategies become visible for both teacher training and language instruction that may assist learners in a purposeful use of FONMT while resisting its allure.