Behind the scenes of machine translation

Artificial intelligence-grounded machine translation has fundamentally changed public awareness and attitudes towards multilingual communication. In some language pairs, the accuracy, quality and efficiency of machine-translated texts of certain types can be quite high. Hence, the end-user acceptability and reliance on machine-translated content could be justified. However, machine translation in small and/or low-resource languages might yield significantly lower quality, which in turn may lead to potentially negative consequences and risks if machine translation is used in high-risk contexts without awareness of the drawbacks, critical assessment and modifications to the raw output. The current study, which is part of a more extensive project focusing on the societal impact of machine translation, is aimed at revealing the attitudes towards usability and quality as perceived from the end-user perspective. The research questions addressed revolve around the machine translation types used, purposes of using machine translation, perceived quality of the generated output, and actions taken to improve the quality by users with various backgrounds. The research findings rely on a survey of the population (N = 402) conducted in 2021 in Lithuania. The study reveals the frequent use of machine translation for a diversity of purposes. The most common uses include work, research and studies, and household environments. A higher level of education correlates with user dissatisfaction with the generated quality and actions taken to improve it. The findings also reveal that age correlates with the use of machine translation. Sustainable measures to reduce machine translation related risks have to be established based on the perceptions of different social groups in different societies and cultures.

The integration of big data with artificial intelligence in the field of digital health has brought a new dimension to healthcare service delivery. AI technologies that provide value by using big data obtained in the provision of health services are being added to each passing day. There are also some problems related to the use of AI technologies in health service delivery. In this respect, it is aimed to understand the use of digital health, AI and big data technologies in healthcare services and to analyze the developments and trends in the sector. In this research, 191 studies published between 2016 and 2023 on digital health, AI and its sub-branches and big data were analyzed using VOSviewer and Rstudio Bibliometrix programs for bibliometric analysis. We summarized the type, year, countries, journals and categories of publications; matched the most cited publications and authors; explored scientific collaborative relationships between authors and determined the evolution of research over the years through keyword analysis and factor analysis of publications. The content of the publications is briefly summarized. The data obtained showed that significant progress has been made in studies on the use of AI technologies and big data in the field of health, but research in the field is still ongoing and has not yet reached saturation. Although the bibliometric analysis study conducted has comprehensively covered the literature, a single database has been utilized and limited to some keywords in order to reach the most appropriate publications on the subject. The analysis has addressed important issues regarding the use of developing digital technologies in health services and is thought to form a basis for future researchers. In today's world, where significant developments are taking place in the field of health, it is necessary to closely follow the development of digital technologies in the health sector and analyze the current situation in order to guide both stakeholders and those who will work in this field.

Generalised access to the Internet and globalisation has led to increased demand for translation services and a resurgence in the use of machine translation (MT) systems. MT post-editing or the correction of MT output to an acceptable standard is known to be one of the ways to face the huge demand on multilingual communication. Given that the use of translation and MT post-editing are increasing the demand for language-skilled professionals, in this article we aim at evaluating the use of MT post-editing in the foreign language class. For this purpose we make use of computer-aided error analysis (CEA) to extract patterns of error found in translation and MT post-editing into the foreign language. This methodology will provide some insights as to the main difficulties found by the students in post-editing into the foreign language and about the suitability of using raw MT output as input for foreign language written production. Thus, a comparative analysis of error frequency is performed on the results of a group of advanced students of Spanish doing post-editing as compared to another group doing translation in order to gauge the level of difficulty of MT post-editing as opposed to translation into the foreign language.

Parents’ beliefs significantly influence how children experience and engage with language learning, especially in multilingual settings where local and global languages coexist. This study examines how Tenggerese parents understand and respond to their children’s English learning within an informal language course context. Situated in Tosari Village, Bromo, East Java, an indigenous and multilingual community, the research seeks to capture parental perspectives on English education amid ongoing cultural and linguistic diversity. Adopting a qualitative case study approach, semi-structured interviews were conducted with six parents whose children attend a local English course. The thematic analysis identified three central patterns: parents’ generally positive views of English and its perceived relevance for education, tourism, and global interaction; their conscious efforts to support English learning while maintaining Tenggerese language and cultural traditions; and differing forms of parental involvement shaped by socioeconomic conditions and educational backgrounds. Although many parents had limited English proficiency, they showed strong emotional commitment and viewed English as an additional resource rather than a replacement for their local language. These findings illustrate how indigenous parents engage in adaptive multilingual practices that connect global aspirations with cultural continuity. The study adds to existing discussions on parental beliefs in EFL contexts and offers insights for developing culturally responsive English education in rural and indigenous settings.

This study examined social influence and effort expectancy as predictors of librarians` use of AI technologies for information retrieval in libraries, with specific reference to Nigeria. To undertake the study, the correlational research design framework was used to examine professional librarians in public universities within the South South geopolitical zone of Nigeria. The population of the study comprised 301 librarians drawn from public university libraries within the South-South zone of Nigeria. Given the manageable size of the data, the entire population was used as the sample size. Thus, the total enumeration sampling technique was adopted. Questionnaire was used as the instrument for data collection and the data collected was analysed using both descriptive and inferential statistics with the help of the Statistical Package for the Social Sciences (SPSS). The findings revealed positive relationship between social influence and librarians’ use of AI technologies for information retrieval. The study also showed a correlation between effort expectancy and librarians’ use of AI technologies for information retrieval. Given the outcome of the study, the paper therefore concluded that critical AI literacy programmes should be organized for librarians and library users so they can become effective and efficient users of AI technologies for information retrieval. The study recommended that library administrators should invest more in AI technologies in the library because of its growing acceptance in society and because it is relatively easy to learn and use.

The development of cyber threats is impacting vital communications infrastructure, with a focus on 5G security, AI, and international cooperation. The purpose of this study is to analyze Enhancing Cyber Defense Capabilities through AI Technology. The research method used in this study is qualitative descriptive. The results of this study show that: First, the use of advanced AI technology in every aspect of the TNI will improve the overall security of the application. AI technology is able to detect and respond to cyber threats quickly and accurately, strengthening operational capabilities on land, sea, air, cyber, and space. Second, the integration of AI in the defense system through the Fourth Industrial Revolution will increase the efficiency and effectiveness of TNI operations in various dimensions. AI aids in the detection, analysis, and response to threats, strengthens decision-making capabilities in the field, and improves the combat readiness of troops. Third, the launch of STRANAS-KA is the foundation for the development of AI experts in the TNI, improving technical and managerial capabilities in AI to optimize the use of AI technology in defense operations. Finally, collaboration with the private sector enables the development of innovative AI solutions in defense operations, improves operational efficiency, and strengthens national resilience against increasingly complex and sophisticated cyber threats.

Emotional difficulties are increasingly prevalent amongst young people, yet the use of AI technology for emotion regulation remains limited. This study aimed to identify young people’s attitudes toward AI technology for emotion regulation and to analyse the factors influencing their decision to use or not use AI technology. Forty participants from China, comprising twenty males and twenty females, with a mean age of twenty-five, took part in the study. Data were collected through semi-structured face-to-face interviews and were analysed using NVivo 11 software. Grounded theory techniques and a three-stage coding approach were used to categorise the data. The grounded theory model demonstrated that user behaviours are influenced by three contextual factors: personal, technological and environmental contexts. Key influencing factors for user behaviours include fulfilling utilitarian, hedonic and social value needs such as perceived usefulness, ease of use, trust, positive emotions, interest, social perception, high value, convenience and privacy protection. This study offered theoretical insights and practical recommendations for designing and developing AI technology aimed at emotion regulation in youth populations.

This exploratory study investigates the use of online translation tools by Ukrainian war refugees in Czechia as a key multilingual communication strategy during their initial months of displacement, particularly in the absence of interpreters. The study aims to identify conceptual lenses that can guide further research on this strategy within translation studies. We explore the constitutive factors shaping communication mediated by machine translation (MT), the perceived benefits and drawbacks of MT use, and the contexts in which MT is deliberately avoided. Our findings suggest that MT use by displaced persons can be examined through concepts such as translational agency, power, national identity, and translational assimilation and accommodation. Practical implications include integrating MT tools into refugee language education, organizing MT literacy trainings for displaced persons and aid providers, and developing guidelines supporting multilingual communication in migration crises as part of state translation policy.

Chapter 40 - Artificial intelligence drives the digital transformation of pharma

Artificial Intelligence (AI) has found its application in many aspects of our lives. The COVID-19 pandemic has further allowed AI to play an increasingly important and beneficial role in our society, but it has also exposed the limitation of AI, particularly related to marginalized populations. This chapter first provides an overview of AI and equity pre-COVID, and then discusses what we know about AI during COVID-19. At the end, we conduct a systematic literature review to examine marginalized populations and their use of AI technologies during COVID-19. The populations examined in this review are children, older adults, people with disabilities, racial and ethnic minorities (in a country or region), low-income, gender, or general marginalized populations. The results indicate a huge gap for research on the use, adoption, and perception of AI technologies by communities that have previously experienced inequities in AI and COVID-19.KeywordsArtificial intelligenceMarginalized populationsCOVID-19Technology

In this article, I provide a retrospective look at the emergence of translation technologies and summarise the pros and cons of the use of neural machine translation and generative AI tools in medical translation. I will examine both the advantages and the risks for the medical translator.

Multilingual communication is frequently observed in immigrant communities due to contact between and across languages. Linguistic aspects of multilingual communication and the sociolinguistic factors that influence multilingual language use across participants and contexts have been widely studied albeit mostly through spoken and relatively small size data sets. Increasing availability of Internet based services and social media platforms make it convenient to collect publicly available and large-scale textual data for multilingual communication. In addition, availability of computational tools makes digital data attractive for research purposes. However, there are challenges and pitfalls for data scientists to be aware of while collecting and analysing multilingual data in immigrant settings. The goal of this chapter is to inform the reader about issues around multilingualism in immigrant contexts through introducing research questions, data sets and methods of analyses across academic fields (e.g. sociolinguistics and computational areas of research) and highlight the challenges and opportunities for future research.

The purpose of this qualitative study was to explore a multilingual learning community management facilitating language enthusiasts in enhancing their foreign languages skills in Jakarta, Indonesia. The method used was an ethnographic study. Data were collected through semi-structured in-depth interviews with the participants and the boards of the community. Also, field notes of what was observed were being written in which we involved as a participant of this community. Five themes that emerged in this research were (1) empowering social media as a communication tool and a language-sharing activity, (2) grouping participants based on language interest, (3) providing language coordinator for each language, (4) creating a multilingual environment in language learning, (5) fostering learner-centred in language learning. Conclusions and implications are discussed as well.

Objective. The purpose of the study is to analyze the possibility of using AI technologies to solve problems related to the selection of employees in project teams, based on production indicators and HR metrics of personnel.Method. Based on the fact that in the field of AI, methods mean algorithms by which tasks are solved, the following number of methods related to AI theory were identified: neural networks, fuzzy logic, expert systems, evolutionary modeling, Machine Learning.Result. An example of the use of AI is given in a situation where it is necessary to recruit personnel for a project based on the length of service and the degree of workload (where a scale with values from “highly loaded” to “not loaded” is used for workload). For the described example, an explanation is given that reveals the use of AI technologies (such as question-and-answer systems) in order to form HR metrics and production indicators. Additionally, the process of applying the objective function to obtain a numerical coefficient based on individual metrics or a combination of them is described in order to make a decision corresponding to its value based on the resulting indicator of the function.Conclusion. Within the framework of the conducted research, the historical patterns that led the field of personnel management to transformation and which made the use of AI technologies relevant in this area were considered. Continuous development and implementation of intelligent tools in the practice of project management facilitates HR processes and increases the efficiency of employee management. The use of AI technologies considered in the study will help to successfully monitor the state of both the project and the project team, which will have a positive impact on the productivity and profit of the enterprise.

Artificial Intelligence in Education (AIEd) is an emerging education field. This paper explicitly investigates the use of AI technology in language learning specifically in English as a Second Language (ESL). As a robust cloud-computing solution, AI-powered technology can be used to aid teaching and learning processes and improve language learning. While students' perspectives on AI-related research are prevalent, teachers' perspectives are not extensively discussed. In addition, studies on the effects of AI technology in ESL primary education, are understudied. Using two databases, namely Google Scholar and the Educational Resources Information Centre (ERIC), 12 articles related to the use of AI technology in ESL teaching and learning in primary schools were extracted out of 672, from 2019 to 2022 by using the PRISMA review methodology. Teachers’ perspectives were discussed based on the effectiveness, convenience, motivation and challenges. It is found that teachers perceived the integration of AI technology positively due to its dynamic characteristics and effectiveness despite facing various challenges resulting in disadvantages associated with its use. This paper explores AI technology's impact on ESL primary education from the perspective of teachers as curriculum stakeholders. Future research should delve further into the challenges that impede the use of AI technology in ESL primary education. This is essential for sustaining the momentum of AI-powered education.