A Qualitative Approach to EFL Postgraduates’ GenAI-Assisted Research Writing Within Social Sciences

Abstract

Systematic and rigorous approaches are necessary to fully understand GenAI’s (Generative AI’s) impact on L2 English/EFL (English as a Foreign Language) academic writing in higher education. In this scope, postgraduate EFL writing has been explored little. The present qualitative study examines this topic within Social Sciences at the University of Extremadura, Spain, where seven participants with a B2 English level or higher enrolled in a 10-h hybrid course about GenAI for academic English writing in October and November of 2024, focusing on AI tools and Broad Data-Driven Learning (BDDL) resources (e.g., simple online corpora tools) to assist their writing. Participants’ feedback was collected by qualitative means (in-class discussions, task writing annotation, and final survey). Overall findings indicate notably positive responses and usage of these tools for the improvement of their texts (e.g., linguistic analysis, lexical-grammatical refinement, and text style improvement). Participants’ activities also showcase miscellaneous approaches and strategies in their management of GenAI. Despite the study’s small sample, these preliminary findings reveal that these postgraduate EFL writers can exploit expert and linguistic knowledge effectively using GenAI, demonstrating meta-linguistic awareness and digital literacy-related skills.

1. Introduction

With the increased use of GenAI (Generative Artificial Intelligence) tools in tertiary education, rigorous and systematic approaches are needed to fully understand GenAI’s impact on L2 English/EFL (English as a Foreign Language) writers. Even though different systematic and scoping reviews have documented how different educational communities respond to GenAI (Alhusaiyan, 2025; Chanpradit, 2025; Crosthwaite & Sun, 2025; Feng et al., 2025; Law, 2024; Luo & Zou, 2024; Xia et al., 2025), the research mostly addresses undergraduate levels in L2/EFL contexts. The reviews examine, among other aspects, advantages such as workload reduction and effective tailored support, and drawbacks such as overreliance, breaches of academic integrity, and insufficient development of critical thinking skills (e.g., Farrokhnia et al., 2024; Rowland, 2023).

Focusing on the effects of GenAI-generated feedback on L2/EFL writers’ mental processes, recent reviews also describe positive developments and opportunities for meta-linguistic and writing skills (e.g., Crosthwaite & Sun, 2025). However, some critical aspects such as the need for strengthening authorial voice, personalization, context, and topic-specific references, are pinpointed in different studies on GenAI-generated academic L1 and L2 writing (Azennoud, 2024; Cheung & Crosthwaite, 2025; Huang & Deng, 2025; Jiang & Su, 2025). In the case of postgraduate L2/EFL users of GenAI, their stronger epistemological awareness and better judgment within their fields seem to make them better evaluators of GenAI-generated content (Ruff et al., 2024). However, meta-cognitive, meta-linguistic, and critical digital literacy skills should be further explored in connection with L2 processing and writing competence at all levels (Williams, 2024; Pérez-Paredes et al., 2025).

As a result, postgraduate EFL writing analysis is a demanded topic. At these advanced university stages, GenAI could be suitably utilized across domains within a DDL (Data-Driven Learning) scope. This approach means that higher-order academic skills such as textual and linguistic pattern observation and evaluation can be exploited with GenAI for writing development. The argument is that L2 users’ interactions with GenAI can be equated with digital corpus literacy skills (Cheung & Crosthwaite, 2025; Crosthwaite & Baisa, 2023; Pérez-Paredes et al., 2025). Writing competence at these higher education levels may benefit from this broad DDL (BDDL) perspective, since BDDL can be easily exploited in the form of widely and freely available linguistic corpus-based resources—such as web concordancers, simple corpus interfaces, digital bilingual collections, translation software, and collocation finders—to support L2/EFL-related learning and writing mechanics (Curado Fuentes, 2025a; Pérez-Paredes, 2024; Ordoñana Guillamón et al., 2024). Their integration with GenAI to target critical thinking skills and digital literacy thus deserves further exploration in academic domains, as Cheung and Crosthwaite (2025) state.

This case study presents a qualitative approach to postgraduate EFL writing processes within Social Sciences after seven participants in this domain took part in a 10-h course on GenAI for academic writing. This type of study is crucial, in our view, because most postgraduate L2/EFL writers are already relying on GenAI to support their research writing practices, and yet, few studies have explored how these participants’ strategies and attitudes may evolve or change after attending courses on critical digital literacy integration, such as interactional GenAI and BDDL skills.

This article begins with a review of relevant literature on GenAI in academic writing, followed by the study’s research questions and methodology. It then presents the results, discusses key insights, and concludes with implications, limitations, and directions for future research.

2. Literature Review

2.1. GenAI and Academic English Writing

The rise of GenAI tools—such as ChatGPT (3.5 and 4, and ChatGPT Sholar), Gemini (2.5 Flash), and Co-Pilot (M365)—has influenced every stage of the academic writing process, including pre-writing, drafting, and revising (Pigg, 2024). The effectiveness of this technology for academic English writing in L1 and L2 contexts often depends on users’ cognitive efficacy with prompt interactions by which they decode, produce, and refine texts within their specific contexts (Cordero et al., 2025; Ingley & Pack, 2023). This competence is realized in the form of a “distributed agency” (Godwin-Jones, 2024) with the tools, which means that academic L1 and L2 writers rely on GenAI to assist tasks such as topic outlining and content drafting (Nordling, 2023; Pigg, 2024).

All kinds of academic and research writers are thus increasingly employing GenAI for various tasks, such as summarizing texts, synthesizing literature, locating research gaps, and simplifying complex information (Pigg, 2024). L2 writers generally regard these tools as helpful for improving vocabulary, grammar, tone, and overall textual cohesion (Barrot, 2023; Ji et al., 2023; Nordling, 2023). However, human oversight remains essential for these L2/EFL writers whose texts must be constantly scrutinized to correct linguistic errors and to find genuine authorial voices (Berber Sardinha, 2024; Markey et al., 2024). For example, one common shortfall of GenAI textual output is the lack of personal engagement (Jiang & Hyland, 2024), since these texts often miss key evaluative language and self-referencing strategies (Jiang & Hyland, 2025; Mo & Crosthwaite, 2025; Zhang & Crosthwaite, 2025). A pedagogical challenge here is to make academic L2/EFL writers aware of such deficiencies so that these users can develop critical thinking skills to improve their texts by means of effective evaluation and revision, combining both disciplinary and linguistic approaches. This type of task can lead to successful academic writing practices with GenAI (Pigg, 2024).

2.2. L2/EFL Writers’ Use of GenAI at Postgraduate Stages

A search conducted in August of 2025 on mainstream databases (Scopus, DOAJ, and Google Scholar) about postgraduate L2/EFL writing with GenAI yielded few results. The studies examined are described below according to major scientific areas.

In Experimental Sciences and Technology, Khuder (2025), Kramar et al. (2024), L. Liu and Wang (2024), Ruff et al. (2024), and Smit et al. (2025) address postgraduate L2 research writing by analyzing survey responses. In general, they find that students at these university levels appreciate ChatGPT more for improving English grammar and vocabulary, and less for content alignment with their thematic developments. Participants in Ruff et al. (2024) and Smit et al. (2025) also used translation and paraphrasing tools, but they relied more heavily on ChatGPT as their use with GenAI progressed. At the same time, concerns about ethics and academic integrity are frequently raised in these contexts (Kramar et al., 2024; Smit et al., 2025).

In Health Sciences, Williams (2024) notices that postgraduate participants find ChatGPT useful for planning and revising L2 texts, especially to improve organization and meet disciplinary conventions in medical texts. Most errors examined in this tool involve the misapplication of technical terms and lack of contextual relevance. Compared to tools like Bing or Bard, ChatGPT is regarded by these medical students as more effective for their writing tasks. Milton et al. (2024), upon surveying over 300 postgraduate students in Health Sciences at an Indian university, find that most L2 students respond very positively to using ChatGPT for writing in English. However, these authors also observe concerns among participants about becoming over-dependent on GenAI, which could undermine their more independent ways of working.

In Social Sciences, Jacob et al. (2023) focus on one L2 researcher in Education, analyzing how she critically evaluates ChatGPT’s limitations—such as factual inaccuracies and bias—while adopting its linguistic suggestions to revise her work. The student also corrected repetitive wording and sought peer input along the process of improving her text. The authors conclude that this type of frequent GenAI user is fully aware of consistent oversight and proofreading demands. With a similar qualitative method, Curado Fuentes (2025b) examines four postgraduate EFL writers’ developments in Tourism, observing that this type of user, already experienced with obtaining support from digital tools for writing, adapts AI technology effectively for revising their own texts.

Overall, albeit scarce, these findings suggest that postgraduate L2 English writers recognize both the strengths and risks of GenAI. They tend to approach these tools with great critical awareness and domain knowledge. As Costa et al. (2024) observe across postgraduate L2 contexts, ethical concerns remain a central issue, especially regarding plagiarism and a loss of voice. As a result, academic L2 writers’ successful approaches at this level could involve shifting from a product-centered view of writing to a more process-focused approach—one that emphasizes contextualization and the development of an individual voice (Godwin-Jones, 2024). Moreover, doctoral and post-doctoral writers are often proficient in specialized terminology and conceptual frameworks. This expertise helps them to evaluate and refine GenAI-generated content, whereas main linguistic challenges in L2/EFL contexts tend to involve the mastering of phraseology and discipline-specific lexico-grammatical nuances (Laso Martín & Comelles Pujadas, 2025).

2.3. Broad Data-Driven Learning (BDDL) for Academic L2/EFL Writing

As mentioned in the Introduction, the integration of a broad DDL (Data-Driven Learning) approach in postgraduate EFL writing can foster critical digital literacies. This approach can engage postgraduate L2 writers with linguistic querying and probing, fostering digital skills that are similarly required for GenAI. BDDL incorporates user-friendly online tools—such as collocation finders, easy-to-use concordancers, and bilingual resources—that help learners resolve language-related questions (Curado Fuentes, 2025a; Pérez-Paredes, 2024; Ordoñana Guillamón et al., 2024). These tools include large collections of academic texts within different scientific disciplines which can provide L2 writers with direct access to a rich academic phraseology within their fields. This type of approach can also suitably complement feedback generated by GenAI (Crosthwaite & Baisa, 2023), whereas L2 writers can develop meta-linguistic analysis and processing with these versatile tools (Ordoñana Guillamón et al., 2024).

Another advantage of BDDL is its shifting focus from complex data analysis to practical EFL learning (Pérez-Paredes, 2024, p. 218). It enhances specific linguistic skills while supporting critical digital literacies. These resources also promote independent learning and reflective thinking—key competencies for these researchers (Criollo et al., 2024). Ultimately, BDDL fosters flexible, self-directed learning environments that align well with the needs of advanced L2 writers. When used in combination with GenAI, these tools can support more informed, precise, and effective academic writing.

3. Research Questions

This case study aimed to obtain direct feedback from postgraduate EFL writers using GenAI within Social Sciences by relying on qualitative instruments. In particular, the study aimed to answer:

How does a small group of doctoral and post-doctoral EFL writers within Social Sciences approach their academic texts using GenAI tools?

How do these writers appraise these tools for English writing within their fields?

4. Methodology

The methodology follows a qualitative study design using participants’ feedback from a short course taught at University of Extremadura (Spain) on GenAI for academic writing. This convenient sample of postgraduate EFL writers was employed because they could provide key information about writing procedures with GenAI during and after they took the course. Additionally, because they belonged to Social Sciences disciplines, some domain-specific information could be considered for comparison. The course lasted 10 h (eight hours of instruction and two hours for task completion) and was offered to doctoral and post-doctoral researchers in October and November of 2024. The course combined in-person seminars, online discussions, and hands-on tasks. Core activities included a lecture on prompt engineering, linguistic analysis, and critical reflections about GenAI use for academic writing. Another important component was the integration of GenAI with BDDL tools, which were explained and showcased as simple, easy-to-use concordancers and collocation finders to support linguistic analysis.

The methodology followed standard practices for qualitative case studies (Mabry, 2008; Sena, 2024), drawing on three primary data sources: (1) classroom observation, (2) analysis of participants’ written tasks, and (3) a post-course survey. Triangulating these data allowed for a comprehensive view of participants’ learning progress and attitudes.

4.1. Participants

The course attendants were native Spanish researchers and lecturers from Social Sciences disciplines: Business Management (one participant), Education (three), and Tourism (three). All attended both face-to-face and online sessions. Five participants were tenured faculty members: Paloma and Valeria (Education), Aurora and Juan (Tourism), and Pablo (Business Management), and two were doctoral researchers (María in Education and Begoña in Tourism). All had at least a B2 level of English, a requirement for course participation. Each gave informed consent for their coursework to be analyzed, but their names are not the real ones to protect individual privacy.

4.2. Data Collection Instruments

4.2.1. In-Class Observation

Observation took place during a four-hour in-person seminar and a four-hour Zoom session. In class, the author of this study took notes about participants’ contributions while the main lecturer, a colleague from a different university, conducted the class in English and Spanish, mainly relying on a slide presentation but also having students participate through questions and discussion. The notes taken about this class were read and validated by both instructors after class. In turn, the online session was recorded. This session had a first half focusing on guided instruction for task writing, whereas, in the second half, participants worked in pairs within breakout rooms, with the author of this paper rotating among rooms to provide support and gather observational data on engagement and tool use. This session recording was then watched by the author of this study, who contrasted major activity developments.

4.2.2. Final Writing Task

Participants completed a written assignment using GenAI and BDDL tools to develop an academic text relevant to their own studies. The task prompt instructed:

“Use GenAI for any stage(s) of your academic writing process—pre-writing, drafting, writing, and/or rewriting—and with any type of text (e.g., research paper, teaching material, technical document). In addition to GenAI, use concordance tools to support your writing. Clearly describe each step you followed and explain how the tools were used.”

The analysis of these tasks was based on the two instructors’ annotations of micro- and macro-level aspects in the students’ writing developments. The evaluators’ notes on these aspects were compared to gather key information according to three main categories: Pre-writing, drafting, and re-writing. Overall, it was found that macro-level elements such as text outlining and organization, thematic coherence, and authorial voice, appeared across all writing stages, whereas micro-level aspects related to lexical exploration, grammatical correction, and cohesive features, pertained to drafting and re-writing steps. The results are classified according to the examination of these aspects within writing stages denoting different purposes in the students’ task.

4.2.3. Final Survey

An online survey (via Google Forms) was administered after course completion to examine participants’ appraisal. The survey was anonymous, but it required participants to state their academic/professional position and university degree. The survey included 33 Likert-scale items (1–5), with 17 questions on GenAI and 16 on BDDL tools. Items measured general attitudes, perceived usefulness for language improvement, ease of use, and intent to continue using the tools, based on the model provided by Hua et al. (2024) for digital literacies. The instrument showed strong internal consistency (Cronbach’s α > 0.96). The survey also included three open-ended questions asking participants to: (1) Identify the most helpful tool; (2) Describe which types of texts they used the tools for; (3) Reflect on both advantages and limitations they encountered.

5. Results

5.1. In-Class Findings

This part of the study focused on observing and listening to participants’ ideas, habits, and interactions with GenAI during the course. These observations provide a baseline for understanding how students approach individual tasks and respond to survey questions later.

In class, senior faculty members led most of the discussions. The two younger researchers, in contrast, listened, agreed, and took notes more frequently. A key point that arose was the discussion of academic integrity and ethical issues related to GenAI. For example, Juan was very enthusiastic about GenAI’s potential for summarizing research, but he also pointed out that the tools often provided incorrect references. Aurora agreed, stating that while these AI tools are a good starting point, they cannot replace real research. She mentioned she uses them mostly to brainstorm ideas and preliminary conceptual frameworks for her topics.

Juan also expressed his concern about one of the course requirements to use GenAI in a way that does not sound “robotic.” He noted that for non-native English speakers, who already face challenges with language revision and acceptance in academic journals, these tools could be very helpful, and he felt that having to make the language sound more “human” was unfair if the original message was already clear. This concern was brought up for further discussion and activities later. It reflects a main challenge discussed in the literature: the need to convey one’s authorial voice in L2 writing (Berber Sardinha, 2024).

The second part of the in-class session focused on specific strategies for writing with GenAI, such as prompt engineering and developing a unique authorial voice. All participants found the explanations and examples highly useful for improving their interactions with GenAI tools. A major challenge discussed was how to find one’s own voice while using the tool for re-writing. Juan brought up again his frustration with having to constantly rewrite to make texts sound less generic. This led to a discussion about linguistic accuracy, academic integrity and plagiarism. The group agreed that using GenAI-generated text is risky, and research articles should be original. Aurora suggested that if a text was “co-authored with GenAI”, the author should explicitly state this in a footnote to ensure an ethical approach. Additionally, the use of other tools, such as collocation finders, corpora, and concordancers, was found interesting and helpful for writing.

The online session, conducted one week later, focused on using GenAI and other tools for rewriting text at both the micro (sentence level) and macro (structural) levels. Most participants were already familiar with online BDDL tools, such as simple concordancers and academic corpora, for confirming and comparing language choices during the writing process. Two participants, for example, had taken a course on the use of COCA (Corpus of Contemporary American English). We then explored a corpus of research proposal texts, which they all considered important, to practice re-writing by examining genre conventions, academic tone, and linguistic nuances.

Working in pairs, students explored these textual and linguistic features. For example, one activity asked them to state the key objectives for a research proposal using clear, simple sentences. All participants used sophisticated prompts in ChatGPT and Co-Pilot, asking the tools to account for grammatical simplicity, clarity, vocabulary, content, context, and audience (e.g., potential funders of the research).

They integrated online concordancers and corpora utilities (Corpus Mate, COCA, Linggle, and NetSpeak) to compare academic English phraseology and vocabulary in Social Sciences, since these corpora contain academic English across various disciplines and subject areas. One phraseological example was the use of “the project aims to” versus “this work is aimed at,” as both are frequently found in academic writing. However, these distinctions were not always clear. María referred to her constant struggle to find adequate lexical-grammatical choices based on more appropriate English usage. Generally, the group favored an active, more direct voice for this type of text. Students also noted the importance of semi-academic tones, which combine familiar language with specialized terms for clarity.

5.2. Writing Task Developments

Most participants combined GenAI with corpus tools for their final written assignment. AI mainly helped them with content, structure and clarity at both macro- and micro-levels of analysis, while most corpus checks were made to ensure the use of appropriate academic English phraseology and collocations (micro-level). Each participant coped with different issues and tactics for their research writing, described below according to writing stages and purposes during the writing process.

5.2.1. Applying GenAI to Pre-Write Research

The following participants deployed GenAI tools to start their research work, focusing on macro-level analyses of academic texts:

Juan used pre-writing strategies by relying on Elicit as a research assistant “to better explain his work on climate change impact on urban planning and design.” The tool summarized top papers. However, he found that only two of the eight papers were academically sound. He then asked Microsoft’s Co-Pilot to create an outline for his article. The tool provided a detailed outline with headings and subheadings, which Juan saved for later.

Juan then used Co-Pilot to develop the article’s introduction, asking for an outline for the text by providing the tool with three PDF articles. Co-Pilot summarized the papers by section. Juan then asked the tool to write the full introduction using the outline and ideas from the papers. Juan was not completely satisfied, so he made manual changes, like correcting an inaccurate description of a paper’s methodology.

Paloma used Co-Pilot to write a 500-word academic text on AI in Education. She first asked Co-Pilot to write about the paradigm shift that this type of technology entailed for teaching. She then specified that she wanted clear simple sentences and specific examples of new types of teaching approaches.

Valeria did the same with Co-Pilot for writing a blog entry on novel digital teaching methods, which she requested to be concise, clear, and informative for a lay audience. She explicitly stated her satisfaction with the text in terms of clarity and conciseness.

Paloma and Valeria also asked Co-Pilot to integrate updated bibliographic references within their texts, which these students checked for validity. They discarded some of them due to their poor and/or questionable quality.

5.2.2. Applying GenAI to Assist with Research

In this case, these participants had already written their own texts and used the tools to improve research concepts and references at the macro-level of text processing:

Aurora’s task involved the introduction section of a research article she was writing for a high-impact journal. She told ChatGPT Scholar about this goal in her initial prompt and asked it to act as an expert on social factor analysis. She wanted the final text to be about the same length as her original one (around 830–840 words) and asked the tool to add scholarly references as needed. Aurora was happy with the content, noting that the final version “faithfully represented” what she wanted to say and it included valid sources. The tool also highlighted research gaps in a bulleted list, a feature Aurora found especially helpful and appealing for promoting her research.

Pablo’s abstract was rewritten by Co-Pilot according to his research needs, but the tool reduced the text considerably. Therefore, Pablo rewrote some parts on his own, and in this process, he asked the tool to insert some updated references within the text, which he checked for academic integrity. He removed two and kept one of the sources (in addition to his initial ones).

5.2.3. Aiming at Text Simplicity and Clarity

In this case, these participants focused on re-writing procedures for text clarity and conciseness at both macro- and micro-level stages:

María asked ChatGPT to rewrite a conference abstract she had written herself so that the language sounded “simple and clear, with few adverbs, connecting words, or dependent clauses, and with words a general audience would understand” (micro-level query). ChatGPT provided a good result: it split the abstract into three short paragraphs, used shorter sentences, and changed all passive voice to active. The language also became more familiar. While María liked the result, she preferred a single paragraph. She asked the tool to combine the text into one paragraph and simplify it further (macro-level step).

Begoña used Gemini to re-write a conference abstract (already submitted and accepted) with clearer and simpler grammar, using cohesive devices, and academic vocabulary in her field (micro-level prompt). However, she found Gemini’s rewritten text to sound “bombastic and robotic.” She therefore asked Gemini why the text did not sound human, and the tool responded with the following points: (1) The language was too formal (e.g., using “delving into” and “renowned authors”), (2) It lacked a personal touch, and (3) It was overly objective. To fix this, Begoña manually added some specific context about her research topic and its application in her local tourism sector (macro-level strategy). The new version was a single paragraph that was more cohesive and personalized, and, in Begoña’s words, “more directly represented my authorial voice” (macro-level realization).

Paloma and Valeria ran prompts in Co-Pilot to convert their texts into slides for classroom presentations. They revised all the bulleted slides for intended content, clear language, and format, combining micro- and macro-level approaches. In their conclusions about the task, they expressed their satisfaction not only with the final product but also with the process of interacting with the tools, alluding to dynamic ways of working that saved time and made academic work enjoyable.

Pablo, in his rewriting step, asked Co-Pilot to revise his research abstract for clear language, strong cohesion, and style, and to maintain the same number of words as his initial text, thus focusing on both micro- and macro-level concerns. The final product convinced him, and he decided to use this version for his submission to a journal.

5.2.4. Using GenAI for Revising Specific Linguistic Aspects

Juan, working on his research article’s introduction, asked Co-Pilot to proofread the text by specifically focusing on “appropriate phrases and vocabulary usage” (micro-level analysis). He also asked Co-Pilot to explain the changes it made. The tool made 15 changes to vocabulary, grammar, and phrasing, explaining each one. For example, it changed “contempt” to “mitigate” for clarity, and it fixed subject-verb agreement errors. It also made sentences shorter to improve conciseness.

5.2.5. Using BDDL to Focus on Linguistic Nuances

As a micro-level activity, Juan also used COCA’s academic section to explore Co-Pilot’s changes. He found that various changes were valid, but he noticed two exceptions: that “dramatically” was used less frequently than “significantly,” and “request for government action” was less frequent than “demand government action.” Juan thus made linguistic changes based on these corpus-based frequencies.

In similar micro-level procedures, María used Corpus Mate to make phraseological changes in her text when she felt that some expressions sounded awkward or when she was uncertain about their proper academic use. For example, she looked up “help” and “personalize” and found that “facilitate personalization” was more common in academic writing. So, she changed her original phrase, “technology that helps personalize teaching” to “technology that facilitates the personalization of teaching.” She also used Corpus Mate and another tool, Linggle, to check and change collocations (e.g., she saw that “broaden knowledge” was used more often than “expand knowledge” within Social Sciences). Additionally, based on her own linguistic introspection, María changed some linguistic items, such as “a mix of research methods” with “mixed research methods.” She also favored the passive voice in two sentences “to make the text sound more academic.”

Aurora used two different corpora, Corpus Mate and COCA, to check her language at a micro-level stage. By comparing the results from both sources, she was able to make a number of stylistic corrections. For example, she improved academic phrases, changing “must increase the understanding of” to “need for a greater understanding of”, checked subject-specific word combinations, e.g., “strategic role” instead of “strategic position”, refined cohesive devices, e.g., using “additionally” instead of “further”, and adjusted the voice of her text.

Paloma and Valeria also made micro-level explorations with COCA, but in their cases, they focused on lexical collocations. Paloma, for example, replaced “deeply impacted” with the more widely used “greatly impacted”, and Valeria proofread collocations such as “fully fabricated” and “minimal importance”, replacing them with more frequent collocations.

Finally, Begoña used Corpus Mate’s frequency charts to replace word combinations in a final micro-level examination; for example, “push factor” was changed to “driving factor.”

5.3. Survey Findings

The analysis of the Likert scale responses indicated predominantly positive evaluations, with all items—except for the “Difficulty with tool” section and one respondent’s score of 2 for BDDL use in academic work—receiving scores of at least 3 on a 5-point scale. To illustrate these findings in detail,

Table 1. Survey items and their mean (M) scores for the GenAI tools.

Section	Survey Item	M Score
Academic use	GenAI helps me with my academic writing	4.71
	GenAI is useful for pre-writing work	4.57
	GenAI is useful for drafting my texts	4.57
	GenAI helps me with re-writing/revising	4.71
	GenAI helps me with paraphrasing	4
	GenAI is helpful for other academic tasks	4.57
Linguistic profitability	GenAI helps to enhance my vocabulary	4.71
	GenAI helps to enhance my grammar	4.28
	GenAI helps to organize my texts better	4.42
	GenAI helps to correct my mistakes	4.57
	GenAI helps to improve my linguistic competence	4.42
	GenAI helps to improve my linguistic confidence	4
Difficulty with tool	My difficulties were technical/navigational	3.85
	My difficulties were linguistic/discoursal	3.85
Usability	I would recommend GenAI to my colleagues	4.14
	GenAI is more valuable than other tools	4.14
	I will use GenAI for writing in the future	4.42

reports the individual items and corresponding mean (M) scores for the evaluation of GenAI exclusively, as the assessment of the BDDL tools produced comparable results. This comparability is further reflected in

Table 2. M scores for the two types of tools by survey sections.

Section	GenAI Tools	BDDL Tools
Academic use	4.50	4.30
Linguistic profitability	4.40	4.14
Difficulty with tool	3.85	3.71
Usability	4.23	4.04

, which presents the mean satisfaction scores across the four sections of the survey.

The scores for BDDL were slightly lower than those for GenAI, primarily due to the items “I find linguistic patterns useful for academic writing” (M = 3.85) and “I would recommend BDDL to my colleagues” (M = 3.71). BDDL was also rated as more technically difficult to manage than GenAI (M = 4.14), while being considered easier to understand from a linguistic perspective (M = 3.28).

The overall survey results also varied according to participants’ academic positions and disciplinary backgrounds. With respect to academic status, the mean score derived from tenured faculty responses was higher than that of doctoral researchers (M = 4.41 vs. M = 3.74). With respect to disciplinary background, participants from Tourism assigned the highest mean scores (M = 4.54), followed by the Business Management professor (M = 4.40) and respondents from Education (M = 3.84). The lower overall mean in the latter case was largely attributable to the doctoral researcher in Education, who assigned lower scores to most items. All participants, except two in Tourism (the doctoral researcher and one of the tenured lecturers), assigned higher scores to GenAI than to BDDL.

Following the Likert-scale items, participants responded to three open-ended questions (see Section 4). For the first question, all participants mentioned ChatGPT and Co-Pilot, while Gemini was mentioned by one respondent. Regarding corpus tools, Corpus Mate and Linggle were preferred by most participants, with COCA cited by two. For the second question, all participants referred to research texts such as abstracts and articles, with two also including academic texts and course materials. In addition, all participants reported using GenAI for research syntheses, outlines, and brainstorming, while four noted its key role in linguistic revision.

Finally, their comments about advantages and disadvantages (question 3) included:

Doctoral researcher (Education): “The best thing is that writing can be significantly sped up with GenAI, and this is good for research writing. However, corpus information is more reliable than GenAI for real language use because it was directly written by humans, and the quality of GenAI can be bad and repetitive sometimes”.

Doctoral researcher (Tourism): “I think GenAI is the future”.

Tenured faculty (Education): “I think these tools entail a great qualitative step in the academic world. Like any other tools, their use determines their usefulness. It is something that is going to stay with us, and so, the sooner we integrate and control them, the better advantage we can take of them”.

Tenured faculty (Education): “I think GenAI is more dynamic and easier-to-use than corpora tools, which need technical training. GenAI, however, must be consistently supervised and corrected by human intervention because it can make many mistakes and compromise academic integrity. It could also hinder or diminish the writer’s linguistic competence in the long run if we don’t practice writing”.

Tenured faculty (Tourism): “The advantage is that they improve the process of revising and proofreading the English text before the article is submitted to an academic journal. The disadvantage is that you can end up overusing GenAI, which would significantly reduce the researchers’ capacity to improve their grammatical and lexical competences on their own because of their overdependence on a digital entity that does it better and faster than the L2 English writer”.

Tenured faculty (Tourism): “Good impression. I will use these tools more for sure”.

Tenured faculty (Business Management): “GenAI is very useful for research and academic activities. I use it every day now. This course has provided interesting ideas for improving their use and application. I think corpora are less interesting or necessary”.

6. Discussion

Based on this qualitative approach to participants’ written tasks, survey responses, and reflections during and after lectures and discussion, the findings generally show that participants view GenAI and BDDL tools positively for pre-writing and rewriting, while also emphasizing the importance of human judgment in the research writing process. Small variations were identified according to individual use, disciplinary approaches, and even academic status. For example, the value of these tools for academic writing activities is appreciated more by the Tourism participants and by tenured faculty members.

The results reported above reveal critical aspects of using GenAI for academic text enhancement which are discussed below in relation to existing literature.

6.1. Adoption of GenAI for Text Enhancement

GenAI is appraised positively by these postgraduate researchers primarily as a means of revising and improving the clarity, accuracy, and organization of their academic texts, coinciding with other studies (Kramar et al., 2024; Milton et al., 2024; Williams, 2024). Participants also reported using the tools to polish lexico-grammatical items and phraseology, and to address macro-level concerns such as overall cohesion, text structure, tone, disciplinary conventions, and intended audience. Importantly, these writers engaged in iterative rewriting, experimenting with prompts until outputs aligned with their intended meaning and authorial voice. Thus, they considered their oversight work important for the writing process, agreeing with other findings (e.g., Markey et al., 2024).

In addition, most participants extended GenAI use beyond revision into other writing tasks such as outlining, documenting, and drafting, which indicate resourceful dynamics in supporting different steps along the writing process. This extended use was particularly noteworthy among tenured faculty members, perhaps due to their greater experience with research writing. This observation correlates with these veteran participants’ higher survey scores, pointing to a more positive evaluation likely based on their recognition of various enriching practices.

6.2. Developing Prompt Literacy

The course facilitated the development of a more refined prompt literacy, as participants moved from basic tool use to more deliberate and targeted prompting strategies. Although all seven participants had prior experience with ChatGPT and Co-Pilot, they reported learning to phrase requests more specifically according to writing goals. While the more advanced researchers were more proactive in testing different affordances, novices also demonstrated acute awareness of issues such as accountability, bias, and “machine-sounding” language, concerns acknowledged in other L2 studies with GenAI (Crosthwaite & Sun, 2025). Active participation in online tasks and final writing assignments underscored their engagement with the process of learning to guide AI outputs more effectively. This type of partnership and co-agency with AI aligns with current analyses of successful academic L2 writing using critical AI literacies (Godwin-Jones, 2024; G. L. Liu et al., 2025).

6.3. Preserving Authorial Voice and Human-like Style

A central developmental insight is participants’ recognition that GenAI’s value extends beyond grammatical correctness to the preservation of authorial voice and stylistic authenticity. Initially, as Juan noted, some questioned whether AI could contribute more than producing correct English. Through practice, however, they began to see the importance of shaping texts so that they sounded human-like and reflective of their own academic identities. Additionally, various concerns were raised regarding the long-term usefulness of GenAI for linguistic enhancement if users over-relied on these tools. As some commented, this type of overdependence may be detrimental to their own linguistic progress, which calls for a pivotal focus on critical digital literacies using meta-cognitive and meta-linguistic thinking (Mizumoto, 2024; Pérez-Paredes et al., 2025).

Alongside prompting strategies, corpus-driven resources are valued for their role in refining lexical and phraseological nuance, even if these BDDL affordances were generally regarded as a bit less supportive than GenAI. A main reason for this less positive appraisal may be the more technical aspect involved in the management of these additional tools, as the survey revealed, even though these were easy-to-use concordancers and collocation/pattern finding utilities. Nonetheless, these resources were found helpful for linguistic enhancement, and two participants in Tourism rated them higher than GenAI. This observation coincides with how postgraduate L2 writers often grapple more with lexico-grammatical issues such as academic phraseology, collocations, and grammar, finding corpus tools useful for this type of linguistic enquiring at micro-level stages of text processing (Curado Fuentes, 2025b; Laso Martín & Comelles Pujadas, 2025; Yoon, 2016).

6.4. Discipline-Specific Focus

These participants demonstrate a capacity to direct GenAI and BDDL to serve their stylistic demands within their own disciplines and fields of work, employing distinctive strategies in their writing processes at various stages (as observed in Khuder, 2025). In their tasks, they requested clear and concise text output, and then critically evaluated AI to identify weaknesses in the texts according to their scientific expertise. For example, Begoña concentrated on linguistic accuracy for conceptual references, Juan on integrating relevant disciplinary background, and María and Aurora on corpus-informed validation of appropriate linguistic usage. These approaches illustrate how postgraduate L2 English writers strategically combine GenAI outputs with other resources to address their own challenges in academic writing (G. L. Liu et al., 2025; L. Liu & Wang, 2024).

The Tourism and Business Management researchers show more positive attitudes (in agreement with Curado Fuentes, 2025b). However, the Education participants also demonstrate effective tool deployment, and, with less buoyant impressions in the survey (especially in the case of the doctoral researcher), they reflect more critically on the artificial and limited aspects of GenAI-generated texts, coinciding with Jacob et al.’s (2023) observations in this discipline.

6.5. Balancing GenAI and BDDL

The participants’ feedback also reflects some tension between the accessibility of GenAI and the linguistic reliability of corpus tools. While GenAI was seen as easy to use but linguistically ambiguous, corpus tools were acknowledged as technically demanding but trusted for validating collocations and phraseological choices. This balance reflects their dual concern: maximizing efficiency without compromising linguistic precision. The finding echoes existing scholarship demonstrating that corpus-based training fosters more accurate and discipline-sensitive language use among postgraduate L2 students, assisting the combination of macro- and micro-level processing strategies (Hua et al., 2024).

6.6. Divergent Attitudes Toward Academic Validity

Some differences emerged in participants’ appreciation of AI-assisted academic writing, as deduced from in-class discussions, survey comments, and task developments. Three people (María and two professors in Education and Tourism) commented in the survey on the need to adopt a cautious position, warning against overreliance on GenAI due to potential risks for integrity, oversight, and linguistic competence. In contrast, other respondents expressed a more direct trust and reliance on these tools. Juan’s case was particularly noteworthy: he shifted from uncritical reliance on GenAI before the course to a more balanced and reflective use of both prompt engineering and corpus consultation. In turn, Pablo expressed little concern with potential negative aspects of GenAI, producing highly positive survey scores, whereas he commented on the lower value of BDDL as an assistant to his English writing. On the other hand, the two faculty members from Education enjoyed the combination of AI and BDDL affordances to target both research and teaching texts, working on initial research which they transformed into class material. These divergent perspectives illustrate how individual trajectories influence diverse attitudes toward AI integration, as seen in Khuder (2025).

6.7. Sensitivity to Linguistic Nuances

Most participants displayed great sensitivity to linguistic nuance in micro-level explorations. This resourceful linguistic analysis is likely due to their advanced English learning levels (B2 or above), contributing to their profiles as linguistic analysts. However, this linguistic level does not always correlate with such intensive linguistic explorations. For example, postgraduate participants in Experimental Sciences and Engineering tend to prioritize efficiency over nuance and rely more directly on AI for translation and text automation (L. Liu & Wang, 2024; Ruff et al., 2024). By contrast, the researchers in our study engaged more critically with AI for linguistic precision and academic writing conventions.

6.8. Integrating Creativity with the Tools

Ultimately, participants’ practices demonstrate that GenAI is most effective when integrated with other tools and approaches such as online corpus consultation, supporting rather than replacing human creativity. This aspect is critical for research writing in order to maintain heterogeneous discourses, multilingual perspectives, multi-disciplinary scopes, and cultural diversity (Kuteeva & Andersson, 2024). An example in this study has been the academic stance already maturing among fledgling researchers via task development and metacognitive reflections. Rather than accepting AI outputs at face value, these postgraduate writers can actively negotiate the tensions between efficiency and authenticity, correctness and authorial voice, and technological reliance and academic integrity. Their engagement illustrates the potential for postgraduate L2 researchers to harness GenAI critically and productively as part of complex academic writing processes.

7. Conclusions

This case study has provided evidence on how postgraduate EFL learners can integrate GenAI and BDDL tools for L2 writing in meaningful and specific ways within their fields.

Regarding the first research question of this study about their strategies and developmental facets, participants showcase their progress from using these technologies for routine tasks to employing advanced prompt engineering, authorial alignment, and discipline-specific/stylistic adaptation. These findings demonstrate that with targeted training, existing linguistic competences can be transformed into deliberate, critical, and effective writing practices assisted by GenAI and other digital tools.

Answering the second research question on their appraisal of these tools, the participants’ scores and appreciations for future use demonstrate an incremental use of these technologies for academic writing at postgraduate levels in these Social Sciences disciplines, aligning with current debates on human–AI collaboration and co-agencies in higher education (e.g., G. L. Liu et al., 2025). Beyond supporting research dissemination, these academics recognize the potential of the tools for miscellaneous academic developments while also acknowledging persistent challenges and potential risks, especially for academic L2 English competences, in agreement with other studies (e.g., Khuder, 2025). They also emphasize the use of appropriate academic phraseology and lexical collocations, which they can explore and learn using these digital tools, as found in other studies (e.g., Yoon, 2016). Therefore, their overall emphasis on combining linguistic precision with disciplinary expertise corroborates the crucial position of human agency in AI-supported writing.

A central limitation of this case study is its small sample size and the exclusive focus on participants who voluntarily enrolled in an ad hoc course. This selective approach constrains the generalizability of the findings regarding writing practices in the overall Social Sciences spectrum, where many faculty members and researchers may resist adopting these tools or may fail to recognize their potential benefits. Consequently, future research must address these gaps. Specifically, the observations made in this study should be tested against larger and more heterogeneous samples to validate and refine these preliminary insights.

Moreover, longitudinal studies are essential to capture the evolving role of GenAI in academic writing, examining not only which tools are employed but also the underlying motivations, strategies, and variations in their use across distinct academic communities. Systematic approaches are necessary to fully understand GenAI’s impact on academic L2 English writing at all levels of higher education. As Raitskaya and Tikhonova (2025) note, GenAI presents substantial potential to enhance cognitive processes that underpin research methodology and scholarly inquiry. Yet, the practical realization of these benefits remains inconsistent, highlighting a critical need for research that investigates long-term effects, discipline-specific practices, and theoretical frameworks addressing cognitive processing and meta-linguistic reasoning in relation to these tools.