Introduction

Society is a complex whole consisting of many cultures. Interaction and penetration of different cultures contribute to socio-economic progress and development. In today's information society, the primary carrier of intercultural communication is language, so translation between different languages becomes key in the process of cultural communication (Ren, 2021). Due to the exponential growth of tourism, the internationalization of business and trade, and the complexities of international relations and foreign policy on a global scale, translation services are becoming particularly important (Shehab & Thawabteh, 2020). The continuous expansion of China's overseas cultural exchanges and the policy of ‘Chinese cultural output’ have a profound effect on China's translation market, which offers more opportunities and places higher demands on translators. In the context of China's ‘going global’ culture strategy, exchanges between countries are becoming more frequent, and there is a growing demand for highly qualified English translators (Wei, 2020a). In the past few years, China has focused more on English translation studies and tried to improve English translation education by enhancing virtual group interaction to collaborate on translation projects, introducing digital pedagogical tools (such as online educational communication platforms and mobile applications for machine translation) and emphasizing the study of post-editing techniques.

In the Chinese context, five new directions in translation education have emerged in the twenty-first century. First, with the introduction of constructivism, translation education becomes student-centered rather than teacher-centered. Secondly, when developing a training program for translation, the demand in the labor market is taken into account. Thirdly, the process method of teaching is being implemented. Fourth, disciplines such as corpus linguistics and English for Special Purposes offer new sources of inspiration for teaching translation. Finally, new methods and technologies in the digital age play an important role in teaching translation in China (Liu et al., 2022).

Today it is hard to imagine a translator performing their duties without using any computer tools—from the very contact with the customer, through word processors, to a whole set of specialized translation programs (CAT programs—Trados Studio, MemoQ, Wordfast, OmegaT, Transit NXT Professional, etc.) and digital applications (Google Translate, Waygo, Baidu Translate, Naver Papago Translate, Microsoft Translator, etc.). Therefore, the impact of technology must be reflected in the education strategies to meet the requirements of a rapidly evolving market and equip students with the necessary knowledge and skills for professional activity in the digital era (Sayers et al., 2021).

The challenge of education for translators is to teach students how to use new technologies and determine how and when the added value of human intuition, creativity, and ethics can and should be used (Heng, 2018; Massey & Ehrensberger-Dow, 2017). Therefore, it is necessary to develop new teaching methods to improve student's language competence and lay a solid foundation for their future professional development (He, 2021).

The purpose of this paper is to explore the practical aspects of using artificial intelligence technologies in teaching English–Chinese translators by taking on the following tasks:

  • Describe current trends in the development of AI technology in the translation sphere;

  • Hold an online conference, ‘Translation Skills in Times of Artificial Intelligence,’ inviting teachers of Chinese higher educational institutions;

  • Prioritize the key competencies of a translator necessary for successful professional activities in the context of digital transformation of socio-economic business relationships;

  • Assess the demand for online services (online educational platforms, mobile applications for teaching English, mobile applications for machine translation, online communication services, online service for joint work on projects) in teaching English–Chinese interpreters;

  • Assess the influence of online services on professional competencies of English–Chinese translators;

  • Based on the competent approach to translator training, develop a pedagogical concept of the online education course ‘Synchronous and asynchronous translation in a digital environment.’

Literature Review

Digital technology is rapidly integrating into the education of translators, dramatically changing the concepts and teaching methods in Chinese educational institutions and providing the ability to customize learning modes and automate data processing (Jiang, 2020). The development of various microclasses, flipped classes, and big data analysis techniques allow for the effective implementation of digital learning. Artificial intelligence (AI) educational technologies provide educators with the ability to support active social learning qualitatively, conduct interactive, hands-on training courses in a virtual environment, track student progress, analyze both qualitative and quantitative data, and determine the most effective teaching methods based on the student's level of knowledge (Li, 2021). Several new learning practices (from software localization to remote interpreting) have gradually begun in curricula (Hubscher-Davidson & Devaux, 2021).

The development of autonomous self-learning AI technologies, and the potential limitations of human creativity and autonomy affect professional translators (Massey & Ehrensberger-Dow, 2017). There is already a demand for translators who perform an extended, adaptable intermediary role in business circles. They should have advanced technical translation skills and the ability to work in a technological translation environment, perform post-editing tasks and analyze, compose, edit, and adapt texts in various (social) media, and provide linguistic advice to writers. This profile fits the broader concept of translation as a strategic, collaborative creative activity.

Machine translation (MT) has recently undergone a period of evolution and development (Bi, 2020). Machine translation is a software system working on the principle of translation, which is also the technical core of machine translation. It can be complemented by other translation support, such as an electronic dictionary, term management, alignment tools, quality check, word count statistics, text analysis, project management, etc. (Wei, 2020b). As a branch of computer linguistics, machine translation has proceeded with computer technology, information theory, linguistics, etc. It has changed from dictionary machine translation to corpus automated translation (CAT), and then to artificial intelligence with the help of neural machine translation (NMT) (Zong, 2018).

The new translation software has become highly simplified, stylized, and humanized. English-speaking professionals working in translation companies must be proficient in essential CAT software and skillfully use Trans Star, SDL Trados, SDLX, Idiom, SDL passolo, Alchemy Catalyst, memoQ, Wordfast, etc. in their workflows (Zhao, 2020). However, the machine translation mechanism relies on rigid grammatical rules. These systems often have significant variations in comprehension during source text processing and difficulties accurately capturing semantic features (Li & Hao, 2021). In addition, students face some difficulties when using digital tools in the learning process. The biggest problem when applying computer programs to learning is that these programs can only prove their value in practice but not in teaching. Without the accumulation of adequate translation memory and term databases, students can only master basic operations. Appropriate training that can help combine machine translation with instruction is rarely offered (He, 2021; Liu, 2018).

Recent advances in neural networks and deep learning have allowed machines to achieve unprecedented accuracy in interpretation and production. Deep learning methods are widely used to analyze and understand text sequences, recognize human speech, convert it to text, and translate it from one language to another. Various online services now exist in machine translation, including Google Translate, Microsoft Translate, DeepL, and open-source alternatives—ESPNet and FBK-Fairseq-ST (Sayers et al., 2021).

Neural machine translation uses an electronic network trained to identify receiver information or data projects and translate them into desired knowledge as output (Kanglang & Afzaal, 2021). AI translators are digital tools or applications that use advanced technology to translate images, visuals, human speech, and even meaning. These digital tools provide rapid translation accuracy and fewer misunderstandings across languages. In addition to written text translation, incredible advances have been made in speech translation, better known as interpretation. Networks such as Google Pixel Buds, Waverly Labs Pilot, and Bragi are used for interpretation (Kanglang & Afzaal, 2021).

Revolutionary developments in AI and deep learning have greatly facilitated natural language processing and improved the accuracy and quality of machine translation. The current trend in machine translation is neural networks, which can quickly process giant amounts of language data, making neural machine translation very accurate (Das, 2018). For example, Google Translate offers three models: neural machine translation, phase-based machine translation (PBMT), and autoML translation, allowing users to dynamically translate processed languages using pre-trained machine learning models provided by Google. When a client requests translation through the API, the Google platform applies NMT methods (Kolhar & Alameen, 2021).

The new technological structure of artificial intelligence based on natural language recognition, translation, transfer, and other technological capabilities has provided a more intelligent multichoice for language teaching and learning (Zheng & Zhu, 2021). Various platforms with intelligent features, such as automatic translation evaluation system, parallel comparison of students' translations, and peer evaluation, have been developed to help translation learning (He, 2021).

The main attention in teaching English translation should be given to the development of the thinking abilities of students, the improvement of their theoretical knowledge and skills, as well as their reasonable application in practice. The ability of students to innovate and solve practical problems is important for translation work, therefore, the methodology of teaching translation should be aimed not only at improving the professional skills and knowledge of students in the field of translation, but also at developing the ability to solve problems in the context of a rapid innovative digital transformation of socio-economic life society (Huang, 2022). Multimodality is a typical feature of modern education and an inevitable requirement to improve the efficiency of translation skills and models. Due to the simultaneous emergence of multimodality and networking in translation skills, teachers need to integrate learning, distinguish between primary and secondary modes, and make learning integrated (Guan et al., 2022). The content of training and the methods of translation technology must be adapted to the changing work environment and the requirements of the modern needs of translators. The reconstruction of teaching content and methods by teachers should be based on the full use of technologies and software that keep pace with the times, including advanced learning platforms. Also, it is necessary to teach students to rationally evaluate the role of translation technology and its impact on the translation profession in the era of artificial intelligence (Jiang, 2022). The integration of media materials into the content of translation training can effectively stimulate the interest of students, deepen their understanding of the content of translation and guide them to more effective learning (Liu, 2023). In order to contribute to the overall improvement in the efficiency and quality of teaching translation in the digital age, it is necessary to encourage the innovativeness of teachers in the development of educational content, to introduce innovations in the content of translation teaching materials and teaching methods (Zhao, 2022). There is a real danger that technology could lead to a reductionist perception of language without acknowledging the richness and complexity of human interaction, identity and culture. In addition to recognizing the limitations of modern technology, educators must help learners of all levels interacting with machine translators to recognize these limitations. A language class that integrates machine translators should also provide students with an opportunity to learn about the limitations of machine translators (Urlaub & Dessein, 2022).

Materials and Methods

Research Design

The online conference ‘Translation Skills in Times of Artificial Intelligence’ was held in January 2022 on the online communication platform DingTalk to explore the practical aspects of using AI technology in training English-speaking translators. The conference was organized by three teachers at the Chinese University of Hong Kong (Shenzhen), who moderated the discussions. Advertising and information campaign with the help of public posts and mailing lists and registration of participants was carried out in WeChat social network.

Forty teachers of English–Chinese translation from Chinese higher education institutions participated in the conference. After being introduced to the aims and objectives of the study, twenty-nine participants agreed to participate in research surveys aimed at identifying practical aspects of AI technologies in English–Chinese translators' education (see Table 1).

Table 1 Information about the study participants
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The three-day online discussion addressed the following questions:

  • Key professional competencies of an English translator in a digital economy;

  • The influence of digital information environment on the development of students' translation skills;

  • The expediency of using AI digital tools based on pedagogical practices;

  • The demand for online services (online educational platforms, mobile applications for English language teaching and machine translation, online communication services, online services for joint work on projects) in practical pedagogical activities;

  • Innovative pedagogical methods of teaching simultaneous/asynchronous interpreting and translation, as well as developing post-editing skills.

At the end of the online conference, participants identified the key professional competencies of an English-speaking translator in a digital economy: linguistic, digital, technical, information, communication, special, business, and personal competencies (Fig. 1).

Fig. 1
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Key professional competencies of an English-speaking translator. *Developed by the authors

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The teachers were asked to prioritize each competency in the context of digital transformation in socio-economic relationships and assess the relevance of various online services (see Appendix 1) in their practice. The participants were also asked to assess the extent to which the possibilities of online services affect the development of professional competencies of an English–Chinese interpreter. The respondents were asked to answer the following question: ‘In your opinion, to what extent does the use of digital technologies based on artificial intelligence in educational practices affect the development of key professional competencies of an English-speaking translator, such as linguistic, digital, technical, information, communication, special, business, personal.’ (degree of influence: not significant, slightly significant, significant) (see Appendix 2). The surveying was conducted on the online communication platform DingTalk.

Statistical Processing

The surveys were compiled, administered, and evaluated by conference moderators using the Typeform online service. The moderators of the study conducted an independent sample T-test (P > 0.054) and the results did not show a significant difference. The error of the general method was tested using the one-factor Harman method. The degree of explanation of the variance of the first factor was 26.41% (below 50%), which confirms the absence of significant general method bias in this study.

Ethical Issues

The study was designed following the recommendations of the Declaration of Helsinki. Participants were informed about the goals and objectives of the study and signed an informed consent form about participation in the study, processing of personal data, and publication of the results.

Results

The teachers of higher education institutions in China rated each of the eight interpreter competencies using a 12-point scale (Fig. 2).

Fig. 2
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Priority of interpreter's competencies necessary for successful professional activity. *Developed by the authors

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The highest-rated competence was linguistic (average score—11.55). It includes the knowledge of lexical, grammatical, phonetic, and idiomatic structures of the source and target languages and translation conventions. Second place was taken by the communicative competence (average score—11.31). This skill includes simultaneous interpretation the ability to perform effective online and offline communication in different forms (face-to-face meetings, video conferences, chat rooms, blogs, forums, social networks, etc.). The third place in the rating belonged to technical competencies (average score—10.82), which is the ability to effectively use machine translation technology, perform high-quality post-editing, and evaluate translation quality. Digital competence was in fourth place (average score—10.21. This competence means the ability to effectively use digital technologies in simultaneous and asynchronous translations, readiness to work in a network and use professional online services and platforms. The fifth place was given to information competence (average score—9.69), which means searching, requesting, evaluating, processing, and retrieving information necessary for professional activity in the online environment. Special competence was in sixth place (average score—9.31). It means knowing a particular segment of the national economy. The seventh place in the competitiveness ranking was given to business competence (average score—8.83) the ability to conduct business negotiations with clients and partners, work in a team, follow the instructions, know the business etiquette and professional ethics. The last place was occupied by personal competence (average score—8.31), which consists in the ability and readiness to realize one’s potential fully, have self-management capacity, successfully solve professional tasks, and have a productive life.

The majority of survey participants (96%) believe that effective educational process for professional training of future translators should be supported by innovative features of modern digital technologies—online educational platforms, mobile applications for learning English and doing machine translation, online communication services, as well as online services for joint work on projects.

The most popular educational online platforms were Zuoyebang (93.1%), Udemy (86.2%), Coursera (86.2%), Absorb LMS (72.4%), LearnWorlds (58.6%), Dedao/iGet (51.7%) (Fig. 3) (Mean = 55.9%, Standard deviation = 27.21703).

Fig. 3
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Educational online platforms used by respondents in their teaching practice (% of respondents). * Developed by the authors

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86.2% of respondents believe that online educational platforms, with their innovative features, can provide quality support for distance learning activities, allowing teachers to monitor the learning process and adjust the learning activities of students and ensure synchronous and asynchronous communication and timely personalized feedback. Online learning platforms allow educators to generate relevant practical learning content in a virtual space and develop students' professional competencies needed in the workplace.

The most popular online services among mobile apps for teaching English were Duolingo (96.6%), Memrise (93.1%), Beelinguapp (86.2%), Grammarly (82.8%), and BaiCiZhan (72.4%) (Fig. 4).

Fig. 4
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Mobile apps for English language learning and teaching used by respondents in their practice (% of respondents). * Developed by the authors

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The most popular online services among mobile apps for teaching English were Duolingo (96.6%), Memrise (93.1%), Beelinguapp (86.2%), Grammarly (82.8%), and BaiCiZhan (72.4%) (Fig. 4) (Mean = 73.2%, Standard deviation = 18.62644).

A total of 93.1% of respondents confirmed that mobile apps for learning and teaching English play a key role in implementing the principles of personalized learning by providing personalized learning materials and guidance, which is especially valuable in language learning. Mobile apps for English language instruction promote hands-on learning experiences based on prior knowledge, learning goals, and student needs.

The most popular mobile applications for machine translation were Google Translate (100%), Waygo (93.1%), Baidu Translate (89.7%), Naver Papago Translate (86.2%), and Microsoft Translator (72.4%) (Fig. 5) (Mean = 73.4%, Standard deviation = 19.83123).

Fig. 5
figure 5

Mobile apps for machine translation used by respondents in their practice (% of respondents). * Developed by the authors

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According to 96.5% of respondents, mobile services for machine translation are convenient tools in educational practices and significantly increase the efficiency of educational processes due to the speed and ease of use and the popularity of online services among students.

The most demanded communication online services were WeChat (100%), Renren (96.6%), GoToMeeting (93.1%), Zoom (89.7%), and ClickMeeting (86.2%) (Fig. 6) (Mean = 84.7%, Standard deviation = 9.7931).

Fig. 6
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Communication online services used by respondents in their practice (% of respondents). *Developed by the authors

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86.2% of respondents confirm that communication platforms and services provide an immersive environment for social and business interactions. They help students develop the practical English-language communication competencies of an oral and written nature that are essential in the work of an interpreter.

The most frequently used online services for collaborative project work were Microsoft Teams (96.6%), Smartsheet (93.1%), Yalla (89.7%), Pronto (86.2%), and Element (82.8%) (Fig. 7) (Mean = 76%, Standard deviation = 18.96312).

Fig. 7
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Online services for collaborative project work used by respondents in their practice (% of respondents). *Developed by the authors

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One hundred percent of the respondents confirmed that collaborative project work has a significant impact on students' information, business, and personal competencies. Online services for collaborative project work are helpful pedagogical tools, as they allow participants of the working study group to plan joint activities, arrange online discussions, conduct audio, and video conferences, publish and edit texts within the workspace.

The results of the questionnaire survey of teachers/translators on the feasibility of using AI digital technology in education practices and the extent of their impact on the development of professional competencies of an English-speaking translator are presented in Table 2.

Table 2 Results of the survey among teachers/translators
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The survey results demonstrated that the use of AI technologies in education practices could have a constructive impact on the development of key competencies of a future translator. 85.5% of the respondents confirmed that innovative technologies in teaching could significantly impact the development of students' linguistic competencies. The importance of digital competence for a modern translator was confirmed by 93.1% of the respondents. Technical competence was deemed significant by an overwhelming 100% of respondents, and information competence was considered crucial by 96.5% of teachers. According to 75.9% of the teachers interviewed, interaction in an immersive virtual environment allows for the qualitative formation of English-language communication competencies. An online environment with enormous information resources allows students to form unique competencies essential for translators (confirmed by 89.7% of the respondents). The networked nature of social-educational interaction in a virtual community of practitioners can significantly impact business development (58.6% of respondents) and students' personal (55.2% of respondents) competencies.

Based on the competent approach to translator training and considering the importance of creating conditions for the development of abilities, knowledge, and skills necessary for successful professional translation activities, the authors developed the pedagogical concept of the online educational course ‘Synchronous and asynchronous translation in a digital environment’ (Fig. 8).

Fig. 8
figure 8

Pedagogical concept of the online learning course ‘synchronous and asynchronous translation in a digital environment’. *Developed by the authors

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The concept is based on the practical development of key competencies in an active group learning online environment—a virtual community of practice (VCoP) that relies on modern digital educational technologies. Training practices should focus on acquiring simultaneous/asynchronous interpretation and translation skills based on the thematic educational informational content (special knowledge in a particular business segment).

Discussion

Improving translation skills requires steady practice, so students need to improve language competence daily in class and whenever possible (Nguyen & Ngo, 2021). Research by Russian scholars has proved that the professional training of a future language worker (an interlanguage mediator or a translator) should develop professional competencies and meet the demands of the increased digitalization of socio-economic business relationships. Translators must have good reading, writing, listening, and speaking skills, memorization, fluency, and understanding of intentions and situations (Kobiakova & Shvachko, 2016). They must work with various information transfer processes in a digital environment (Enbaeva & Plastinina, 2021). For example, Polish universities' educational courses about using digital technologies for translation are gaining popularity. They are offered at every stage of the Polish higher education system. The observed proliferation of such courses shows a niche for them. It is driven by the growing market demand for tech-savvy translations, the increasing pace of translators' work, and the desire of translators to update and improve their knowledge and skills (Organ, 2021).

Besides acquiring professional skills in technical translation, it is essential to qualitatively promote the development of students' thinking abilities. It is crucial to focus not on the amount of knowledge but on the need to learn throughout life, i.e., to teach to reflect rather than think (Sigacheva et al., 2021). Innovative digital thinking of the future interpreter can be developed with the help of online training tools that can qualitatively support synchronous and asynchronous communication acts. Training a mediator/interpreter/translator requires the simultaneous development of L2 communicative competence and translation/interpretation competence. This is a complex task that can be solved in different ways: (1) by incorporating L2 communicative competence into the structure of translation competence, or (2) by incorporating the development of mediating skills into language education. Another challenge is bridging the digital divide, the answer to which is the effective use of various online sources and the combination of online and offline distance learning (Enbaeva & Plastinina, 2021).

The evolution of digital technologies has significantly democratized learning, with the teacher giving up more control to students and students becoming more integrated into convergent technologies (Marczak, 2018). The digital transformation of socio-economic relations and the educational system had a significant impact on the pedagogy of translation and interpretation. For example, they emphasize the importance of the mediation process along with source and target content and highlight the need to focus on students' critical self-awareness (Colina, 2017). Based on constructivist learning theory, the student-centered educational concept emphasizes active inquiry, discovery, and knowledge construction. The student-centered approach changes the learning concepts, teaching methods, and assessment measures (Ge, 2021).

Since the outbreak of COVID-19, most institutions have conducted classes online. This period gave researchers a unique opportunity to study and validate the performance and effectiveness of learning activities based entirely on e-learning tools and platforms (Su et al., 2020). There are various types of online courses for trainee translators available in different formats and for audiences with different backgrounds in the open educational market.

Digital learning environments provide opportunities to implement active group learning. A quasi-experimental study among students at Hunan University (China) confirmed that creating a virtual community of practice has a significant impact on the effectiveness of post-editing learning. The success is explained by the networked nature of social interaction, as participants communicate and share knowledge through social networks, chat rooms, emails, and forums (Wang & Wang, 2021). Regardless of the learning paradigm and specific didactic solutions, the status and style of online translation courses, their relevance to the professional community, and the high demand from trainees prove the effectiveness and practical value of the online format (Tivyaeva, 2021).

If a translation education program is viewed solely in an academic, intellectual, or theoretical context rather than in a social or practical one, it loses relevance for the student, as confirmed by research on the experience of teaching undergraduate translation courses at Al-Quds University and An-Najah National University (Palestine) (Shehab & Thawabteh, 2020). In this regard, interpreter education must keep pace with technological developments, giving students as much direct experience as possible with the tools, processes, and practices they are likely to encounter in their professional lives. Particular attention should be paid to the development of information and digital literacy skills and working with (parallel) corpora and digital language data, which constitute a significant component of language technology. In addition, an overemphasis on technology in the training of translators should be avoided, as well as the associated risk of dependency and routinization. Curricula should encourage and foster human aspects such as intuition, creativity, and ethical judgment (Massey & Ehrensberger-Dow, 2017).

The extensive use of modern pedagogical technologies in education undoubtedly brings a new life and greater return and puts higher demands on teachers. In addition to extensive professional knowledge, excellent teaching experience, and outstanding pedagogical proficiency, they need to master the idea. Technological approaches of modern education, such as computer skills, multimedia, network learning tools, etc. (Su, 2021). In the context of rapid change, teachers must constantly progress, change traditional educational approaches, and strive to learn advanced teaching methods that can bring better learning outcomes (Zheng, 2021).

However, although both new machine translation technologies and digital language learning tools are constantly being introduced into the market, the educational approaches of Chinese educational institutions still produce interpreters and translators of intermediate and low levels. The modern interdisciplinary concept of ‘artificial intelligence + English translation’ is not the primary goal of language education (Jiang, 2020). The reason is that many supervisors, teachers, and students believe that artificial intelligence is simply a technology devoid of emotional care, cultural cognition, and evaluative judgment. The biggest challenge on the way to becoming excellent translators and interpreters is linguistic and cultural differences. Even with the skillful use of AI technology in translation, it is not easy to interpret cultural differences (Jiang, 2020). The integration of intercultural education into the teaching of the English translation is an inevitable requirement of time. A study at Guangzhou Foreign Language University confirmed that intercultural education and more research on intercultural education could promote students' understanding of foreign cultures and use cultural knowledge to interpret translated texts (Shi & Wang, 2019).

Post-editing machine translation has become a widespread practice in the language services industry. This skill is increasingly included in translation courses. Since machine translation tools cannot provide high-quality translation compared to manual translation, learning how to modify machine translation texts is essential in the training process for professional translators (Bonyadi, 2020).

Conclusions

Today's translation services market demands highly competent professional interpreters capable of performing high-quality simultaneous/asynchronous interpreting and translating using innovative technological solutions. In this regard, interpreter training must keep pace with technological developments, giving students as much direct experience as possible with tools, processes, and practices that simulate the realities of professional life.

Guided by a competency-based approach to interpreter training, considering the need to create conditions for the development of abilities, knowledge, and skills required for successful professional interpreting activities, the authors developed a pedagogical concept of the online educational course ‘Synchronous and asynchronous translation in a digital environment.’ The concept relies on the practical development of key translation competencies during an active group learning process in a virtual community of practitioners (VCoP) using state-of-the-art digital learning technologies. The course suggests training practices focused on acquiring the skills of simultaneous/asynchronous interpretation and translation based on the thematic educational, informational content (special knowledge in a particular business segment).

The survey results demonstrated that the use of AI technologies in education practices could have a constructive impact on the development of key competencies of a future translator. The majority of survey participants (96%) believe that effective educational process for professional training of future translators should be supported by innovative features of modern digital technologies. 86.2% of respondents believe that online educational platforms, with their innovative features, can provide quality support for distance learning activities. 93.1% of respondents confirmed that mobile apps for learning and teaching English play a key role in implementing the principles of personalized learning by providing personalized learning materials and guidance, which is especially valuable in language learning. 86.2% of respondents confirm that communication platforms and services provide an immersive environment for social and business interactions. One hundred percent of the respondents confirmed that collaborative project work has a significant impact on students' information, business, and personal competencies. 85.5% of the respondents confirmed that innovative technologies in teaching could significantly impact the development of students' linguistic competencies.

Limitations

The study’s main limitation was the small sample size (29 educators) from four universities in China. Due to the small sample size, the data obtained are preliminary and superficial and cannot be generalized.