The Role of University Writing Centers in AI-translated
Academic Writing – The case of international graduate students
in JapanYuhong Zhu
Hiroshima University
Despite the recent interest in AI language pedagogy in the Computer-Assisted Language
Learning scene, most research studies have focused on the usefulness (and potential academic
integrity concerns) of AI-generated texts. Very little research has been done in relation to how
AI translation tools may help second language learners of English. This study explores whether
and to what extent International Graduate Students at a Japanese university are utilizing AI
translation during their English research writing process. I choose this group of students dueto their unique learning needs given the context: they are writing English research articles in a
predominantly English as a Foreign Language (EFL) environment, and will have to translate
between English and Japanese, which are often both non-native languages to them. Throughsemi-structured interviews, I have shown that all participants made use of AI translation tools
during their research writing process, and their patterns of usage varied depending on factors
such as language background, academic program, and personal goals. The study also offerssome tentative suggestions similar writing centers may consider in order to achieve morecomprehensive services for both International Graduate Students and research writing for
publication purposes.
1. Introduction
Scholars who are non-native speakers of English face a common dilemma when it comesto getting their voices heard and exchanging ideas in the academic world. On the one hand,publishing in English international journals is often associated with more prestige and exposure(Bennett 2013), but requires a high command of academic English. One might have to thinkand draft in their native language, and attempt translating their ideas into English at a laterstage. On the other hand, writing academically in the researcher’s L1 may alleviate the stressassociated with language, while introducing a “reverse translation” task – some English terms
in the field, especially newly-coined ones, have to be translated into the native language of theresearchers. Writing and translation are inseparable for researchers using L2 English.
Several studies have investigated the use of Machine Translation (MT) and the extent it can
assist English academic writing in a second/foreign language context (Groves and Mundt 2015;
Escartín et al. 2017; Ducar and Schocket 2018, among others). The general consensus is that
although Machine Translation systems such as Google Translate may aid ESL/EFL writers(both students and academics) to a certain extent, professional translating and proofreading
assistance is still necessary while teachers and administrators need to mind potential academic
integrity (i.e. plagiarism) issues with regard to MT.
Since the public launch of ChatGPT in late 2022, there has been more interest than ever inEnglish pedagogical research using generative AI and/or Large Language Models (LLMs).
Virtually all journals related to academic writing education and assessment have either
witnessed a surge in research articles focusing on AI and L2 writing education, or allocatedspecial issues and sections on these topics1. In this context, the majority of the literature isconcerned with AI-generated texts and their implications for both education and academic
integrity. Far less attention has been directed towards translations done by generative AI
models and ways to incorporate these more powerful translation tools into academic writing
education (see also §2.1).
This paper aims to fill this gap as I explore the usefulness of AI translation services to aid
English academic writing, from the perspective of International Graduate Students (IGS) in
Japan. IGS comprise a unique student population in Japanese universities, who conduct theirresearch and publish mainly in English yet receive rather limited support for their research
writing needs (§2.2). By presenting the qualitative data from four interviews with international
doctoral students at Hiroshima University, I also intend to share some reflection on how English
educators and university writing centers may facilitate the writing and translation processagainst the backdrop of AI and L2 learning.
The paper is structured as follows: §2 summarizes two strands of research pertinent to thecurrent study – firstly, the increasing scholarship concerning generative AI and English as a
Second/Foreign Language (ESL/EFL) teaching through the use of Machine Translation (MT)tools. The context of the study, a writing center in a highly internationalized Japanese university,
will also be introduced in this section. The methods are discussed in §3, followed by the resultsin §4. I discuss some remaining issues and future directions in the concluding section §5.
2. Background
2.1 Recent scholarship on AI, Machine Translation and Computer-Assisted Language
Learning
Worries about how technological advances may negatively impact language learning havecoexisted with innovative teaching methods incorporating various technological tools even
before generative AI / LLMs gained public attention. Machine Translation (MT) has been
demonstrated to be effective in aiding beginner writers (Garcia and Pena 2011), genre-specificacademic writing (Escartín et al. 2017), and revising and postediting (Lee 2020; see references
cited therein) in second language classrooms. Nevertheless, there have also been studies thatare skeptical towards the extent MT can really help second language writers. Costa-jussà,
Farrús, and Pons (2012) have found that the accuracy of MT in medical writing varies greatly
depending on the source/target languages. Groves and Mundt (2015) argue that while tools
such as Google Translate may help L2 writers at a lexico-grammatical level, they are lacking
when it comes to improving writing at a more holistic scale (e.g., discourse appropriateness,
context, argumentative structure). In some teaching contexts, texts from MT are even treatedas “a bad model” for students to correct and learn from (cf. Niño 2009). To this extent, early
MT tools are viewed as a learning support with limited capability, only useful in class whencertain conditions are met.
The emergence of publicly-available generative AI models such as ChatGPT has drasticallychanged the landscape of the Computer-Assisted Language Learning field. As it becomestrivially easy for L2 writers to prompt and produce acceptable English texts with the help ofgenerative AI, the focus of related research has been shifted to identifying and preventing
One example being the “disciplinary dialogues” published in the December 2023 issue of Journal of Second
Language Writing.
1

potential negative outcomes of AI in education (Cotton, Cotton, and Shipway 2023; Dakakni
and Safa 2023; Eke 2023, among others). Most of these studies take a rather critical stanceagainst the use of ChatGPT in education, evident just by reading their titles: “Chatting andCheating”, “A 21st Century Pandora’s Box”, “Threat to Academic Integrity”, and so on.
In the midst of the somewhat pessimistic atmosphere in the field, one study is of note.
Warschauer et al. (2023) summarize the affordance issues in L2 writing as three contradictions:the “imitation” contradiction, the “rich get richer” contradiction and the “with or without”
contradiction. Firstly, the introduction of AI has put text imitation, traditionally considered asa somewhat effective way of learning L2 writing, under harsher scrutiny for academic integrity
violations. As a matter of fact, Liang et al. (2023) show that the so-called “GPT-detectors” arebiased against L2 writers, falsely identifying L2 writings as AI-generated and plagiarism. The“rich get richer” contradiction concerns with the widening gap in relation to digital literacy –people who were equipped with technological expertise will also excel at using AI to aid theirwriting, furthering the “language divide” from those who rely on traditional learning methods.
Lastly, the “with or without” contradiction refers to the inequality in L2 proficiency among all
learners. A generative AI can be queried to provide useful ideas and knowledge about writing,but only if the learner knows how to ask (preferably in English) to begin with.
It is apparent that recent scholarship concerning Computer-Assisted Language Learning is
sharply delimited by the moment when students and teachers noticed the existence of ChatGPT.
As the majority of studies focus on the level of generation Large Language Models are capableof (and what it entails for language education), there is yet one area that receives surprisingly
low attention – LLMs and their contribution to Machine Translation. Thanks to the recent
development in Natural Language Processing, ESL/EFL students now have access to a rangeof tools for their translation needs: for instance, DeepL, Google Translate and chatbots(ChatGPT, Bing, etc.). Despite the equally increasing accessibility and accuracy of AI
translation, very few studies have revisited the topic of using translation tools to teach L2
writing. Below I report two recent studies in the Japanese EFL context that I am aware of.
In Birdsell (2022), the author explores the extent DeepL helps Japanese L1 students in theirEnglish writing, and how easily it is for language teachers to identify essays written with DeepL.
The results show that five English instructors, following a predetermined evaluation rubric,
consistently rated DeepL-translated English writings higher than essays written without thehelp of Machine Translation. In addition, essays produced by DeepL were identified with highcertainty by the raters, who often commented on the “uncommon word choices or grammar forJapanese university students” (Birdsell 2022, 120). In other words, language teachers wererelying on frequent lexical and grammatical issues Japanese students faced when determining
whether a piece of writing was AI-translated, and would conclude it was “too good” in absenceof these errors. Notably, the participants in this study were first-year undergraduate students
enrolled in advanced English classes, who learned English as part of their program
requirements, and did not necessarily have a specific academic goal (e.g., to write and publish
in English).
Sasaki, Mizumoto, and Matsuda (in prep.) investigate the effect of AI translation in aslightly different vein, namely translation as corrective feedback to English writing. Instead ofusing AI-translated text directly for evaluation, they compared how second-year Japanese
students improved their English writing assignments after receiving two types of feedback:traditional corrective feedback from their instructors, or English text translated by DeepL as
feedback. Interestingly, their data show that while students receiving either type of feedbackimproved significantly in their revision, there was no significant difference between the effectsizes of instructor corrective feedback and AI translation feedback – DeepL translation was in
the least equally helpful as a learning tool for the students. To that effect, posttest questionnaires
also demonstrate that DeepL translation did not only help the students with the writing task at
hand, but also introduced expressions they were unaware of and prepared to use in the future.
Although students may respond more positively to the interactive nature of teacher feedback,this study indicates that AI translation should at least be considered as an effective alternative
when teaching resources are limited.
Both of the above studies have approached English academic writing as a part of theuniversity curriculum. Most of the English courses offered in Japanese universities fall under
the umbrella term “English for Academic Purposes” (EAP) and primarily target Japanese
undergraduate students. The learning goal is usually overall English competence or test
preparation. On the other hand, English writing for research publication can be considered as“English for Specific Purposes” (ESP) and concerns with rather different skillsets andrequirements. As a result of the recent internationalization efforts of many high-ranking
universities in Japan (cf. Top Global University Project; MEXT 2014), English research writing
education now serves a student population that differs significantly from undergraduate-level
EAP courses. The next subsection provides an overview of the main target audience of research
writing education – International Graduate Students, and the local context at the Hiroshima
University Writing Center.
2.2. International Graduate Students at Hiroshima University and the Writing Center
At this point, the readers might wonder why this study has targeted international students
at a Japanese university, a seemingly small population given the context 2 . Counter to theperception, international students have already become a sizeable group by proportion in Japan,
thanks to the internationalization initiatives some Japanese universities now focus on. Their
contribution to the English research output of a university is even more salient, since theoverwhelming majority of international students are enrolled at the graduate level. Below in
Figure 1 I show the proportion of International Graduate Students (IGS) over the past five years
at Hiroshima University, a “Type A" Top Global University3.

Figure 1. Japanese and International Graduate Students population at Hiroshima University. Data from 2023
Hiroshima University Factbook (https://sites.google.com/view/hiroshima-u-factbook)See Rakhshandehroo (2018, 1831) for a report on recent changes in international students distribution in
Japanese Higher Education.
3
For an introduction of the “Top Global Universities” project, see MEXT (2014).
2

Among the nearly 4500 enrolled graduate students at Hiroshima University, almost onethird are IGS. This proportion has remained largely unchanged over the past five years, despite
the disruption caused by the COVID-19 pandemic. Most of these students belong to academic
programs fully taught in English, often with English academic publications as one of theirgraduating requirements. Moreover, the overwhelming majority of IGS come from other Asian
countries where English is not a first language 4 . These two observations entail that manyInternational Graduate Students, in addition to facing Japanese language barriers in and out of
the university, will also receive education and conduct research in English as a foreign/second
language.
Although recent studies such as Rakhshandehroo (2018) and Rakhshandehroo and Ivanova(2020) have addressed the challenges IGS enrolled at English-medium programs often face,the “linguistic limitations” the authors focus on mainly concern with obstacles due to low
Japanese proficiency. The type of academic support Rakhshandehroo and colleagues suggest
Japanese institutions focus on, is also mostly administrative and Japanese-centered in nature(e.g., bilingual university paperwork, academic supervisors acting as “translators” during lab
meetings and seminars). On the academic/research end, the IGS in these studies are reported
to discuss their research and degree process (in English) only with their supervisors. There isan apparent inadequacy in research support in English offered to IGS, especially when
considering that some professors in English-medium programs also have limited English
proficiency (Rakhshandehroo and Ivanova 2020, 49, 52). Moreover, faculty members may
have discipline-specific expertise, but are in no capacity English education professionals.
Under this context, the Hiroshima University Writing Center was established precisely toaddress the dire need for research writing support for both students and faculty. Founded in late2013, the HU Writing Center quickly expanded its services to include both Japanese andEnglish academic writing following the 2012 Action Plan to “[support] research, particularly
in terms of publications in English” (Araki and Miyokawa 2021, 6). As part of theinternationalization and research support effort, the Writing Center created the Writing Advisor
Fellow Program in 2016, which hires employees who are mostly recent PhD graduates frominternational institutions. Working alongside Japanese- and English-speaking graduate student
tutors, the Writing Advisor Fellows primarily provide consultations on English research writing(journal articles, conference proceedings, abstracts, among others) and also organize
seminars/workshops focusing on English writing and presentation skills. The goal for thewriting consultations is specifically set to be research dissemination in English, be it
publications or international presentations. This distinguishes the fellowship position fromtraditional writing center tutors, who may also provide feedback on a variety of texts includingclass projects and writing assignments.
In addition, as the fellows use English during the consultations, their clients are
predominantly IGS enrolled in English-medium programs, who may be drafting a journal
article or part of their dissertations in English. In effect, while the Writing Advisor Fellows arealmost exclusively visited by International Graduate Students and faculty 5 , they have also
become one of very few people IGS can discuss their research with other than their advisors –a “mini-advisor” to some extent. As such, the HU Writing Center, a hybrid education andSome top-ranked countries by population are: China, Indonesia, Vietnam, Bangladesh and Cambodia.
Among the 159 English consultation appointments in 2023, 144 were made by IGS and international faculty
members. The rest of the visitors were primarily Japanese faculty members.
45research support unit, stands in a unique position among university writing centers in Japan,
most of which mainly target Japanese L1 students and academic writing for course or testpreparation purposes (Johnston, Cornwell, and Yoshida 2008; Delgrego 2016).
The significant presence of IGS at Hiroshima University and the special role of the HU
Writing Center have highlighted both the necessity and feasibility for an in-depth investigationof International Graduate Students and their research writing process. My role as the current
Writing Advisor Fellow has also provided me with the professional insight and connections tocarry out the project. Bearing in mind the relevant literature on Computer-Assisted Language
Learning in §2.1, the current study addresses a gap in the intersection between MachineTranslation in English pedagogy and writing center research. As a preliminary step of a larger
project on IGS and research writing support, this study asks the following Research Questions
from the viewpoint of International Graduate Students:(1). To what extent are IGS using AI translation to aid their English research writing?
(2). What factors may affect their writing / translating experience?
(3). How can the university writing center aid this AI-assisted writing process?
3. Methods
The current study bases its conceptual framework on several studies about English-Taught
Programs (ETPs) and International Graduate Student satisfaction in Japanese universities(Bradford 2016; 2019; Rakhshandehroo 2018; Rakhshandehroo and Ivanova 2020). Althoughthese studies are framed under slightly different contexts (e.g., undergraduate ETPs, overall
experience of IGS), they serve as good baselines for the current investigation. Furthermore,differences in the results may warrant further discussion, since they may indicate the uniquechallenges one would face during research writing at the graduate level. When appropriate, I
make use of the typology of challenges international students face proposed in Bradford (2016),
while addressing additional factors specific to academic writing and publishing. Following
Braun and Clarke (2006), the present study adopts an inductive Thematic Analysis approach.
The primary data of the study are from semi-structured interviews conducted with fourInternational Graduate Students (1 male, 3 females) at Hiroshima University. All of them wereat the “doctoral course / hakushi katei kōki” stage of their programs at the time of the interview.
They were recruited through the connection of the HU Writing Center as they had either used
the writing consultation service or participated in English writing workshops. The participants
all signed an informed consent form before proceeding to the interview. Three of the interviews
were conducted in English entirely, while during one interview the informant interchanged
between his native language (Mandarin) and English. Table 1 shows the demographic
information of the four participants.
Table 1. Demographic information of the participants.
Participant Nationality L1
Graduate Program
P1
ChineseMandarin
Early Childhood Education
P2
Malaysian
Malay
Applied Linguistics
P3
ChineseMandarin
International Education Development
P4
ChineseMandarin
Higher Education
The interview proceeded as follows. After elaborating on the purpose of the study, I allowed
the participants to read the consent form and give their signed consent. The interview mainlyconsisted of open-ended questions regarding their writing experience as IGS at Hiroshima
University, concerns they had with translating between languages when they wrote in English,
and their thoughts and worries on AI and specifically AI translation tools (e.g. DeepL, Google
Translate, ChatGPT). The interviews were recorded and automatically transcribed via
Microsoft Teams (regardless of whether the interview took place online or in person), withtranscription errors being hand-corrected.
4. Results
4.1. Usage patterns of AI translation
Following the Research Questions outlined in §2.2, I report several emerging themes fromthe interview data. The first question concerns whether and how International Graduate
Students make use of AI translation in their research writing process. All four participants
reported that they had made use of AI for the purpose of translating between texts to someextent throughout their graduate program (whereas their engagement with AI-generated texts
varied):Table 2. Direction(s) of AI translation. Items in italics indicate translation between two nonnative languages.
P1
Chinese → English; Japanese → English
P2
English → Japanese; Japanese → English; Occasionally English → Malay
P3
Chinese → Japanese; Japanese → Chinese; Japanese → English
P4
Japanese → English; Occasionally Chinese → English
As shown in Table 2, each of the interviewees not only made use of AI translation tools,
but also did it in multiple directions. Noticeably, in addition to using their L1, translations
between their L2s (English ⇌ Japanese) were commonplace. Not having access to their L1
during some translation tasks had made the writing process frustrating, as they would remain
unsure about both the original and the target texts:“I have taken it for granted that it [AI translation] must have some misunderstandingof my original ideas, because my poor English skills lead to this, not ChatGPT. I
take this as a prerequisite to use [translation tools]” – P1
“And the difficult part is, even though I use them [AI tools] to translate, you knowhow[ever] much we are good at a second language, we are not the native speaker,
and we don’t have the feeling. In Japanese, kan (勘, “intuition”).” – P3
“Sometimes when I tried to translate Japanese to English, as I don’t, I’m not sofamiliar with [the] English language, so I’m not sure. I usually translate that, andthen try to ask the native speakers to check the meaning” – P3“…All the previous studies, papers, were in English, so I had to translate fromEnglish to Japanese, both are, for me are non-native languages… Sometimes I justfelt ‘What am I doing?’” – P4
Most of the interviewees were conscious of the fact that neither English nor Japanese wastheir L1, yet they were often required to translate between these two languages to communicate
with their Japanese supervisors/cohort or to participate in primarily Japanese seminars.
Crucially, while Birdsell (2022) and Sasaki (2023) have promoted AI translation as apedagogical tool for its ability to provide L1 feedback to the Japanese students, this was simply
not applicable to IGS who had to translate between two L2s. I will return to this point during
the discussion in §5.
Moreover, most of the interviewed IGS agreed that AI translation was useful in certain
aspects of English writing, but less so during other stages. Grammar and vocabulary werementioned by all participants as what AI tools excelled at. Among them, P1 also treated AI
translation as a learning tool, where he could be made aware of words and expressions he was
not familiar with for future use. On the other hand, when it comes to content and structural
aspects of academic writing, the participants felt less certain on how much AI translation could
be of help. This can be seen from the quotes below.
“I think checking grammar is very important for me, sometimes it [AI translation]
would remind me of that grammar I already know, but I can’t use in my own
writing… But I know it has its limits, its ceiling. Sometimes I wonder if what it
offers me is really usable.” – P1
“I use it sometimes for vocabulary, sometimes for grammar, and sometimes for the
fluence of the sentence. How to write the results, how to write the statement, I thinkit’s related to the academic writing skills. That part is not so much [done by AI]. I
use them for the language check.” – P3
“Now I write in English, but I always feel stuck. I don't have enough vocabulary, I
need to have it to polish my grammar… I’m not worried about the content, because
I will always check it. The AI just polishes the English… But I do worry about it
becoming too robotic.” – P4
In short, being a non-native speaker of both English and Japanese has made it morechallenging for IGS to write academically in English. They have tried to resolve some of thewriting issues through the help of AI translation tools, yet have also acknowledged the limitsof AI in offering guidance in academic writing skills. Crucially, the interviewed clients all had
a clear idea regarding what types of feedback HU Writing Center is able to offer – comments
related to higher-level issues such as structure and logic, precisely where AI is still lacking.
Some participants commented that they would typically visit the Writing Center for thesehigher-level concerns, while leaving vocabulary/grammar/fluency problems to AI:“Since we have things like ChatGPT now, the purposes I use the Writing Center
may have changed. Maybe we can save more time, since we don’t have to bebothered with basic grammar issues. We can now discuss on a deeper level, on somecritical points6.” – P1
“As a user/client in the Writing Center, I feel like different tutors, they have different
styles to do the session… Some people, they are really good at giving comments on
the paragraph, some are really good at tutoring on the structure, and someone isreally good at rephrasing.” – P3
When asked about potential ethical concerns with using AI translation, many commented
on their ambivalent feelings towards originality and privacy issues:

6

P1 chose to respond in Mandarin Chinese for this question. The quote was translated by the author.

“…Take literature review for example: me reading and memorizing papers versus
the AI summarizing them for me. There seems to be very little difference between
them in essence” – P1
“I don’t feel like I’m cheating [using DeepL]… I really need help with thetranslation”; “If I prompt something and, they [ChatGPT], they’d give good ideas…
I’m worried if I take those and put them into my writing, whether, is this really mywork?” – P2“…They talk about AI collecting a lot of information, from you and, then fromeveryone, and then they deliver to everyone… I’m worried that if my idea, if I ask
ChatGPT to polish my English, but they give my idea to others…” – P4
Whereas P1 argued that summarizing relevant scholarship with the help of AI did notconstitute academic integrity violations in his opinion, P2 shared her concern with ChatGPT’s
ability to expand on certain inquiries – it would be difficult to “unsee” potentially useful ideasthat were not part of the translation task but were nevertheless generated by AI. On the other
hand, P4 was worried about the potential for these AI tools to leak what the users have uploaded,some of which might be sensitive or unpublished data. IGS remained skeptical about thepersonal information and privacy crisis that could be brought by the prevalence of AI.
4.2. Factors affecting AI translation usage
In this subsection I report some factors that may have influenced the interviewees in their
AI usage. It is important to note that some aspects the participants commented on, were notdirectly related to specific properties of the AI tools, but were more reflective of the academic
environment they were in.
Between the two popular varieties of AI translation tools, DeepL and ChatGPT, theparticipants had different preferences due to the functions they provide. P1 valued the “ability
to have a ‘chat’ with ChatGPT”, where he could revise the translation by pointing out aspects
the chatbot might have missed in the first attempt. In comparison, DeepL only provided atranslation result, with very little room for editing and revising. In comparison, P2 preferred touse DeepL for her translation tasks because she considered it to be “more accurate”. In her case,the task-centric nature of DeepL may help reduce her uncertainty with translation. The almost
opposite accounts of P1 and P2 also demonstrate that the language environment associatedwith their academic programs plays a pivotal role in how they seek assistance from AI
translation. P1 comes from an overwhelmingly Japanese doctoral program where he could only
communicate with a limited amount of people in English. As his target language of publication
was English, much communication within his program (e.g., seminar materials, course
projects) needed to be translated into English before it became useful for his research writing:“I have some levels of Japanese so I can just cover all the daily dialogue, but ofcourse for some serious topics I would like to use English… as I choose English as
my main language of my doctoral program, I [will] never have a Japanese academic
writing” – P1
On the other hand, P2 is enrolled in an English-medium program where most of her cohorts
are international students. While her English level improved through interacting with heradvisor and peers, there were still some Japanese writing requirements that made it necessary
for her to rely on AI translation – “I had to write my research plan in Japanese and submit it tothe support office”. Those documents, though administrative/managerial in nature(Rakhshandehroo and Ivanova 2020, 43), were directly related to her research topics (applied
linguistics) and required her to know discipline-specific terms in both languages.
Compared to academic programs heavily skewed towards one language or the other, P4
was in a rather peculiar context where code-switching between Japanese and English wasprevalent: all of the readings in her seminars and courses were written in English, yet theadvisor used Japanese to communicate with the class and all course assignments were done inJapanese. Being at the middle of two foreign languages, the interviewee had to constantly
translate terminology and ideas back and forth, from the English readings to her Japanese classprojects, then to her own English publications. While this mode of bilingual education was
somewhat innovative in the university, it did pose substantial challenges to International
Graduate Students like P4 – unlike her Japanese peers, she did not have a first language to rely
on during the back-and-forth translations between Japanese and English. To her, the efficiency
and accessibility of AI translation tools were crucial to her success in the program.
One might wonder why IGS enrolled in Japanese-heavy programs decided to write
academically in English to begin with, as it was often the case that English publications were
not required for graduation if a student could write professionally in Japanese. P3 and P4
provided similar reasons, citing their professional goals as the main reason:“I don’t have to… there is no clear principle like ‘you have to publish in English’…
you know my professor doesn’t care about my English writing”; “When I decided
to use two languages, the reason is that I want to have a broad, or more options in
the future. Once I write English paper[s], it means that some day I could be valuedby another country… English [writing] is not so supported by my professor, so I doit myself.” – P3
“I’m thinking about my future, my career, I don’t know where I want to go… If I
go back to China, they don’t recognize Japanese papers. And if I want to go to othercountries, they cannot read Japanese. So how can I prove that I can do the job?” –P4
As shown in these quotes, both participants were required to write research papers inJapanese by their academic programs. Nevertheless, both chose to write English on their own,
sometimes with almost no support from their supervisors. Compared to domestic graduate
students, IGS were less certain on whether they would seek employment in Japan after
graduation (cf. Rakhshandehroo 2018). Their decision to write and publish in English wasautonomous, as many of them were already considering moving abroad (or back to their homecountries) and staying more marketable on the international stage. Without the explicit support
from their supervisors, many of them did not have an authoritative figure proficient in English
research writing throughout their program, and had to resort to AI translation tools.
5. Discussion and conclusion
This study aims to investigate to what extent and in what ways International Graduate
Students in a Japanese university are utilizing AI translation tools during their research writing
process. I have demonstrated that many participants had to translate in multiple directions
involving more than one non-native language (i.e. Japanese and English) due to the language
context of Japanese graduate programs. The interviewed doctoral students, being at a more

advanced stage of their studies, were also more autonomous and used both AI translation andwriting center services to tackle their vocabulary/grammar issues and higher-level concerns
respectively. Also notable is the fact that AI translation has allowed the IGS to use their L1s as
a resource when learning English writing: whereas there is virtually no way for a Japaneseinstitution to address the vast variety of foreign languages the IGS speak natively, the AI tools
have provided a space where they can use their first languages to revise their writing withrelative ease (cf. Sasaki et al. 2023; “L1 use as a resource”). Given these circumstances,integrating AI translation with writing has become a sustainable learning model for theInternational Graduate Students.
Moreover, I have identified several factors that either lead to preference over certain AI
translation programs, and ones that made it necessary for the IGS to rely on AI translation.
Some of these factors are not specific to AI writing support, but instead point at more generalissues IGS may experience in their graduate programs – academic supervisors’ overall
indifference to English research writing being one particularly worrisome observation. As a
research support unit of the university, the HU Writing Center alone is not equipped to resolve
all the issues arising from these interviews. Nevertheless, I offer some open-ended suggestions
from both the participants’ reflections for writing centers to consider for their future work.
Several participants, while acknowledging that the HU Writing Center does not offer full
proofreading services, did wonder whether it would be allowed to request ‘light’ proofreading
for their AI translated texts (P3 referred to it as “native check” in her response). Although mostwriting centers in Japan still operate under a “no proofreading” policy (North 1984; Delgrego
2016), it might be viable to incorporate AI texts into our consultation services – translated texts
generated by AI are usually free of grammatical mistakes or typos, which makes it easier for
writing center tutors to focus on content and fluency issues. Arguably, it is less productive torequire writing center clients to write solely in English and the tutors to ignore the prevalent
lower-level issues they observe in the texts. AI translation tools provide an accessible way tofill the proofreading service gap during the research writing process.
The interviewed students were also expressing interest in seminars and training sessions
for AI use. On the one hand, they would appreciate it if the school (or the Writing Center, forthat matter) was more explicit on policies regarding ethical and responsible use of AI. P2,
during her interview, mentioned that she was not sure if her text translated by DeepL could be
still considered as “her work”, and whether the translated text was eligible for writing
consultation services. Regardless of whether this type of text is accepted, more clarificationfrom the university or the Writing Center would at least reduce the uncertainties associatedwith AI and writing. Moreover, although not clearly communicated by the participants, writing
centers and similar education support units may also assist the university students by providing
instructions or workshops on prompt construction when interacting with AI – how to get
feedback more efficiently, how to improve the translation quality, to name a few topics. More
proactive instruction on AI usage may also help the Writing Center in the longer run as itstreamlines the writing process for the writers before they visit us.
These suggestions aside, I am in no way suggesting that the current study can provide
solutions to all that are related to AI translation and research writing, and supporting
International Graduate Students in this context. It is more suitable to consider this article as acall-to-action for the current situation of English research writing in Japanese universities,
especially ones involving International Graduate Students due to their language background.
As Japanese universities strive to become more international, it is increasingly important to notonly allow for more international (graduate) programs and enrollment, but also provide fitting
services in education and research support.
References
Araki, Hiroko, and Norifumi Miyokawa. 2021. “Development of the Hiroshima University
Writing Center.” Reviews in Higher Education, no. 157: 3–16.
Bennett, Karen. 2013. “English as a Lingua Franca in Academia: Combating Epistemicide
through Translator Training.” The Interpreter and Translator Trainer 7 (2): 169–93.
Birdsell, Brian. 2022. “Student Writings with DeepL: Teacher Evaluations and Implications
for Teaching.” In JALT Postconference Publication 2021, 117.
https://doi.org/https://doi.org/10.37546/JALTPCP2021.
Bradford, Annette. 2016. “Toward a Typology of Implementation Challenges Facing
English-Medium Instruction in Higher Education.” Journal of Studies in International
Education 20 (4): 339–56. https://doi.org/10.1177/1028315316647165.
———. 2019. “It’s Not All about English! The Problem of Language Foregrounding in
English-Medium Programmes in Japan.” Journal of Multilingual and Multicultural
Development 40 (8): 707–20. https://doi.org/10.1080/01434632.2018.1551402.
Braun, Virginia, and Victoria Clarke. 2006. “Using Thematic Analysis in Psychology.”
Qualitative Research in Psychology 3 (2): 77–101.
https://doi.org/10.1191/1478088706qp063oa.
Costa-jussà, Marta R., Mireia Farrús, and Jordi Serrano Pons. 2012. “Machine Translation in
Medicine: A Quality Analysis of Statistical Machine Translation in the Medical
Domain.” In Advanced Research in Scientific Areas, 1995–98.
Cotton, Debby R. E., Peter A. Cotton, and J. Reuben Shipway. 2023. “Chatting and Cheating:
Ensuring Academic Integrity in the Era of ChatGPT.” Innovations in Education andTeaching International 61 (2): 228–39.
https://doi.org/10.1080/14703297.2023.2190148.
Dakakni, Deema, and Nehme Safa. 2023. “Artificial Intelligence in the L2 Classroom:
Implications and Challenges on Ethics and Equity in Higher Education: A 21st Century
Pandora’s Box.” Computers and Education: Artificial Intelligence 5: 100179.
https://doi.org/10.1016/j.caeai.2023.100179.
Delgrego, Nicholas. 2016. “Writing Centers in Japan from Creation until Now: The
Development of Japanese University Writing Centers from 2004 to 2015.” Studies in
Language and Culture, no. 7: 16–27.
Ducar, Cynthia, and Deborah Houk Schocket. 2018. “Machine Translation and the L2
Classroom: Pedagogical Solutions for Making Peace with Google Translate.” Foreign
Language Annals 51 (4): 779–95. https://doi.org/10.1111/flan.12366.
Eke, Damian Okaibedi. 2023. “ChatGPT and the Rise of Generative AI: Threat to Academic
Integrity?” Journal of Responsible Technology 13 (April): 100060.
https://doi.org/10.1016/j.jrt.2023.100060.
Escartín, C. P., S. O’Brien, M. J. Goulet, and M. Simard. 2017. “Machine Translation as an
Academic Writing Aid for Medical Practitioners.” In Proceedings of MachineTranslation Summit XVI: Research Track, 254–67.

Garcia, Ignacio, and María Isabel Pena. 2011. “Machine Translation-Assisted Language
Learning: Writing for Beginners.” Computer Assisted Language Learning 24 (5): 471–
87. https://doi.org/10.1080/09588221.2011.582687.
Groves, Michael, and Klaus Mundt. 2015. “Friend or Foe? Google Translate in Language for
Academic Purposes.” English for Specific Purposes 37: 112–21.
https://doi.org/10.1016/j.esp.2014.09.001.
Johnston, Scott, Steve Cornwell, and Hiroko Yoshida. 2008. “Writing Centers in Japan.” 大
阪女学院大学紀要, no. 5: 181–92.
Lee, Sangmin-Michelle. 2020. “The Impact of Using Machine Translation on EFL Students’Writing.” Computer Assisted Language Learning 33 (3): 157–75.
https://doi.org/10.1080/09588221.2018.1553186.
Liang, Weixin, Mert Yuksekgonul, Yining Mao, Eric Wu, and James Zou. 2023. “GPT
Detectors Are Biased against Non-Native English Writers.” Patterns 4 (7): 100779.
https://doi.org/10.1016/j.patter.2023.100779.
MEXT [Japanese Ministry of Education, Culture, Sports, Science and Technology]. 2014. ス
ーパーグローバル大学創成支援. Retrieved fromhttps://www.mext.go.jp/a_menu/koutou/kaikaku/sekaitenkai/1360288.htm
Niño, Ana. 2009. “Machine Translation in Foreign Language Learning: Language Learners’
and Tutors’ Perceptions of Its Advantages and Disadvantages.” ReCALL 21 (2): 241–58.
https://doi.org/10.1017/S0958344009000172.
North, Stephen M. 1984. “The Idea of a Writing Center.” College English 46 (5): 433.
https://doi.org/10.2307/377047.
Rakhshandehroo, Mahboubeh. 2018. “A Qualitative Exploration of On-Campus Experiencesof English-Speaking Graduate International Students at a Leading Japanese University.”
Journal of International Students 8 (4): 1831–47.
https://doi.org/10.5281/zenodo.1468091.
Rakhshandehroo, Mahboubeh, and Polina Ivanova. 2020. “International Student Satisfaction
at English-Medium Graduate Programs in Japan.” Higher Education 79 (1): 39–54.
https://doi.org/10.1007/s10734-019-00395-3.
Sasaki, Miyuki. 2023. “AI Tools as Affordances and Contradictions for EFL Writers: Emic
Perspectives and L1 Use as a Resource.” Journal of Second Language Writing 62(December): 101068. https://doi.org/10.1016/j.jslw.2023.101068.
Sasaki, Miyuki, Atsushi Mizumoto, and Paul Kei Matsuda. n.d. “Machine Translation as aForm of Feedback in L2 Writing.” Available at SSRN:
https://ssrn.com/abstract=4380211 or http://dx.doi.org/10.2139/ssrn.4380211.
Warschauer, Mark, Waverly Tseng, Soobin Yim, Thomas Webster, Sharin Jacob, Qian Du,
and Tamara Tate. 2023. “The Affordances and Contradictions of AI-Generated Text for
Writers of English as a Second or Foreign Language.” Journal of Second Language
Writing 62 (December): 101071. https://doi.org/10.1016/j.jslw.2023.101071.