2. Literature Overview
Previous studies of children’s engagement with technologies, and media in particular, highlight some research directions. The first group of authors concentrate on the impact of the Internet on children from an early stage of life [
2,
3,
11,
12]. Studies indicate that digital technologies are mainly useful for young children for four main purposes: leisure and entertainment; information and learning; creation, and communication [
3]. Another direction of studies focuses on research of challenges in relation to technology-enhanced teaching and learning [
8,
9,
13] smart environments of classrooms as well as tools and/or study platforms that support smart education (e.g., Google Classroom [
14]; EDUKA class [
15,
16] etc.). Although teachers are not likely to be replaced by robots, recent literature indicates a growing concern that machine-like processes will become more dominant in education [
17].
With the development of artificial intelligence, there is a growing body of research related to datafication as big data clearly has benefits for many different sectors in society, including education [
18]. If used effectively and ethically, data has become an integral part of education [
19]. In many respects, datafication should be understood as a global phenomenon—not only because of the transnational nature of the companies (i.e., Google), but also due to the overwhelming process of the digitization of everyday life. The researchers [
18] highlights the attempts of some educational sectors to support the development of online learning systems, whereas others, on the contrary, introduce social norms, such as the banning of mobile phones. Still, there are many individual schools or districts which use digital technologies to engage the interest and support for initiatives that teach young people about the risks and opportunities posed by datafication [
18]. According to the authors, schools and educational institutions rely on digital platforms to deliver content to students, to process attendance data, and manage all schooling needs. However, educational platforms, like other digital platforms, are powered by data. The more teachers and schools depend on platforms, the more data is generated, collected and used by technology companies. Some of this data is personal and sensitive and concerns student health and well-being [
18]. On the other hand, education in the form of “data literacy”, has become the dominant response to the challenges of datafication [
19]. As a result, Google has become one of the world’s most prominent providers of educational hardware and software since its first entry into education in 2005. Specifically, Google Classroom gained its enormous popularity resulting from the COVID-19 pandemic [
14]. Moreover, teachers and students are Google users not only in education settings but also in their free time, thus producing data from which Google receives a substantially big part of its income [
14].
The datafication of education is closely related to MT, which is rapidly developing into a tool that is transforming language learning and teaching, whereas traditionally, professional translators and language teachers did not take MT-generated texts seriously [
20]. The majority of schoolchildren and higher-education students have experimented with MT, but few know what it really takes to choose the right translation application and assess the quality of MT. Google Translate is the most extensively used and the most widely available MT tool that can quickly transform enormous amounts of text from one language to another, although the degree of accuracy is different in different languages [
20]. It has been estimated that around 500 million people use Google Translate, and the number of language pairs offered is increasing every year [
21]. MT based on artificial intelligence has fundamentally changed the way society views multilingual communication. Despite the rapid advances in MT technology, in some languages, especially low-resource languages, the quality of MT can be poor because of low amounts of parallel data [
22,
23]. Recent research has found that public perceptions of the potential and quality of MT are inadequate [
24,
25]. Nevertheless, members of the public, possibly including children, often use MT and consider its quality to be acceptable and equivalent to human translation [
26]. Thus, the main problem is the lack of awareness of the possibilities and quality of MT as well as risks of using MT on a daily basis and in different situations.
Recently, research has also focused on the challenges of efficient incorporation of MT into teaching/learning settings rather than on preventing or forbidding to consult MT applications [
20,
27,
28,
29,
30,
31]. MT can have a significant positive impact on the way languages are taught. The researchers [
31] claims that the grammatical and lexical accuracy of MT is improving; therefore, students are increasingly using it when doing their homework tasks in a second language, which brings up certain challenges for language teachers. References [
27,
28] carried out a longitudinal study of the use of Google Translate, specifically on how it can help students learn a second language. It was found that MT could be beneficial to language learners when writing texts in their native language and then translating them into the second language as it might offer more choices in vocabulary, but it did not seem to contribute to retaining the active vocabulary over a period of time [
27,
28]. The researchers [
32] explored whether the quality of Google Translate is high enough for students to write texts in their native language, translate them with Google Translate into English, submit them to their teachers, and see whether the teachers can determine how the assignment had been carried out. The researchers [
32] also looked into the teachers’ reactions after they had been informed that the scripts were MT-generated. The teachers agreed that it might become increasingly difficult to prevent pupils learning foreign languages from using Google Translate outside of (or even within) the classroom to translate their work from their native tongue. However, capturing nuances across languages by using MT is challenging. Another significant problem that may arise is students’ motivation to learn to write (and read) in a foreign language if high-quality translations into the target language are available to them. There is little reason to expect that language learners will not take full advantage of the tools available to them in an era of rapidly growing artificial intelligence, which makes it challenging for teachers to come up with new teaching methodologies for reading and writing in a foreign language that incorporate Google Translate [
32].
Research shows that students, in their language acquisition, rely on each other and admit to each other that they use Google Translate for homework and a variety of assignments, which, according to the author, reveals a lack of confidence in their own abilities and a reluctance to approach their teachers about it in many circumstances [
33]. Moreover, in many cases, the practice of MT is even considered as cheating by language teachers [
30,
34]. Thus, it has been claimed that MT disrupts the process of foreign language education and that it is necessary to provide guidance to teachers and students on how to gradually and thoughtfully implement MT in the foreign language classroom [
35]. In addition, it is, according to research, necessary to develop a dialogue among MT software developers, educators, children, parents, and researchers. The collaboration between industry and scholars could contribute to ensuring “the quality and safety of children’s digital experiences” and making sure that children’s “rights are taken into consideration in the development of digital media products and services” [
36].
4. Results
The interviews were analysed by means of thematic content analysis. This was carried out by using NVivo software which facilitated the analysis of the full texts of interviews by means of codes (“nodes”)> On the basis of the four research questions, nodes were created, which allowed relevant information to be grouped and synthesised efficiently.
4.1. RQ1 How Do Children Use MT?
To delve into the first research question, the following sub-questions were used:
RQ 1: RQ 1a. Do you know what MT is? What MT applications/tools do you use? Why?
RQ 1b. How often do you use MT? What language combinations do you use MT for? What types of MT do you use: text-to-text, image-to-text, etc.?
RQ 1c. What devices do you use for MT?
RQ 1d. What are your most and least favourite MT applications/tools? Do you perceive MT as positive or negative?
All the respondents () said that they knew what MT was. The vast majority of the children indicated that they used Google Translate (), and some respondents also mentioned DeepL () and Google Lens (). Two respondents indicated two tools which were not MT tools: a girl (aged 13) said she used the Duolingo application and a boy (aged 16) identified Alkonas, an online English–Lithuanian dictionary, as an MT tool. This suggests that sometimes children tend to assume that all online language-learning tools are MT tools.
In terms of the most/least favourite MT tools (see
Figure 1), four respondents could not identify their favourite/least favourite MT tool. In general, Google Translate, Google Lens, and DeepL were mentioned as favourite tools. Five respondents identified Google Translate as their favourite MT tool; one respondent gave three tools as favourite ones: Google Translate, Google Lens, and DeepL. Another respondent mentioned Google Translate and Google Lens as her favourite tools. One respondent said DeepL was her favourite one “because it gives exactly what you ask for, but in another language” (F8_G16). The remaining respondents did not provide any reasoning behind their choices. This might be partly due to the fact that the participants of our study were familiar with a relatively narrow scope of MT tools. Generally, it could be stated that some responses in terms of favourite MT tools had more to do with the frequency of use rather than the respondents’ actual preference for the tool in question.
In terms of the least favourite MT tools, only one respondent indicated that “Google Translate is the absolutely worst one...because it translates in a very bad way” (F8_G16). The interviewee did not provide any arguments to support this claim.
The question about the frequency of using MT generated a variety of responses. Out of 12 respondents, only one interviewee (F2_B12) said that he never used MT and one respondent (F1_B16) did not indicate how frequently he used MT. Based on the answers of the remaining interviewees, we devised the following scales of frequency: “often”, “sometimes”, “occasionally” and “rarely”. Two children said they used MT often, without specifying a concrete number of times per day or week (F10_B17, F8_G16). One more respondent (F5_G13) also uses MT often (i.e., 2–3 times a day); three respondents (F4_B13; F7_B15, F9_B12) use MT sometimes (i.e., 2–3 times a week), while two respondents (F3_B16, F6_G12) use it occasionally (i.e., once a week). One respondent (F2_B17) uses it rarely (i.e., once in two weeks). Due to the relatively small study sample, it is difficult to see connections between the respondents’ age and the frequency of usage.
Importantly, some respondents stressed that the frequency of their use of MT depended on several aspects: for example, one interviewee (F10_B17) stressed that MT tools could be used more often if one “is struggling with or is lazy about learning the language in question”. Another boy (F2_B17) indicated that his infrequent use of MT had to do with the teacher’s attitude towards MT:
“I only use MT when doing homework; we are not allowed to use MT during lessons. If we use Google Translate in class, our German teacher deducts two points”.
(F2_B17)
Another respondent (F8_G16) suggested that the use of MT was related to the nature of school work that was required and emphasised that she had started using MT more frequently as a result of having to write essays.
As far as the types of MT are concerned, 10 respondents indicated that they used a combination of two types of MT, i.e., image-to-text and text-to-text, whereas one respondent (F2_B12) said he was only familiar with and used text-to-text type of MT. All the participants who use MT said that they used it either on their smartphone or computer.
The respondents’ use of MT involves a variety of languages and their combinations. All the respondents were native speakers of Lithuanian and translated from and/or into the following languages: English (n = 9), German (n = 4), Russian (n = 4), French (n = 2), and Italian (n = 1). One respondent (F3_16) did not indicate the language combinations for which he used MT. The following language combinations from/into which the study participants translated using MT were identified:
Foreign language ←→ native language (FL ←→ NL) (n = 4) (F1_B16, F2_B17, F8_B16, F9_B12)
Native language → foreign language (NL → FL) (n = 2) (F4_B13, F6_G12)
Foreign language → native language (FL → NL) (n = 2) (F10_B17, F5_G13)
Foreign language ←→ foreign language, foreign language ←→ native language (FL←→FL, FL ←→ NL) (n = 2) (F7_B15, F2_G15).
As can be seen, the participants most commonly use MT to translate both from and into their native language. Importantly, two participants (F7_B15, F2_G15) who used MT to translate from and into foreign languages elaborated on how and why they did that:
“[I do it] if I want the translation to be more accurate…[I translate from German into English] because in English there are so many more words, so many more options than in Lithuanian”.
(F7_B15)
“I translate from French into Lithuanian and the other way round when the task is to translate. Otherwise, I use the English–French combination.... If the text is very long, I always translate it into English because people who use Google Translate can submit their translation and that is why the result is better. I know this because I saw the Help the Community button on Google Translate, it asks you to select the languages you know and provide your translation....”.
(F2_G15)
These interviewees’ responses illustrate that they are rather experienced in using MT as they can compare how different language combinations work and what differences they generate; on this basis the participants make informed decisions in terms of which language combination should be used in what type of situations.
4.2. RQ2: What Are the Reactions of Family Members, Friends and the School to the Application of MT?
RQ2 consisted of the following sub-questions:
RQ 2a. How do children learn about MT (e.g., from family members (older siblings, parents), school, on their own (by exploring smart technologies), etc.)?
RQ 2b. How do parents perceive MT? How do teachers perceive MT? Do they perceive MT to be positive or negative?
The responses as to how the children had learned about MT varied (see
Figure 2): the respondents reported that they found out about MT from their parents (n = 3), friends (n = 2) or as a result of their own efforts (n = 3). Two respondents did not remember how they had learnt about MT. One respondent (F2_B17) said he had found out what MT was by himself, but had been taught how to use image-to-text translation by a friend. Another respondent (F10_B17) said he had learnt about MT in school, but did not specify whether the information came from a teacher or friends/classmates. Strikingly, not a single respondent explicitly reported learning about MT from teachers.
As far as the attitudes of parents towards MT are concerned, the interviewees’ responses revealed that they believed their parents to hold generally positive views toward MT. In total, six respondents commented on how their family members perceived MT: five respondents said that their parents saw it as a positive thing which, for example, MT “helps [me] to do homework and learn and understand better” (F7_B15). One respondent (F4_B13) mentioned that his parents also used MT when they needed translation. Another respondent (F5_13) reported feeling neither encouraged nor discouraged to use MT by her parents.
The responses regarding the attitudes of teachers toward MT as perceived by the respondents present a different picture. The responses were quite nuanced and could be categorised as follows:
Teachers allow the use of MT and encourage it (n = 1);
Teachers allow the use of MT, but do not encourage it (n = 5);
Teachers allow the use of MT; the teachers’ (perceived) attitude is not specified (n = 2);
Teachers do not allow the use of MT (n = 3).
Although the majority of the respondents indicated that they were allowed to use MT in classes (n = 8); strikingly, only one respondent (F5_G13) specified that two of her teachers (Russian and English) encouraged the use of MT by explicitly telling to translate unknown words using MT. Rather surprisingly, two respondents indicated that as an alternative to MT, their teachers suggested and encouraged the use of paper dictionaries (F7_B15; F8_G16). According to one of the respondents (F8_G16), this is because “they [teachers] believe one remembers better in this way” and added that teachers seemed to be less opposed towards MT if students used it to translate separate vocabulary items rather than full-fledged texts. The other respondent (F7_B15) also mentioned that the teacher provided the students with translated word lists, which, in the eyes of the respondent, made MT unnecessary and irrelevant.
As indicated above, one of the respondents (F2_B17) who pointed out that the use of MT was not allowed in class also explained that the teacher would punish students for using it by deducting two points from the final grade. Another interviewee’s (F2_G15) teacher warns her students that she “can tell when MT is used”, presumably as a deterrent to those thinking of using MT. It should be noted that both children from this family (F2) go to the same school: the school adheres to the policy of not allowing the use of MT in the classroom.
Importantly, the question did not aim to find out about the attitudes of teachers of specific subjects, but all of the children (n = 5) who provided more details regarding the question talked about their teachers of foreign languages. Therefore, it appears that MT is not considered as an option in other lessons.
As far as the attitudes of peers/friends are concerned, three children (F5_G13; F7_B15; F8_G16) answered the question and said that their friends used MT too and that it was perceived as a positive thing (F5_G13).
6. Conclusions
After completing the research on children’s (aged 12–17) attitudes towards MT applications, we have arrived at the following conclusions.
The most popular MT tool that children use is Google Translate. In some cases, children do not distinguish between MT tools and other online resources (e.g., online dictionaries). In general, the respondents of our study are familiar with a relatively narrow scope of MT tools, namely Google Translate, DeepL, and Google Lens. Due to the relatively small study sample, we were not able to establish connections between the respondents’ age and frequency of use of MT. Concerning the types of MT, the majority of respondents indicated that they use a combination of two types of MT, i.e., image-to-text and text-to-text. All the participants use either their smartphones or computers to access MT tools. They employed MT for translation from the native language (Lithuanian) and/or into the following languages: English, German, Russian, French, and Italian. Our findings indicate that the respondents seem to be experienced in applying MT in different language combinations and are aware of the differences of generated results. Based on the final output, the children in our sample make decisions in terms of which language combination should be used in what type of translations.
The respondents stated that they had found out about MT from their parents, friends, or as a result of their own efforts. No one mentioned that teachers had guided them into the intricate world of MT applications. Thus, we may conclude that the occasional use of MT by children correlates with the perceived more negative rather than positive attitudes of their teachers’ towards MT. The majority of teachers allow the use of MT, but do not encourage it, whereas some of them do not allow it at all. Moreover, some of them encourage the use of paper dictionaries as an alternative to MT. The study has revealed that MT tools are mostly used when studying foreign languages, whereas no one mentioned MT to be considered as an option necessary to obtain information for other study subjects. According to our study participants, contrary to their teachers, parents consider MT as a positive thing which they rely on themselves when in need and have shown their children how to use it.
The main reason why children use MT, as found by our study, is bound to be school-related activities. The majority of the respondents use MT for homework, written tasks, translation of texts or individual vocabulary items, presentations, and checking accuracy/for errors. The fact that MT is so widely used for homework, but not in class, can be related to the perceived negative attitudes of teachers toward application of MT tools. Additionally, some of the respondents mentioned several entertainment-related fields of MT application: e.g., while watching movies, trying to understand the meaning of song lyrics, videos or games, and when surfing the social media.
Concerning the respondents’ perceptions of reliability of MT output, we have obtained a fragmented picture. Only one respondent claimed trusting the output of MT, whereas most of the respondents said they found MT both reliable and unreliable, depending upon the situation and the language pairs involved. Some respondents trusted the translation of individual vocabulary items, but not sentences. Importantly, those respondents who do not have a good command of the language in question, are more prone to trust MT. Some respondents try translating the same item into another foreign language or looking up for a proper meaning “in a ‘real’ dictionary online”, while others choose to edit the translated text until they arrive at an accurate translation.
Author Contributions
Conceptualization, V.L., D.L. and J.M.; Methodology, V.L., D.L. and J.M.; Software, D.L.; Validation, D.L.; Formal Analysis, V.L., D.L. and J.M.; Investigation, V.L., D.L. and J.M.; Resources, V.L., J.M.; Data Curation, D.L.; Writing—Original Draft Preparation, V.L., D.L. and J.M.; Writing—Review and Editing, J.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research has received funding from the Research Council of Lithuania (LMTLT, agreement No S-MOD-21-2).
Institutional Review Board Statement
The permission to conduct the research was obtained from KTU Research Ethics Commission (No. M6-2021-06). Before starting the interview, parents and children were informed about the aim and object of research. We also provided information on how the data gathered are going to be used in our research study. The data were obtained without any possible individual identification. After the agreement of parents was obtained, the interview with the child started.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
All data are available from the corresponding author upon reasonable request.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
The following abbreviation are used in this manuscript:
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