

































English Language Teaching Educational Journal   ISSN 2621-6485 

Vol. 7, No. 3, December 2024, pp. 139-149  

        https://doi.org/10.12928/eltej.v7i3.12016        http://journal2.uad.ac.id/index.php/eltej/index         eltej@pbi.uad.ac.id  

Shaping the future of translation careers: Student interest and 

the need for curriculum reform in the AI era 

Eko Setyo Humanika a, 1, R. Yohanes Radjaban b, 2,* 

a, b Universitas Teknologi Yogyakarta, Jl. Siliwangi no 1, Jombor, Mlati, Sleman, Yogyakarta, Indonesia 
1 eko.humanika@uty.ac.id;2 ry.radjaban@uty.ac.id*  

* corresponding author   

  

A R T I C L E  I N F O 

 

A B ST R ACT   

 

 

Article history 

Received 28 November 2024 

Revised 16 December 2024 

Accepted 21 December 2024 

 This study aims to analyze the students’s interest in translation career 
in the artificial intelligence (AI) era and the growing need for 
translation curriculum reform. It is observed that the students’ interest 
in translation carrer fluctuate due to the AI’s advancement, especially 
in its application to machine translation. Being aware of this trend will 
give valuable insight to the translation education reform.   A 
quantitative-qualitative mixed method approach was employed. This 
study involves 45 students from the English Literature Department at 
the University of Technology Yogyakarta (UTY) during the 2023-
2024 academic year. Participants were selected using a stratified 
random sampling technique and included fourth-year students (who 
have taken 4 translation subjects in the curriculum), third-year (two 
subjects), and second-year (not yet taken any). Data collection was 
conducted through questionnaires and semi-structured interviews. The 
questionnaire assessed students’ interest in translation careers and their 
view on translation instruction, while the interview provided deeper 
insight on those matters from six respondents representing the positive 
and negative view.  The findings showed that 26,6% of respondents 
are interested in a translation career, 55,6% are neutral, and 15,53% are 
not interested. It indicated that only less than one-third of the 
respondents were interested in a translation career. Most of them were 
students from higher level. It is also found that more than a half of the 
respondents were undecisive; most of them were from the second and 
third-year. It is typical because students from fourth-year tend to focus 
more on career planning. The study also highlights the need to reform 
translation curricula by integrating machine translation into classroom 
instruction, as a computer assisted as well as automatic. The findings 
of this study suggest the need for further research on developing an AI-
based model for teaching translation.  

 

© The Author(s) 2024. Published by Universitas Ahmad Dahlan  
This is an open access article under the CC–BY-SA license. 

    

 

 
Keywords 

Translation career 

Students’ interest 

Artificial intelligence 

Machine translation 

Curriculum 

 

 

 

 

How to Cite: Humanika, E. S. and Radjaban, R, Y. (2024). Shaping the future of translation careers: Student 
interest and the need for curriculum reform in the AI Era. English Language Teaching Educational Journal, 
7(3), 139-149. https://doi.org/10.12928/eltej.v7i3.12016       

1. Introduction  

Translation is often oversimplified as a simple task of converting a message from one language 
(the source language) to another (the target language), which anyone fluent in both languages can 
handle. However, many individual start translating confidently, only to realize that producing an 
appropriate final product is more challenging than expected.  As the saying goes, translation is like 
chopping an onion: at first, it seems manageable, but before long, the difficulties become apparent. 

https://doi.org/10.12928/eltej.v7i3.12016
http://journal2.uad.ac.id/index.php/eltej/index
mailto:eltej@pbi.uad.ac.id
mailto:eko.humanika@uty.ac.id
mailto:ry.radjaban@uty.ac.id
http://creativecommons.org/licenses/by-sa/4.0/
https://doi.org/10.12928/eltej.v7i3.12016
http://creativecommons.org/licenses/by-sa/4.0/
http://crossmark.crossref.org/dialog/?doi=10.12928/eltej.v7i3.12016&domain=pdf


140 English Language Teaching Educational Journal   ISSN 2621-6485 

 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

To translate effectively, one must possess what is known as 'translation competence,' which 
consists of several sub-competences. Albir et al. (2019), citing the PACTE group, identified five 
essential sub-competences: bilingual, extra-linguistic, translation-specific knowledge, instrumental, 
and strategic. Mastery of these elements is essential to ensure that the final product is accessible and 
accurate for the target audience. In other words, a translator must not only be proficient in both the 
source and target languages but also possess an understanding of the subject matter, knowledge of 
appropriate translation techniques, and cultural sensitivity. 

Translation, as a form of cross-linguistic communication, has a long history. From the creation of 
Rosetta Stone in 196 BC to the dissemination of religious texts during the medieval period and through 
the Renaissance to the colonial era, when translation flourished due to global exploration, the field has 
evolved significantly. And in the 20th and 21st centuries, technological advancements have reshaped 
translation practices (O’Keeffe, 2023; Rokan, 2021).  

The history of translation as a profession is also rich and multifaceted. As explored in Lange, et al. 
(2024), this profession has existed since ancient civilization, with early examples of evidence found 
in Assyria, Egypt, Israel, China, Greece, and Rome. In this period, translators mostly worked on 
religious or administrative texts, indicating the importance of translation in facilitating communication 
across cultures and languages. In the Medieval period, the translator played a crucial role in 
transmitting knowledge, particularly in the context of the Islamic Golden Age, where scholars 
translated Greek philosophical and scientific texts into Arabic. In the Renaissance era, this profession 
gained recognition and was acknowledged for its contribution to literature and scholarships. During 
the Enlightenment era, translators played a valuable role in spreading ideas and facilitating cultural 
exchange.  

The professionalization of translation began in the early 20th century with the establishment of 
international organizations like the International Association of Conference Interpreters (AIIC) and 
the rising demand for interpreters in global institutions such as the United Nations. In Indonesia, the 
translation profession gained official recognition in 2014 when the Indonesian Minister of 
Administrative and Bureaucratic Reform issued Regulation Number 49 on Translator's Functional 
Position in Government Bodies. This regulation delineated four professional levels: junior expert, 
associate expert, senior expert, and principal expert, providing a clear career pathway for translators 
within governmental departments. 

The latest progress has shown that technology has a significant role in translation. With technology, 
translation methods have transformed drastically, moving from conventional manual operations 
counting on translators’ expertise to more technology-assisted and automated methods. Technological 
innovations have had a notable impact on the translation industry. One of the most significant 
developments in translation technology is the integration of artificial intelligence (AI), particularly 
through deep learning and neural techniques in machine translation (MT). This integration has 
substantially improved translation accuracy and efficiency (Mohamed, 2024). It offers faster, more 
accessible, and affordable solutions for language conversion. These tools can process large volumes 
of text within seconds, offering real-time translation in multiple languages.  

In more detail, Steigerwald et al. (2022) and Datta et al. (2023) highlight the advantages of AI-
based machine translation, as follows. 

1. Neural systems employ deep learning techniques, which involve multiple processing layers to 
analyze and generate language. This approach has significantly improved the fluency and 
accuracy of the translation. 

2. The systems use a data-driven approach that utilizes a vast amount of previously translated 
texts to train AI models. These models learn patterns and relationships in language, allowing 
them to generate contextually relevant and grammatically correct translations. 

3. These systems can continuously improve as they are exposed to more data. By incorporating 
user feedback and new translations, these systems can adapt and refine their outputs over time, 
making them more effective for specialized fields, including scientific literature. 

4. They can be trained specifically in scientific jargon and terminology, which is crucial for 
accurately translating texts in specialized fields. This requires access to high-quality, domain-
specific training data. 



ISSN 2621-6485 English Language Teaching Educational Journal 141 
 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

5. The flexibility, performance, and ability of neural machine translation (NMT) systems to 
handle complex linguistic features, including in low-resource languages, further highlight their 
advantages. 

6. Many AI-powered translation tools, such as Google Translate and DeepL, are designed to be 
user-friendly and accessible, allowing researchers and the general public to utilize them easily 
for translation tasks. 

Seeing the impressive advancement of AI-based machine translation, a concern emerges on 
whether MT will replace human translators in the future, just as the impact of robots taking over 
numerous jobs in manufacturing industries. AI’s ability to translate quickly, accurately, and without 
fatigue has raised questions about whether human translators are still needed. Indeed, AI systems are 
becoming increasingly proficient, and in certain contexts, they may outperform human translators. 
With these facts, the above concern is reasonable. 

However, despite advancements in machine translations, machine translations have limitations in 
some ways (Chen, 2024). First, the complexity of natural languages—with their irregularities, 
ambiguities, and rich expressive potential—poses a significant challenge for AI. Capturing the full 
range of linguistic nuances, subtleties, and contextual meaning remains difficult for current machine 
translation systems. Second, AI lacks a true understanding of context and culture, which are essential 
for accurately interpreting implicit meanings and cultural nuances. As a result, machine-generated 
translations may suffer from misinterpretations or a loss of meaning. Third, while AI systems can 
generate translations, human involvement is still necessary for pre- and post-editing to ensure accuracy 
and quality. Thus, current machine translation technology cannot fully replace human translators. The 
most effective approach appears to be the integration of machine translation tools that assist, rather 
than replace, human translators. 

Research has shown that combining AI with human intelligence can significantly improve 
translation quality. Herbig et al. (2019) argue that AI-based MT offers a strong starting draft as its 
output, enabling translators to concentrate on refining the draft. This initial AI-generated draft is 
generally of high quality, which allows human translators to focus on enhancing clarity, flow, and 
specific nuances. They also note that AI can lessen the cognitive demands on translators, allowing 
them to concentrate on more complex aspects like context and nuance that are crucial for top-quality 
translations. Hiebl and Grosmann (2023) further support this, highlighting that while AI boosts speed 
and efficiency, human translators bring essential skills, such as contextual insight, cultural awareness, 
and subjective judgment, which are invaluable for maintaining translation quality. 

Given these developments, AI's implication in classroom translation instruction is inevitable. 
Yuxiu (2024) confirmed that translation technology can enhance translation education by (1) serving 
as an effective teaching tool that offers students a more efficient, accurate, and practical translation 
experience, (2) providing clear advantages and new opportunities for translation teaching, and (3) 
improving translation efficiency, thus improving translation teaching quality and standards. 
Furthermore, Kanglang and Fazzal (2021) suggested that educational institutions consider the 
implications of AI in translation instruction to ensure that human translators continue developing their 
skills and maintaining relevance in a changing industry. And, Caukin et al. (2024) reinforce the idea 
that AI can create a more inclusive, adaptable, and engaging learning environment for translation 
students. 

Recent development has convinced that in an era where artificial intelligence is reshaping 
industries, integrating AI into translation training is no longer optional—it is imperative. By 
equipping future translators with the skills to navigate AI-driven tools, we ensure their 
relevance and adaptability in a rapidly evolving field. 

This research contributes to translation studies by exploring students' interest in translation careers 
and the need for curriculum reform driven by the rapid advancements in artificial intelligence. It 
provides valuable insights for curriculum developers to align translation programs with the current 
realities of the translation industry. 



142 English Language Teaching Educational Journal   ISSN 2621-6485 

 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

2. Method 

2.1. Participant 

This study included 45 students from the English Literature Department at UTY, representing 
second-, third-, and fourth-year students from the 2023/2024 academic year. Fourth-year students have 
completed four translation courses as part of their curriculum, third-year students have completed two, 
while second-year students have not taken any. A stratified random sampling method was used to 
select participants, ensuring representation from three consecutive academic batches. 

2.2. Data Collecting Technique 

Data for this research were gathered using structured questionnaires and semi-structured 
interviews. The questionnaire contained 14 items, to reveal (1) the respondents’ experiences with AI-
based machine translation, (2) their interest in the translation profession in light of MT advancements 
(3) their confidence in choosing translation as one of their future careers, and (4) the need for 
instructional materials on translation topics within the curriculum. 

Respondents were asked to indicate their level of agreement with each statement using a Likert 
scale, ranging from ‘Strongly disagree’ to ‘Strongly agree’. In assessing the questionnaire's internal 
consistency, Cronbach's Alpha was calculated to evaluate the reliability of responses across items. 
The overall Cronbach's Alpha coefficient was 0.82, indicating high reliability, as values above 0.7 are 
generally accepted as reliable for psychological and social science measures (George & Mallery, 
2021). This level of reliability suggests that the items consistently measure the intended constructs 
across respondents. For construct validity, item-total correlations were analyzed to determine the 
degree to which each item was aligned with the overall scale score. Items with correlations above 0.4 
generally contribute adequately to construct validity (Field, 2024). Most items demonstrated 
correlations above this threshold, confirming that the questionnaire items effectively measure the 
constructs related to machine translation (MT) perception and interest in translation as a career. 

Semi-structured interviews were conducted with 6 students, representing a mix of positive and 
negative views from their respective batches. These interviews aimed to gain deeper insights into 
students' career interests in translation and their perspectives on translation instruction within the 
curriculum. 

2.3. Data Analysis 

1) Questionnaire data 

Descriptive statistics were used to analyze the questionnaire data, providing an overview of the 
respondents' views on the topics of interest. Inferential statistics were applied to explore potential 
differences in responses based on academic year, treating academic year as a variable due to the 
evolving nature of career choices over time. 

2) Interview data 

The interview data were analyzed thematically, following a six-step process outlined by Byrne, D. 
A. (2022): becoming familiar with the data, generating initial codes, identifying themes, reviewing 
those themes, defining and naming themes, and producing the final report. 

Ethical approval was secured from the appropriate institutional review board. Informed consent 
was obtained from all participants, who were assured of the confidentiality and anonymity of their 
responses. They were also made aware of their right to withdraw from the study at any time without 
facing any consequences. 

3. Findings and Discussion 

3.1. Findings 

The analysis of the questionnaire data indicates that 87 % of this study's respondents are familiar 
with translation machines, 79.5% reported regularly using the tools to carry out translation tasks, and 
82.5% confirm their belief that artificial intelligence-based machine translation (AI-based MT) is a 
potential tool to assist them in doing their translation. These results suggest a generally positive 
outlook toward AI-based MT, with 81% of respondents expressing favorable views on the subject. 



ISSN 2621-6485 English Language Teaching Educational Journal 143 
 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

Further analysis of the questionnaire responses indicates that students are particularly drawn to AI-
based MT due to its ability to translate quickly and efficiently, reduce repetitive tasks, and demonstrate 
improved accuracy. These factors appear to enhance their career interest in the translation field. 

The diagram below illustrates the data on students' interest in pursuing translation careers based 
on their academic batch. 

 

Fig. 1. Students’ interest in translation career by batch 

The quantitative data in the diagram above show that in batch 2021/2022, the ‘Strongly disagree’ 
and ‘Disagree’ responses to the statement stood at 20%. This indicates that one-fifth of respondents 
had a negative perspective on the statement presented in the survey. The ‘Neutral’ category comprised 
40% of the responses, suggesting that almost half of the respondents neither agreed nor disagreed, 
indicating indecision or uncertainty. 

Meanwhile, the positive response to the statement comprised 40%, with 13.3% of participants 
expressing agreement and 26.7% strongly agreeing. This suggests a more moderate level of positive 
sentiment toward the statement, as only a third of the respondents expressed a favorable opinion.  

In the 2022/2023 batch, there is a little difference in sentiment compared to the previous batch. 
The combined ‘Strongly disagree and ‘Disagree’ answers decreased to 13.3%, which indicated a 
reduction in the number of respondents expressing negative opinions. This suggests that fewer 
participants disagreed with the statement, indicating a potential decline in opposition. However, the 
‘Neutral’ category notably increased, from 40% to 60%. This increased neutrality indicates a growing 
sense of indecisiveness or uncertainty among the respondents. The combined ‘Agree’ group remained 
at 26.6%, with equal distributions of 13.3% expressing agreement and strong agreement. This decrease 
from the previous year implies a little decline in overall positive sentiment towards the statement. 

In the most recent batch, 2023/2024, the data reveals interesting changes. The combined ‘Strongly 
disagree’ and ‘Disagree’ groups remained stabil at 13.3%, indicating a continued lack of significant 
opposition. However, the proportion of neutral responses increased dramatically to 66.7%, making it 
the dominant sentiment among the respondents. This significant increase in neutrality indicates a 
notable change towards indecision or uncertainty. 

The combined ‘Strongly agree’ and ‘Agree’ group experienced a decline to 20%, with 13.3% of 
respondents expressing agreement and 6.7% strongly agreeing. The reduction in strong agreement 
reflects a decrease in the intensity of positive sentiment, while the overall level of agreement also 
decreases. 

The qualitative data from the interviews revealed several interesting findings. Respondents who 
agreed noted that their experience with AI-based MT provided them with valuable insights into the 
translation process. It allowed them to concentrate on the fundamental challenges of translation while 
leaving technical matters to the machine. This enabled them to finish their translation tasks more 
quickly and with fewer or no minor errors like mistyping and misspellings.  

However, these respondents also believe MT cannot fully replace human translators. They noted 
that while MT performs well in academic and technical contexts, it struggles with cultural and literary 
translations. One respondent mentioned, "In our Literary Translation course, we found that machine 
translation fails to capture the nuances from the source language into the target language." Another 
respondent confirms: “One example is when machine translation is asked to translate Berakit-rakit ke 
hulu, berenang-renang ke tepian. Bersakit-sakit dahulu, bersenang-senang kemudian. The translation 
is very literal. The meaning has been transformed but not the rhyme. This reflects a belief that human 
translators are still essential for texts requiring a high degree of cultural and literary sensitivity. 



144 English Language Teaching Educational Journal   ISSN 2621-6485 

 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

These respondents also highlight an urgent need to integrate MT into translation sessions within 
the classroom. Currently, most translation activities follow a traditional approach, primarily focusing 
on text analysis, transfer, and restructuring. In this conventional procedure, students begin their 
translating by analyzing the source text to understand its core message. They then transfer this message 
by identifying equivalent expressions and restructuring the text according to the target language's 
grammatical rules and contextual nuances. 

As technology has provided the instrument to help translators do their job, this study's respondents 
recommend using machine translation in classroom sessions. It can be in the form of a Computer-
Assisted Translation (CAT) tool or even automatic translation.  They know that professional 
translators have used MT to do the job. Keeping students away from MT in the classroom activity 
means keeping them in distance from the real world of the translation industry. 

A different argument emerges from the respondents who disagree with the statement in the 
question. These disagreeing respondents foreshadow the future threat of machine translation for 
humans. One of the respondents claims:” The quality of Google Translate improves significantly from 
time to time. It used to produce bad translations, but now it has performed impressively in translating 
text. In many cases, human interference to revise or edit the translation is unnecessary. Google 
Translate is more than enough for the readers who only want to know the message.” Another 
respondent quoted a machine translation vendor who claimed that their product could achieve 90% 
translation accuracy, suggesting that the remaining 10% improvement is merely a matter of time 

With these considerations, they do not choose translation as one of their career options in the future. 
They believe that the number of job opportunities in the translation field in the future will decrease 
significantly because clients can already account for machine translation. They do not want to take 
the risk. 

Despite different views on translation careers, respondents generally agree on the value of 
integrating MT into classroom translation sessions. They see the potential use of machine translation 
as a CAT tool and a form of automatic translation. By familiarizing them with these tools, the 
classroom could better prepare them for the realities of the translation industry, where proficiency 
with technology is increasingly essential. 

 

3.2. Discussion 

The data analyzed in this study shows that 79.5% of respondents are familiar with MT and often 
use it for translation assignments. This finding is in line with recent studies and surveys. Hellmich and 
Vinall (2023) claim that MT tools are commonly used by students learning foreign languages, with 
around 70% reporting frequent use of services like Google Translate. Bindels and Pluymaekers (2022) 
report in their study conducted with first-, second-, and third-year translation undergraduate students 
in the Netherlands that 65% of their respondents use MT in their tasks. In France. Loock and 
Lèchhauguette (2021) conducted a survey of 89 students enrolled in a translation course at the 
University of Lille and found that 83% of them used online MT tools for their homework assignments.  

Almost a similar phenomenon is seen in Spain. A survey conducted by Pastor (2021) to the students 
of the Translation Program of the University de València identified that 72% of her respondents 
reported resorting to MT as a problem-solving tool when faced with text fragments containing 
especially difficult sentences or complex syntactic structures. It is also reported that 100% of her 
respondents believe that MT can help them to translate under certain conditions.  In Turkey, Çakir and 
Bahyan (2021) reported in their study to English Language and Literature students at a state university 
in Turkey that 87% of their respondents use MT (53% every day and 34 % sometimes). Another 
review by Lee (2023) highlights that MT has become an integral part of foreign language education, 
with usage rates increasing post-pandemic due to greater reliance on digital resources in remote 
learning environments. Students prefer using MT for its accessibility and immediate feedback, 
particularly for writing, translating complex texts, and facilitating understanding in a second language. 

These findings confirm the widespread use and integration of MT tools among students, with many 
relying on them for their translation tasks. The respondents in this study also recognized the 
advantages of MT—such as speed, efficiency, and reduced monotony—which have sparked their 
interest in translation as a career. Many students highlighted that MT’s ability to enhance translation 



ISSN 2621-6485 English Language Teaching Educational Journal 145 
 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

accuracy and productivity has positively impacted their view of the profession, suggesting that the 
convenience offered by MT tools is contributing to their career interest. 

Research that explored whether the convenience and advantages of MT encourage students to 
pursue careers in translation indicates mixed impacts, as MT introduces both benefits and challenges 
in the field. In his study to 35 second-year students of Taras Shevchenko National University, Ukraine, 
Bakhov et al. (2024) found that using AI-assisted tools in translation education can significantly 
improve students' academic performance and translation quality. This enhancement in their skills 
increases their confidence and interest in pursuing a career in translation. 

Other studies explore the positive response to the issue. Tian et al. (2023), in their study of 108 
grade 2021 students selected from three Chinese universities, highlighted that future work self-
elaboration positively relates to students' exhibition of translation technology. In line with the above 
studies, García-Escribano and Díaz-Cintas (2023) observed that integrating MT in audiovisual 
translation training has sparked interest in post-editing careers. 

However, when considering career options, data in this study disclose that most respondents are 
generally neutral about pursuing a career as a translator. As seen in Figure 1, 55.56% of the 
respondents expressed no strong opinion, 28.86% showed interest, and 15.53% opposed the idea. The 
data also indicates that uncertainty is higher among students from Batch 2022/2023 (60%) and 
2023/2024 (66.7%) compared to the more decisive students in Batch 2021/2022 (40%). Additionally, 
Batch 2021/2022 students show more career clarity, with 60% already set on their career options, 
compared to 33.3% in Batch 2023/2024. Such determination is typical, as students in higher semesters 
often become more focused on specific career decisions than those in lower. 

The data also reflect broader trends observed in translation programs worldwide. Hao and Pym 
(2022) reported that only around one-third of translation program graduates pursue work as translators 
or interpreters. Various studies illustrate this trend with specific figures: for example, among Swansea 
University graduates in 2020 (sample of 29), 34.48% were employed in translation services. An EMT 
survey with 1,138 graduates found 49.03% working as translators or interpreters, while a study by 
Schmitt, Gernstmeyer, and Müller reported that 67.29% of 1,278 graduates held translation or 
interpreting positions. Meanwhile, in Indonesia, Husein and Bahar (2020) conducted a survey with 45 
students from the University of Muhammadiyah Gorontalo, which revealed that respondents had a 
moderate interest in pursuing careers as translators. 

As indicated in the findings, respondents of this study see the profession from two perspectives. 
The agreeing group perceives it with optimism. They see new possible opportunities in the emergence 
of AI-based MT. Meanwhile, respondents confirming disagreement stated their concern about MT’s 
threat to the human role in translating jobs. They are concerned that job opportunities will significantly 
decline due to the advancements in machine translation. 

The results of this study align with those of Kirov and Malamin (2020), who found that translators 
generally perceive the impact of AI on their profession with a mix of concern and optimism. While 
many see AI as a potential threat, they also recognize that these technologies can help reduce repetitive 
tasks, allowing them to focus on editing machine-translated text and provide a new opportunity: 
machine translation post-editing (MTPE). 

Responding to advancements in MT, researchers have long worked to incorporate MT into 
classroom translation instruction. This integration began in the 1960s but gained significant 
momentum in the 2010s due to rapid advancements in neural machine translation (NMT) 
technologies. This approach utilizes multiple processing layers to examine and produce language, that 
significantly enhance translation fluency and precision.  In 2023, MT was incorporated into legal 
translation courses to enable students to gain practical skills, MTPE. This evolution highlights how 
MT has changed from a simple automation tool to a vital element in translation education, equipping 
students with essential skills for a technology-driven industry (Steigerwald et al., 2022; Cuenca, et al, 
2020; Cunha, 2023). 

Several approaches have been proposed for integrating MT into translation courses. For instance, 
Bulut (2019) suggested a six-week instructional scenario that combines human and machine 
translations. Students were asked to translate texts both manually and with the aid of MT, followed 
by a comparative analysis of their human translations versus the MT output. This approach allows 



146 English Language Teaching Educational Journal   ISSN 2621-6485 

 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

students to critically assess the strengths and weaknesses of MT, particularly in terms of context and 
cultural sensitivity and enhances their understanding of translation accuracy. 

Another approach involves a series of automated translation activities, such as pre-editing source 
texts, providing prompts, and post-editing machine-generated output. Pre-editing involves modifying 
the source text to improve the quality of the MT output, including simplifying complex sentences and 
ensuring consistency in terminology (Miyata & Fujita, 2021). Prompts, which provide specific 
instructions to MT systems, are another tool that can refine the translation process. For example, 
specifying "rhyme" in the prompt when translating an Indonesian proverb results in a more creative 
and contextually appropriate translation. This highlights the importance of training students in the 
strategic use of MT prompts (White et al., 2023). 

Post-editing is another key skill that translation students must develop. It involves revising and 
improving the MT output to ensure accuracy, coherence, and fluency. The post-editing process can be 
divided into several steps: initial assessment, error correction, fluency and coherence improvements, 
and final quality assurance (Shin & Chon, 2023). Teaching students these post-editing skills is crucial 
to preparing them for the demands of the professional translation industry, where MT is increasingly 
used in combination with human expertise. 

Finally, Pastor (2021) proposed a comprehensive approach for advanced translation students, 
which includes practicing with different MT engines, analyzing MT errors, and engaging in post-
editing tasks. This also involves evaluating MT outputs both manually and automatically, as well as 
exploring the professional implications of MT in the translation field. 

4. Conclusion 

The emergence of AI has significantly reshaped students' interest in translation careers. This study 
confirms that the influence could be twofold. On one hand, some students view AI as a tool that can 
enhance their translation skills and efficiency rather than seeing it as a threat. This technological 
advancement opens up new career paths within translation. On the other hand, some other students 
are concerned that automation might replace traditional translation jobs. The fear is that machine 
translations could diminish the demand for human translators, leading to fewer job opportunities in 
the field.  

However, despite these concerns, the limitations of AI remain evident—particularly in its inability 
to fully capture the nuances of culturally rich texts, idiomatic expressions, and literary works. As 
students become more aware of these shortcomings, they are beginning to recognize the irreplaceable 
role of human translators. Human expertise, particularly in cultural sensitivity, emotional depth, and 
creative interpretation, remains essential for high-quality translations. These insights reinforce the 
enduring relevance of human translators in a profession increasingly influenced by technology. 

Looking ahead, the future of translation careers lies in the integration of AI and human expertise. 
By combining the strengths of both, the translation process can be enhanced, producing high-quality 
outputs that improve the speed and efficiency of MT while preserving the essential qualities that only 
human translators can provide. The next step, therefore, is to incorporate AI tools into translation 
training programs in universities, ensuring that students are equipped to navigate the evolving 
landscape of the profession. 

Future research should focus on developing AI-based teaching models for translation that 
effectively integrate machine translation tools. These models could provide students with practical 
skills in using MT alongside human judgment, preparing them for a technology-driven translation 
industry where proficiency in both domains is essential. 

 

Acknowledgment 

We express our gratitude to the Rector of the University of Technology Yogyakarta, LPPM, and the 
Head of the English Literature Department for their support and for providing research facilities. We 
also sincerely thank the respondents for their participation and cooperation during the data collection 
process. 

 



ISSN 2621-6485 English Language Teaching Educational Journal 147 
 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

Declarations 

Author contribution : Authors 1 and 2 collaboratively conducted the research, including 
proposing the topic, drafting the proposal, applying research 
methodologies, performing analysis, and presenting the data and 
discussion. Author 1 took the lead in the article’s publication process 
and both contributed to the publication fee. 

Funding statement : No funding is available for this research. 

Conflict of interest : I declare that there is no competing interests. 

Ethics Declaration : As the author, I confirm that this work has been written based on 
ethical research principles in compliance with our university’s 
regulations and that the necessary permission was obtained from the 
relevant institution during data collection. I fully support ELTEJ’s 
commitment to upholding high standards of professional conduct and 
practicing honesty in all academic and professional activities.   

Additional information : No additional information is available for this paper. 

 

 
 

 
REFERENCES  

Albir, A. Galan-Manas, A. Kuznik, A. Neunzig, W. Olalla-Soler, C. Rodriguez-Ines, P. & Romero, 

L. (2019). Evolution of the efficacy of the translation process in translation competence 

acquisition. Meta Translators’ Journal. 64(1), 219-265. Montreal: University of Montreal 

Press. https://doi.org/10.7202/1065336ar  

Bakhov, I. Bilous, N. Saiko, M. Isaienko, S. Hurinchuk, S. & Nozhovnik, O (2024). Beyond the 

dictionary: Redefining translation education with artificial intelligence-assisted app design and 

training. International Journal of Learning, Teaching and Educational Research. 23(4) 118-

140 . Mauritius:  Society for Research and Knowledge Management 

https://doi.org/10.26803/ijlter.23.4.7  

Bindels, J. & Pluymaekers, M (2022). The use of machine translation by undergraduate translation 

students for different learning tasks. Journal for Data Mining and Digital Humanities France: 

Nicolas Turenne https://doi.org/10.46298/jdmdh,9019 

Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic 

analysis. Quality & quantity, 56(3), 1391-1412. New York: Springer. 

https://doi.org/10.1007/s11135-021-01182-y  

Çakir, I. & Bahyan, S (2021). The effect of machine translation on translation classes at the tertiary 

level. Journal of Narrative and Language Studies 9(16) 122-134. Turkey: Karadenis Technical 

University 

Caukin, S. Trail, L. Vinson, L. & Wright, C (2024). Tech talk entering a new frontier: AI in 

education. International Journal of the Whole Child. 8(2) 47-55. New Jersey: Wiley-Blackwell 

https://www.researchgate.net/publication/377556990 

Chen, M. (2023). Trust, understanding, and machine translation: the task of translation and the 

responsibility of the translator. AI & SOCIETY. Vol. 39 pp 2307-2319. New York: Springer 

Science+Business Media. https://doi.org/10.1007/s00147-023-01681-6 

Cunha, S. (2023). MT and legal translation: application in training. Proceedings of Machine 

Translation Summit XIX, Vol. 2: Users Track, 11–23, Macau SAR, China. Asia-Pacific 

Association for Machine Translation. https://aclanthology.org/2023.mtsummit-users.2  

 

https://doi.org/10.7202/1065336ar
https://doi.org/10.26803/ijlter.23.4.7
https://doi.org/10.46298/jdmdh,9019
https://doi.org/10.1007/s11135-021-01182-y
https://www.researchgate.net/publication/377556990
https://doi.org/10.1007/s00147-023-01681-6
https://aclanthology.org/2023.mtsummit-users.2


148 English Language Teaching Educational Journal   ISSN 2621-6485 

 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

Datta, G. Joshi, & N, Gupta, K (2023). Performance comparison of statistical vs. neural-based 

translation system on low-resource language. International Journal on Smart Sensing and 
Intelligent System 1(16). 1–13. New Zealand: Sciendo.  https://doi.org/10.2478/ijssis-2023-

0007  

Cuenca, E. Estrella, P. Bruno, L. Mutal, J. Girletti, S. Volkart, L & Bouillon. P. (2020). Re-design 

of machine translation training tool (MT3). Proceedings of the 22nd Annual Conference of the 

European Association for Machine Translation, (pp. 375–382), Lisboa: European Association 

for Machine Translation. https://aclanthology.org/2020.eamt-1.40 

Field, A. (2024). Discovering Statistics Using IBM SPSS Statistics (6th ed.). London: SAGE 

Publications. 

García-Escribano, A. & Díaz-Cintas, J (2023). Integrating post-editing into the subtitling classroom: 

what do subtitlers-to-be think? Linguistica Antverpiensia, New Series: Themes in Translation 

Studies, 22. pp. 115-137. Belgium: University of Antwerp 

George, D., & Mallery, P. (2021). IBM SPSS Statistics 27 step by step: A simple guide and reference 

(17th ed.). New York: Routledge. 

Hao, Y. & Pym, A. (2022). Where do translation students go? A study of the employment and 

mobility of Master graduates. The interpreter and translator trainer. 17(9). 1-19. United 

Kingdom: Taylor & Francis https://doi.org/10.1080/1750399X.2022.2084595 

Hellmich, E.  & Vinall, K. (2023). Student use and instructor beliefs: Machine translation in 

language education. Language Learning & Technology, 27(1), 1–27. Manoa: ScholarSpace. 

https://hdl.handle.net/10125/73525  

Herbig, N. Pal, S. Van Genabith, J & Krṻger, A (2019). Integrating artificial and human intelligence 

for efficient translation. arxiv.org/abs/1903.02978v1. New York: Cornell University  

https://doi.org/10.48550/arXiv.1903.02978 

Hiebl, B. & Gromann, D, (2023). Quality in human and machine translation: Interdisciplinary survei. 

Proceeding of the 24th Annual Conferencesof the European Association for Machine 

Translation, pages 375–384, Tampere, Finland. European Association for Machine 

Translation. https://aclanthology.org/2023.eamt-1.37.pdf 

Husein, D. Bahar (2020). English major students’ self-concept perspective on viewing translator as 

a profession. New Language Dimension I(2) 49-54. Surabaya: Universitas Negeri Surabaya. 

Kanglang, L. & Fazaal, M (2021). Artificial intelligence and translation teaching: A critical 

perspective on the transformation of education. International Journal of Education Science 

33(1-3), 64-73. United Kingdom: Taylor & Francis. 

Kirov, V. & Malamin, B.(2022). Are translators afraid of artificial intelligence? Societies 12(2) 1-

14. Switzerland: MDPI.  https://doi.org/10.3390/soc12020070  

Lange, A. Monticelli, D. & Rundle, C. (Eds.). (2024). The Routledge Handbook of the History of 

Translation Studies. London & New York: Routledge. 

http://dx.doi.org/10.4324/9781032690056 

Lee, S. (2023). The effectiveness of machine translation in foreign language education: A systematic 

review and meta-analysis. Computer Assisted Language Learning, 36(1-2), 103-125. United 

Kingdom: Taylor & Francis. https://doi.org/10.1080/09588221.2021.1901745 

Loock, R. & Léchauguette, S (2021). Machine translation literacy and undergraduate students in 

applied languages report on an exploratory study. Revista Tradumάtica No.19 pp. 205-225. 

Bacelona: Universitat Autònoma de Barcelona. https://doi.org/10.5565/rev/tradumatica.281 

Miyata, R & Fujita, A. (2021). Understanding pre-editing for black-box neural machine. Proceedings 

of the 16th Conference of the European Chapter of the Association for Computational 

Linguistics. Main Volume, (pp. 1539–1550), Online. Association for Computational 

Linguistics. https://doi.org/10.18653/v1/2021.eacl-main.132  

https://doi.org/10.2478/ijssis-2023-0007
https://doi.org/10.2478/ijssis-2023-0007
https://aclanthology.org/2020.eamt-1.40
https://doi.org/10.1080/1750399X.2022.2084595
https://hdl.handle.net/10125/73525
https://doi.org/10.48550/arXiv.1903.02978
https://aclanthology.org/2023.eamt-1.37.pdf
https://doi.org/10.3390/soc12020070
http://dx.doi.org/10.4324/9781032690056
https://doi.org/10.1080/09588221.2021.1901745
https://doi.org/10.5565/rev/tradumatica.281
https://doi.org/10.18653/v1/2021.eacl-main.132


ISSN 2621-6485 English Language Teaching Educational Journal 149 
 Vol. 7, No. 3, December 2024, pp. 139-149 

 Humanika, E. S. and Radjaban, R, Y. (Shaping the future of translation careers…..) 

Mohamed, Y. Khanan, A. Bashir, M. Mohamed, A. Adiel, M. & Elsadig, M. (2024). The impact of 

artificial intelligence on language translation: A review. IEEE Acces Journals Vol 12 pp: 25553 

– 25579. New York: Institute of Electrical and Electronics Engineers. 

https://doi.org/10.1109/ACCESS.2024.3366802 

O’Keeffe, B. (2023). The translation of stone. American Book Review. 44(4). 105-110. Nebraska: 

University of Nebraska Press. https://doi.org/10.1353/abr.2023.a921791  

Oner Bulut, S (2020). Integrating machine translation into translation training; Toward ‘Human 

Translator Competence’, transLogos, 2(2). 1-26. Turkey: Diye Global Communications 

http://dx.doi.org/10.29228/transLogos.11  

Pastor, D. (2021). Introducing machine translation in the translation classroom: a survey on students’ 

attitude and perception. Revista Tradumάtica No.19 pp. 48-65. Bacelona: Universitat 

Autònoma de Barcelona. http://dx.doi.org/10.5565/rev/tradumatica.273 

Rokan, K (2021). Rennaisance and the development of translation in the Arab world. Journal of 

Humanities and Education Development. 3(4) 10-13.Jaipur: Shillonga Publications Group 

http://dx.doi.org/10.22161/jhed.3.4.2  

Shin, D. & Chon, Y. (2023). Second language learner’s post-editing strategies for machine 

translation errors. Language Learning and Technology. 27(1) 1–25. Manoa: National Foreign 

Language Resource Center. https://hdl.handle.net/10125/73523 

Steigerwald, E. Ramires-Castaneda, V. Brandt, D.  Baldi, A. Saphiro, J. Bowker, L & Tarvin, R. 

(2022). Overcoming language barrier in academia: Machine translation tools and a vision for a 

multilingual future. BioScience Journal, 72(10) 989-998. Oxford: Oxford University Press. 

https://doi.org/10.1093/biosci/biac062  

Tian, S. Jia, L. & Zhang, Z. (2023). Investigating students’ attitudes towards translation technology: 

The status quo and structural relations with translation mindsets and future work self. Frontiers 
in Psychology. pp 1–16. Switzerland: Frontiers Media SA 

https://doi.org/10.3389/fpsyg.2023.1122612 

 White, J. Fu, Q. Hays, A, Sandborn, M. Olea, C. Gilbert, H. Elnashar, A. Spencer-Smith, J & 

Schmidt, D.C. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. 

cs>arXiv:2302.11382. New York: Cornell University 

https://doi.org/10.48550/arXiv.2302.11382 

Yuxiu, Y. (2024). Application of translation technology based on AI in translation teaching. System 

and Soft Computing. Vol. 6 pp.1–8. Netherland: Elsevier. 

https://doi.org/10.1016/j.sasc.2024.200072 

 

https://doi.org/10.1109/ACCESS.2024.3366802
https://doi.org/10.1353/abr.2023.a921791
http://dx.doi.org/10.29228/transLogos.11
http://dx.doi.org/10.5565/rev/tradumatica.273
http://dx.doi.org/10.22161/jhed.3.4.2
https://hdl.handle.net/10125/73523
https://doi.org/10.1093/biosci/biac062
https://doi.org/10.3389/fpsyg.2023.1122612
https://arxiv.org/list/cs/recent
https://doi.org/10.48550/arXiv.2302.11382
https://doi.org/10.1016/j.sasc.2024.200072

	1. Introduction
	2. Method
	2.1. Participant
	2.2. Data Collecting Technique
	2.3. Data Analysis
	1) Questionnaire data
	2) Interview data


	3. Findings and Discussion
	3.1. Findings
	3.2. Discussion

	4. Conclusion
	Declarations


