Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants

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While these tools offer considerable utility for professional doctorate students, especially those engaged in practice-based, qualitative inquiry, their use also presents serious epistemological, ethical, and pedagogical challenges. This article critically examines the promise and limitations of AI in the context of professional doctorates, with a specific focus on qualitative research. Drawing from recent scholarship, it highlights how overreliance on AI can bypass crucial aspects of intellectual development, compromise reflexivity, and obscure researcher accountability. This paper argues for a principled and transparent use of AI, guided by structured frameworks, to ensure that the human researcher remains central to meaning-making and scholarly authorship. The proposed stance foregrounds epistemic agency and underscores the importance of learning through complexity and discomfort in the doctoral journey." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1411", "name": "Knowing what you don’t know: why professional doctorate students should..." } } ] } Home Browse Knowing what you don’t know: why professional doctorate students should... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article van Veggel N. Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.12688/f1000research.173204.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Opinion Article Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] Nieky van Veggel https://orcid.org/0000-0001-6738-6989 Nieky van Veggel https://orcid.org/0000-0001-6738-6989 PUBLISHED 18 Dec 2025 Author details Author details Anglia Ruskin University, Chelmsford, England, CM1 1SQ, UK Nieky van Veggel Roles: Conceptualization, Funding Acquisition, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the Research on Research, Policy & Culture gateway. Abstract Abstract* The integration of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, into academic research is accelerating. While these tools offer considerable utility for professional doctorate students, especially those engaged in practice-based, qualitative inquiry, their use also presents serious epistemological, ethical, and pedagogical challenges. This article critically examines the promise and limitations of AI in the context of professional doctorates, with a specific focus on qualitative research. Drawing from recent scholarship, it highlights how overreliance on AI can bypass crucial aspects of intellectual development, compromise reflexivity, and obscure researcher accountability. This paper argues for a principled and transparent use of AI, guided by structured frameworks, to ensure that the human researcher remains central to meaning-making and scholarly authorship. The proposed stance foregrounds epistemic agency and underscores the importance of learning through complexity and discomfort in the doctoral journey. READ ALL READ LESS Keywords Artificial Intelligence in Research, Professional Doctorates, Qualitative Inquiry, Research Ethics, Research Integrity Corresponding Author(s) Nieky van Veggel ( [email protected] ) Close Corresponding author: Nieky van Veggel Competing interests: No competing interests were disclosed. Grant information: East Suffolk and North Essex NHS Foundation Trust [418278]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 van Veggel N. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: van Veggel N. Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.12688/f1000research.173204.1 ) First published: 18 Dec 2025, 14 :1411 ( https://doi.org/10.12688/f1000research.173204.1 ) Latest published: 18 Dec 2025, 14 :1411 ( https://doi.org/10.12688/f1000research.173204.1 ) Introduction The rise of artificial intelligence (AI) in higher education research has been swift and far-reaching. In particular, large language models (LLMs) such as ChatGPT have emerged as accessible, general-purpose tools capable of generating fluent academic text, suggesting analytical categories, and assisting in the early stages of qualitative data analysis. These technologies are being explored across disciplines, and their adoption is accelerating, fuelled by institutional demands for efficiency, student curiosity, and a pervasive cultural narrative of technological progress. For professional doctorate students, typically mid- or late-career professionals undertaking research within their own practice contexts, AI presents a tempting proposition. Balancing complex work responsibilities, tight time constraints, and academic expectations, these students often seek tools that can streamline writing, organise literature, or support qualitative analysis. AI appears to offer precisely this kind of support: fast, accessible, and easy to use. But this apparent solution comes with significant and under-examined risks. This article argues that while AI can provide practical assistance in professional doctorate research, its uncritical use poses a fundamental threat to the learning journey that these programmes are designed to enable. Unlike traditional PhDs, which are often aimed at preparing academics to contribute to disciplinary knowledge, professional doctorates are concerned with developing professionals as researching professionals , practitioners capable of generating, interpreting, and applying knowledge within complex, real-world settings ( Taylor, 2007 ; Wellington & Sikes, 2006 ). The aim is not simply to conduct research, but to become a particular kind of scholar-practitioner: reflexive, ethical, and epistemologically aware. This transformation hinges on the intellectual and moral labour of engaging with data, theory, and practice in context. Qualitative research, widely used in professional doctorate projects, demands this labour. It invites uncertainty, rewards criticality, and foregrounds interpretation over automation. While AI can mimic surface-level analysis, it cannot engage in the nuanced, situated, and reflexive sense-making that lies at the heart of qualitative inquiry. Nor can it assume responsibility for methodological choices or ethical consequences. The purpose of this article, therefore, is not to discourage the use of AI outright, but to caution against using it in ways that bypass the developmental, interpretive, and ethical demands of professional doctorate research. Drawing on current literature and emerging AI-integrated research frameworks, this manuscript explores the intersection between AI assistance and professional learning. It demonstrates how over-reliance on AI tools can risk epistemic detachment, erode scholarly authorship, and undermine the very transformation that the professional doctorate is designed to cultivate. Ultimately, this article advocates for a critically engaged, ethically aware approach to AI, one that acknowledges the affordances of these tools while insisting on the centrality of human interpretation, voice, and judgement. In a research culture increasingly shaped by automation, professional doctorate students must remain not just users of tools, but authors of meaning. This is the path to becoming a researching professional, and it cannot be taken on autopilot. Professional doctorates Professional doctorates differ fundamentally from traditional research doctorates in both purpose and orientation. While PhD programmes often aim to produce disciplinary knowledge that contributes to theoretical advancement, professional doctorates are designed to develop practitioners who can generate, interpret, and apply knowledge within complex, real-world settings ( Taylor, 2007 ; Fulton et al., 2011 ). This emphasis on practical impact and reflective inquiry situates professional doctorate students at the intersection of academic and workplace knowledge cultures, requiring them to navigate not only research methods but shifting identities and epistemological paradigms. As Wellington and Sikes (2006) highlight, students undertaking professional doctorates often enter with considerable professional expertise and authority but may feel uncertain in academic spaces. They describe this as a “tight compartment” in which students must reconcile their practitioner identity with their emerging role as researcher. Unlike undergraduate or early postgraduate learners, professional doctorate students are not blank slates; they bring with them habits of practice, ways of knowing, and assumptions formed through years of experience. The challenge, and opportunity, of the professional doctorate lies in facilitating a shift from “knowing-in-action” to “knowing-about-action” and ultimately “knowing through research.” This transition is not only cognitive but ontological, requiring students to reorient themselves toward knowledge as something to be interrogated rather than applied. Qualitative research is particularly well suited to this process of transformation. At its core, qualitative inquiry is interpretive, iterative, and relational. It invites the researcher to attend not just to what people do or say, but to how meaning is constructed, negotiated, and situated within broader social, organisational, or cultural systems. This level of engagement demands deep reflexivity, the capacity to question one’s own assumptions, values, and position in relation to the research context. For professional doctorate students, this means not only conducting interviews or analysing documents, but reflecting on the meanings that underpin their own practice and how these intersect with the lives and experiences of others. Morgan (2023) underscores the importance of this interpretive dimension, noting that qualitative research involves an ongoing dialogue between the researcher, the data, and the emergent theoretical insights. This dialogue is non-linear and often uncomfortable. It resists easy answers and demands the kind of sustained attention that fosters intellectual humility and depth. Similarly, Wachinger et al. (2024) observe that qualitative analysis is more than a mechanical process of theme identification; it involves judgement, contextual awareness, and ethical discernment. These are not skills that can be downloaded or replaced by AI systems, no matter how fluent or responsive. Moreover, qualitative research methods teach students how to sit with ambiguity, how to recognise that multiple truths can coexist, that categories are porous, and that certainty is often an illusion. This is particularly important in professional doctorate work, where research questions are grounded in real-world complexity and often involve navigating contested values, institutional constraints, or interpersonal dynamics. AI tools, by contrast, are not designed to navigate such ambiguity. They are optimised to produce confident, coherent responses, even when the underlying issue is uncertain or contested. As a result, there is a risk that students who rely too heavily on AI will miss the opportunity to learn how to think critically through complexity. In this context, the qualitative research process becomes both a methodology and a developmental tool. It supports students not only in producing new insights but in becoming the kind of scholar-practitioner who can make sense of, and act within, the layered realities of professional life. The process of conducting qualitative research, reading richly, listening closely, analysing deeply, writing reflexively, thus becomes central to the formation of the researching professional. It is in this space that students learn not just how to do research, but how to be as researchers. The risks of outsourcing thinking The convenience offered by AI tools like ChatGPT is undeniable. These systems can generate text rapidly ( Khlaif et al., 2023 ), summarise long documents, and even identify patterns in datasets ( Burger et al., 2023 ). For professional doctorate students juggling full-time employment, research timelines, and complex life responsibilities, such capabilities are understandably attractive. However, these apparent benefits come with considerable epistemological and developmental risks, particularly when students begin to substitute AI-assisted outputs for their own intellectual labour. Professional doctorate programmes aim not merely to produce research outputs, but to foster critical, independent thinkers capable of engaging with the complexities of professional practice. Central to this development is the process of inquiry itself, posing difficult questions, analysing contradictions, and reflecting on one’s assumptions. Overreliance on AI shortcuts can obscure this process. As Chubb et al. (2022) argue, the institutional pressures to “speed up to keep up” risk encouraging superficial engagement with research problems in favour of rapid production. In such an environment, AI becomes a tool for deliverables rather than a partner in thinking. Kulkarni et al. (2024) echo this concern, warning that automation in research may gradually erode scholars’ capacity to theorise. Rather than developing new insights, students may increasingly default to AI-generated interpretations that appear plausible but lack grounding in the specificities of context or data. This can result in a kind of epistemic detachment, where students accept interpretations they have not critically examined and whose assumptions they may not fully understand. This phenomenon is especially problematic in qualitative inquiry. As previously discussed, qualitative research requires slow thinking, sensitivity to context, and a willingness to dwell in ambiguity. When ChatGPT or similar systems are used to carry out coding, theme generation, or even early interpretation, they may produce outputs that seem legitimate but are in fact unmoored from the situatedness that qualitative inquiry demands. As Wachinger et al. (2024) found in their comparative study, AI-generated analyses often miss less obvious but potentially more meaningful themes, particularly those that challenge dominant discourses or arise from marginalised voices. This points to a broader cognitive risk. AI may promote what van Veggel et al. (2025) describe as “analytic complacency”, a state in which the researcher becomes a passive recipient of insight rather than its active producer. This is antithetical to the kind of learning that the professional doctorate is meant to facilitate. Doctoral-level research is supposed to stretch students intellectually, to expose them to complexity, contradiction, and uncertainty. It is in grappling with difficult data and unresolved tensions that genuine insight emerges. Moreover, repeated reliance on AI to initiate or structure interpretation may inhibit the development of key research capacities: critical reading, theoretical framing, conceptual abstraction, and methodological judgement. These are slow-forming capabilities, cultivated through iterative practice and reflection. Delegating these tasks to AI too early or too often can short-circuit this developmental process, leaving students with the appearance of progress but lacking the epistemic maturity to defend or elaborate their findings. The consequence is a hollowing out of the researcher’s role. Instead of becoming skilled interpreters of complex data, students may become technicians managing a series of AI-generated outputs, shaping prompts, fine-tuning models, and copying outputs into dissertations with minimal critical engagement. This not only jeopardises research quality but undermines the core pedagogical function of the professional doctorate: to develop professionals who can think, reason, and act with scholarly depth. In sum, while AI can certainly assist with efficiency and surface-level organisation, the cost of outsourcing thinking is high. It risks replacing learning with automation, depth with convenience, and transformation with simulation. For professional doctorate students, the choice is not merely about tool use, it is about what kind of researcher, and what kind of professional, they are becoming. Authorship, integrity, and epistemic agency At the heart of all research lies an ethical relationship between the researcher and the knowledge they produce. In qualitative inquiry, this relationship is particularly acute: meaning is not discovered, but co-constructed through interpretation, reflection, and engagement with context. Authorship, therefore, is not simply about who types the words, it is about who takes responsibility for interpretation, whose voice guides the analysis, and whose perspective shapes the narrative. As AI becomes more capable of generating text that mimics human reasoning, these fundamental questions become both more urgent and more complex. The issue of authorship and integrity in the context of AI is receiving increasing attention across disciplines. Tang et al. (2024) argue that transparent declaration of AI use is a non-negotiable requirement for maintaining academic credibility. This is not merely a matter of formality. When AI tools are used without disclosure, they obscure the boundaries between human reasoning and machine assistance, misleading examiners, supervisors, and readers about the origins of the work. Such practices erode trust in academic outputs and threaten the core values of honesty and accountability in scholarship. For professional doctorate students, the stakes are even higher. Their research is often closely linked to their workplace roles and professional identities. As such, questions of authorship are not just academic, they are intimately tied to how they understand and represent their professional knowledge. Using AI to produce interpretations without deep engagement risks divorcing the findings from the practitioner’s lived reality, weakening both the rigour and the relevance of the research. Moreover, qualitative inquiry requires the researcher to be visible in the text, not in a self-indulgent way, but as a situated, reflexive interpreter of meaning. This visibility is essential for establishing trustworthiness and for acknowledging the partial, positioned nature of all interpretation. AI, by contrast, is fundamentally decontextualised. It cannot disclose its assumptions, explain its reasoning, or justify its conclusions. It lacks positionality. As Hosseini et al. (2024) make clear, AI cannot be held ethically accountable for the claims it produces. It cannot respond to the concerns of participants, engage in moral reasoning, or revise its analysis in light of new understanding. This has profound implications for epistemic agency. The use of AI risks shifting the researcher’s role from meaning-maker to manager of outputs, someone who curates, edits, and assembles rather than thinks, reflects, and questions. While this shift may appear efficient, it undermines the developmental goal of the professional doctorate: to cultivate scholarly practitioners who can navigate ambiguity, theorise practice, and communicate their findings with clarity and conviction. As Mantere and Vaara (2024) point out, authorship is not only about responsibility, it is about voice. Academic writing is a space in which students articulate who they are as scholars. It is where they take a stance, frame their contributions, and position themselves within broader conversations. When students rely too heavily on AI to construct their narratives, they risk losing that voice. The resulting work may be grammatically fluent and structurally coherent, but it lacks the authenticity and reflexivity that distinguishes robust qualitative research. Furthermore, the homogenising tendencies of AI raise concerns about originality. AI systems are trained on existing data, meaning they are more likely to reproduce dominant discourses than to challenge them. This is particularly problematic in qualitative research, which often seeks to illuminate marginalised perspectives, expose taken-for-granted assumptions, and generate alternative framings of reality. As Wachinger et al. (2024) observed, AI-generated analyses tend to follow conventional patterns and miss opportunities for theoretical or political insight. If students begin to internalise these patterns as authoritative, they may become less inclined to explore unorthodox, disruptive, or critical lines of inquiry, thereby limiting both the originality and social relevance of their work. The use of AI has profound implications for authorship, integrity, and scholarly identity ( Yeo, 2024 ). For professional doctorate students, who are learning not only how to conduct research but how to become researching professionals, it is vital that they remain at the centre of the knowledge production process. Authorship is not just a technical attribution, it is a moral and epistemological stance. It signals ownership, responsibility, and voice. These are the very qualities that define the professional doctorate, and they cannot be automated. Expanding practice to include frameworks for responsible use While the risks of AI use in qualitative research are significant, rejecting these tools outright would be both impractical and intellectually limiting. The goal is not to eliminate AI from the research process, but to integrate it in ways that preserve the epistemological integrity and educational function of professional doctorate work. This requires not only caution, but also structure. Emerging frameworks are now offering pathways to support critical, ethical, and developmentally appropriate uses of AI within qualitative research. One of the most promising contributions comes from van Veggel et al. (2025) , whose Integrated Prompt Framework offers a pragmatic structure for engaging with AI tools like ChatGPT across four key domains: planning , prompting , evaluating , and procedural use. This model emphasises that AI should not drive the research but support the researcher in thinking more expansively, asking better questions, and refining their methodological awareness. At each phase, the researcher is encouraged to interrogate both the process and the output, asking not only “What does the AI produce?” but “How does this align with my epistemological stance, research context, and ethical commitments?” In particular, the evaluating component of this framework is crucial. It asks students to pause and assess AI-generated content against criteria such as trustworthiness, theoretical congruence, and interpretive nuance. This step supports the development of critical reflexivity, helping students to move beyond surface-level use of AI and to engage with it as a prompt for deeper analysis. Rather than accepting AI output at face value, students are encouraged to triangulate it with their own insights, theoretical readings, and the specificities of their research data. Other frameworks, such as the Guided AI Thematic Analysis (GAITA) proposed by Nguyen-Trung (2024) , similarly advocate for keeping the researcher firmly in control. In GAITA, ChatGPT is used as a brainstorming partner in the early stages of coding, helping to surface alternative perspectives or overlooked themes. However, all final interpretations are generated, verified, and articulated by the human researcher. The AI serves as a cognitive aid, not a surrogate for judgment. These models also foreground the concept of AI literacy, a vital capacity that professional doctorate students must now cultivate. As Turobov et al. (2024) argue, responsible use of AI depends not only on methodological discipline but on an understanding of how these systems work: their training data, limitations, biases, and vulnerabilities. Without this awareness, students may unknowingly allow AI to introduce distortions, particularly around issues of power, representation, or cultural framing. Developing AI literacy also means understanding when not to use AI. For instance, AI is ill-suited for tasks that involve emotional nuance, complex ethical tensions, or culturally specific meaning systems, domains where qualitative research often operates. Students must learn to identify these boundaries and be able to justify their methodological decisions in light of them. This is not simply a technical choice; it is a professional judgement that reflects their emerging identity as a researcher. Importantly, these frameworks are not intended to replace academic supervision or peer review but to scaffold reflective practice. They provide a vocabulary and a set of checkpoints that students and supervisors can use to discuss the ethical and epistemological implications of AI use. This helps re-centre the educational focus of the professional doctorate: to develop thoughtful, critically engaged scholars who can navigate complexity, rather than simply manage outputs. In this sense, responsible AI use becomes an opportunity rather than a threat. It invites professional doctorate students to develop new skills, confront new dilemmas, and engage with emerging scholarly practices, while holding fast to the principles that underpin meaningful, ethical research. AI can prompt, suggest, and support, but the researcher must always decide, justify, and interpret. That is where the real learning happens, and where professional identity is formed. Conclusions As artificial intelligence becomes an increasingly prominent feature of the research landscape, professional doctorate students face both an opportunity and a responsibility. The opportunity lies in the ability to engage with powerful tools that can support aspects of the research process, through drafting, summarising, coding, or generating analytical prompts. The responsibility, however, is to ensure that these tools are used in ways that preserve the developmental purpose of the professional doctorate and the intellectual integrity of qualitative research. This article has argued that professional doctorates are not merely about producing a thesis or acquiring a credential, they are about becoming a different kind of professional. One who is capable of asking difficult questions, grappling with ambiguity, theorising practice, and constructing knowledge that is both grounded in experience and conceptually rigorous. This transformation cannot be automated. It depends on a process of critical engagement with data, theory, and self. It is in the struggle to interpret, the tension between practice and abstraction, and the willingness to reflect that the student becomes a researching professional. AI, in this context, is not a threat if used wisely. But its dangers arise when it is treated as a surrogate for thinking, a shortcut through the messy work of analysis, or a replacement for the student’s own voice. The seductive fluency of ChatGPT and similar tools can create a false sense of mastery, leading students to mistake coherence for depth, or convenience for insight. When this happens, the learning journey is hollowed out, and the purpose of the professional doctorate is undermined. What is needed, therefore, is not rejection of AI but a reframing of its role. AI can function as a supportive scaffold, prompting questions, surfacing alternative perspectives, or helping to organise ideas. But it must never replace the human work of interpretation. The frameworks discussed in this article, from van Veggel et al.’s Integrated Prompt Framework to Nguyen-Trung’s Guided AI Thematic Analysis, offer practical guidance for engaging with AI critically and reflectively. These approaches foreground the role of the researcher as the primary agent in knowledge production, and emphasise AI literacy, ethical judgement, and methodological rigour. Professional doctorate students must also develop the confidence to say no to AI when appropriate. Not every task benefits from automation. In fact, some of the most important aspects of qualitative research, reflexivity, ethical sensitivity, theoretical abstraction, resist delegation. These are the moments that demand human presence, vulnerability, and thoughtfulness. It is in these moments that learning becomes transformation. The broader implications extend beyond individual theses or projects. As AI continues to reshape the norms and practices of academia, there is a risk that efficiency becomes the overriding goal, displacing care, critique, and curiosity. Professional doctorate students, as boundary-crossers between practice and academia, are uniquely positioned to resist this drift. They can model what it means to do research that is rigorous, relevant, and reflexive, research that uses tools wisely but never forgets who is ultimately responsible. In the end, becoming a researching professional is not about mastering a set of techniques or even completing a doctoral programme. It is about becoming someone who can stand behind their ideas, account for their interpretations, and contribute ethically to their field. No AI can do that. And that is why the researcher, human, situated, and thinking, must remain at the centre of professional doctorate research. Data availability No data are associated with this article. Acknowledgements The author is grateful to Prof Hilary Engward and Dr Sally Goldspink for fruitful discussions on this topic which led to this work. A preprint version of this paper is available on the Open Science Framework: https://doi.org/10.35542/osf.io/8cbvu_v2 . 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 18 Dec 2025 ADD YOUR COMMENT Comment Author details Author details Anglia Ruskin University, Chelmsford, England, CM1 1SQ, UK Nieky van Veggel Roles: Conceptualization, Funding Acquisition, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information East Suffolk and North Essex NHS Foundation Trust [418278]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 18 Dec 2025, 14:1411 https://doi.org/10.12688/f1000research.173204.1 Copyright © 2025 van Veggel N. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article van Veggel N. Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.12688/f1000research.173204.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 18 Dec 2025 Views 0 Cite How to cite this report: Thong CL. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r452058 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-452058 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 11 Feb 2026 Chee Ling Thong , UCSI University, Kuala Lumpur, Malaysia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190996.r452058 Please see my comments below: "In a research culture increasingly shaped by automation, professional doctorate students must remain not just users of tools, but authors of meaning. This is the path to becoming a researching professional, and ... Continue reading READ ALL Please see my comments below: "In a research culture increasingly shaped by automation, professional doctorate students must remain not just users of tools, but authors of meaning. This is the path to becoming a researching professional, and it cannot be taken on autopilot" "Professional doctorate programmes aim not merely to produce research outputs, but to foster critical, independent thinkers capable of engaging with the complexities of professional practice. Central to this development is the process of inquiry itself, posing difficult questions, analysing contradictions, and reflecting on one’s assumptions. Overreliance on AI shortcuts can obscure this process." Kindly provide the most recent references and citations pertaining to the current research culture in the utilization of artificial intelligence (AI) and automation. I would appreciate it if authors could elaborate on how AI is currently being employed in traditional doctoral programs, providing specific case studies across various domains and compare it with professional doctorates. In order to make the article scientifically sound, I am of the opinion that more detailed study should be provided by referring to the current literature - since there is no data associated with this article. Is the topic of the opinion article discussed accurately in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Partly Are arguments sufficiently supported by evidence from the published literature? Partly Are the conclusions drawn balanced and justified on the basis of the presented arguments? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: AI in education/doctoral education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Thong CL. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r452058 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-452058 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Shankar PR and Paczek MK. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r452059 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-452059 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Feb 2026 Pathiyil Ravi Shankar , IMU University, Kuala Lumpur, Malaysia Ms Katarzyna Paczek , IMU University, Kuala Lumpur, Malaysia, Kuala Lumpur, Malaysia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190996.r452059 This article is well written and presents an argument that the uncritical use of AI is a threat to the development of doctoral researcher capabilities. It cites the case of qualitative research, where these capabilities are developed through in-depth engagement ... Continue reading READ ALL This article is well written and presents an argument that the uncritical use of AI is a threat to the development of doctoral researcher capabilities. It cites the case of qualitative research, where these capabilities are developed through in-depth engagement in the qualitative research process. The article appropriately describes aspects of qualitative analysis including researcher reflexivity, contextual understanding, attention to nuance and rigour. It also well highlights implications of AI use in issues related to authorship, integrity and professional workplace and scholarly identity. It provides an alternative view of AI use by including published emerging frameworks to integrate and support use of AI in qualitative research. We are not comfortable with this statement ‘Unlike undergraduate or early postgraduate learners, professional doctorate students are not blank slates;’ No learner regardless of the level can be regarded as a blank slate according to modern learning theories. The author may need to better explain a professional doctorate and its difference from a PhD as this may not be a term in common use globally. Areas which can be strengthened: Although this is an opinion article, it provides limited evidence from published literature regarding how AI is currently used in qualitative research, or any desirable or undesirable effects of AI use in qualitative doctoral research identified by other researchers. Including more evidence will provide the reader with a comprehensive picture and help to make the presented argument more robust. The term ‘overreliance’ of AI can be better explained or defined and provide a practical example of overreliance. Provide a practical example for responsible use of AI in qualitative research. This will assist the reader in implementation. Is the topic of the opinion article discussed accurately in the context of the current literature? Partly Are all factual statements correct and adequately supported by citations? Yes Are arguments sufficiently supported by evidence from the published literature? Partly Are the conclusions drawn balanced and justified on the basis of the presented arguments? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Health professions education, research, educational research We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Shankar PR and Paczek MK. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r452059 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-452059 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Tatipang DP. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r452061 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-452061 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Feb 2026 Devilito Prasetyo Tatipang , Universitas Negeri Manado, Tondano, North Sulawesi, Indonesia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190996.r452061 This article is quite good in describing the context of AI and how this technology is used, basically the main criticism that the author needs to pay attention to is that it covers aspects regarding the lack of empirical ... Continue reading READ ALL This article is quite good in describing the context of AI and how this technology is used, basically the main criticism that the author needs to pay attention to is that it covers aspects regarding the lack of empirical support, a reductive view of AI, and the idealization of qualitative research. The author needs to add empirical evidence and a balance of argument in order for the article to be scientifically sound. Through the opinion of this article, AI (especially LLMs like ChatGPT) is reducively seen as a substitute for human thinking, ignoring its potential as augmented intelligence. This is outdated because recent research shows AI supports the research process through human-in-the-loop interactions. Therefore, the authors need to reformulate the research question from the perspective of augmented intelligence by including the latest empirical data (e.g. autonomous vs. assistive mode experiments). Add a systematic literature review of human-AI interaction and augmented cognition in the methods and discussion sections, and most importantly explicitly state at the limits that the risks of outsourcing thinking come from epistemic design, not the technology itself. In addition, the opinions in this article are mostly theoretical in nature with no empirical evidence, comparisons, or quantitative data, making hypothetical claims (e.g. AI lowers educational performance). Unfortunately, this will undermine scientific credibility. Therefore, the author needs to add a comparative research approach (experimental/quasi-experimental) comparing students with different levels of AI support, measuring educational performance, analysis, and reflexivity quantitatively, or conducting a systematic literature review with findings as hypotheses in the methodology. Furthermore, the author can also use an AI (permitted, high-risk, inappropriate) use case matrix at the qualitative stage to further support, complement the vignette examples with AI prompts, outputs, and audits. From the author's presentation in the article, it can be understood that the view of this article idealizes qualitative research as slow, reflective, exclusively human ignoring the advances of CAQDAS, topic modeling, NLP ethnography, and computational grounded theory over the past decade. In fact, it is a romantic epistemology in terms of method, not scientific. What the author needs to do is integrate this computational method into a theoretical-methodological framework, show via text/algorithm data, and measure the interpretive coherence of how AI supports reflection. However, if this is not possible, the author can outsmart it by adding a systematic review of contemporary literature in the methodology section, and there is a need for recognition/limitation that qualitative research has been digitally integrated. Is the topic of the opinion article discussed accurately in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Are arguments sufficiently supported by evidence from the published literature? Partly Are the conclusions drawn balanced and justified on the basis of the presented arguments? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: AI-Enchanted ELT, TESOL, Educational Technology, Plurallingual Education, Translanguaging I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Tatipang DP. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r452061 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-452061 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Leogrande A. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r449935 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-449935 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Jan 2026 Angelo Leogrande , LUM University Giuseppe Degennaro, Casamassima, Italy Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190996.r449935 The article is well-written. However the following modifications are necessary: Technologically reductive conception of AI. A technologically reductive conception of AI is promoted by the article. This is because the article views the current state ... Continue reading READ ALL The article is well-written. However the following modifications are necessary: Technologically reductive conception of AI. A technologically reductive conception of AI is promoted by the article. This is because the article views the current state of AI, especially the current generation of LLMs, from the perspective of text generation and/or AI as the substitute for human reason. This is, however, very outdated given the current research being undertaken. This can be remedied by formulating the research question from the perspective of augmented intelligence. This means that the research question can be addressed from the point of view of the support provided by the current generation of LLMs to the research process. This should be accompanied by the provision of data and the latest technology to empirically support such claims. Confusion between misuse and the nature of the technology. The article has a conceptual error in confusing the impact of the technology itself and the impact of its use, in that the danger of “outsourcing thinking” is attributed to AI in general when, in fact, the danger is in its misuse design. In scientific terms, this is a causal error in that the error is in the technology but in the lack of epistemic standards in its application in learning and scientific inquiries. In order for this problem to be solved, empirical separation between the impact of AI and the impact of its misuse is needed, preferably through experiments or quasi-experiments that collect data on the impact of AI in autonomous, assistive, or human-in-the-loop modes of interaction in terms of quality, reflexivity, and learning. In the absence of this information, integration of the systematic literature review of human-AI interaction and augmented cognition in the method and discussion sections should be done in that the danger of outsourcing thinking is in epistemic design and not in AI itself, which should also be explicitly stated in the limitations and conclusion sections. Lack of empirical comparison. There is a great lack of empirical support in the article, which remains largely theoretical without experimental support in terms of empirical evidence, comparison, and quantitative data. Specifically, the article lacks the empirical support that the use of artificial intelligence in a structured manner by students results in lower levels of educational performance compared to non-AI-based educational and research contexts, which makes many findings largely hypothetical. This issue would be resolved by the addition of a comparative research approach, which would be based on empirical observations or experiments comparing students with different levels of support from AI (e.g., support, analysis, or automation). This would be done based on educational performance, analysis, and reflexivity, using both quantitative and qualitative approaches. Alternatively, a literature review would offer a more robust approach, and the findings would be considered hypotheses in the methodology, with the empirical limitations explored in the discussion and conclusion sections. Non-scientific idealization of qualitative research. The article has a weak, idealized, and non-scientific view of qualitative research, describing it as necessarily slow, reflective, exclusively human, and by definition unautomatable, and ignoring the last several decades of advances in computer-assisted qualitative data analysis software, topic modeling, NLP ethnographies, and computational grounded theory. This approach represents epistemological romanticism, rather than a scientifically grounded one. In order to overcome this weakness, the analysis should integrate these methods into its theoretical and methodological approach, showing through text data, algorithms, and measures of text coherence and interpretive validity how artificial intelligence can be used to support and extend, rather than supplant, the process of reflection and theoretical development. In the absence of the possibility of actual implementation, the article should include a systematic literature review, with the approach in the methodology and limitations acknowledging that contemporary qualitative research has already extensively integrated with digital and computational technologies. Epistemological error: confusion between meaning production and authoring. The epistemological error of confusing meaning production with authoring, and the assumption that only the human subject has the ability to produce meaning, has to be addressed in the analysis by incorporating a theoretical paradigm that aligns with the theories of extended and distributed cognition and the theory of sociomateriality, where meaning is produced as a result of the interaction of the hybrid system, where human actors and technological artifacts collaborate in the production of cognitive meaning. In this theoretical paradigm, artificial intelligence lacks intentional understanding but may play an operative role in meaning production as part of the distributed cognitive system. The epistemological error of confusing meaning production with authoring may be remedied by incorporating experimental data, human and AI interaction procedures, and meaning co-production measures into the analysis, or by incorporating a systematic review of the literature into the analysis, depending on the availability of the former. Failure to address the opposite risk: Cognitive poverty without AI. Artificial intelligence, when used appropriately, would serve not only as an aid to research but also as an instrument for cognitive amplification, leading to an increase in exploratory work, synthesis, and problematization, rather than the opposite effect. This would ideally need to be addressed with comparative research on the quality of the research produced with the aid of artificial intelligence, contrasted with the quality of research produced without the aid of artificial intelligence. Alternatively, the literature on cognitive amplification, workload, and research technology should be systematically reviewed. - Ethics of fear as opposed to the governance of ethics. The ethics framework described in the article is essentially one of fear as opposed to a governance framework of AI. From a scientific point of view, a good ethics framework needs to be converted into working and verifiable processes like model auditing, traceability of prompts, interaction versioning, and co-authoring mechanisms, which need to be backed by the use of metrics of transparency, reproducibility, and accountability. Ideally, this needs to be done through empirical implementation. If this is not feasible, a systematic review of the literature regarding the governance of AI, open science, and responsible research needs to be done. This needs to place the methodologies within the framework and address the absence of the methodologies in the limitations and conclusions sections. Is the topic of the opinion article discussed accurately in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Are arguments sufficiently supported by evidence from the published literature? Yes Are the conclusions drawn balanced and justified on the basis of the presented arguments? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Economics, Econometrics, Machine Learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Leogrande A. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r449935 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-449935 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: George B. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r449932 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-449932 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Jan 2026 Babu George , Alcorn State University, Lorman, Mississippi, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190996.r449932 This opinion article provides a timely and important warning about the uncritical use of large language models in professional doctorate research, particularly for qualitative inquiry. The central thesis is coherent and valuable: professional doctorates aim to develop "researching professionals," and ... Continue reading READ ALL This opinion article provides a timely and important warning about the uncritical use of large language models in professional doctorate research, particularly for qualitative inquiry. The central thesis is coherent and valuable: professional doctorates aim to develop "researching professionals," and heavy AI reliance risks short-circuiting the epistemic and ethical learning that qualitative research is meant to cultivate. The article appropriately foregrounds reflexivity, accountability, authorship, and epistemic agency as core stakes, moving beyond a productivity-focused view of AI tools. STRENGTHS The article makes a clear normative case for keeping human interpretation central to professional doctorate research. It appropriately identifies several distinct risks associated with AI use: pedagogical concerns about learning and development, methodological concerns about research quality, and integrity concerns related to authorship and accountability. The article constructively gestures toward structured frameworks for AI use, including the Integrated Prompt Framework (planning, prompting, evaluating, procedural use) and Guided AI Thematic Analysis (GAITA). This signals a pragmatic intent to guide rather than simply prohibit AI use. The paper successfully contextualizes the issue within professional doctorate education, recognizing that these programs serve mid- to late-career professionals with specific developmental goals distinct from traditional research doctorates. AREAS FOR IMPROVEMENT 1. Argument Structure and Scope The article sometimes conflates several distinct claims without clearly separating which risks are primarily pedagogical versus methodological versus integrity-related. This makes it difficult for readers to prioritize concerns or identify which safeguards address which specific risks. The argument is framed broadly across professional doctorates and qualitative research, which risks overgeneralizing across different methods (e.g., grounded theory, thematic analysis, discourse analysis) that have different vulnerabilities and quality criteria. The conclusion reiterates the central warning but does not sharpen it into testable or decision-grade guidance about what to do, when, and why for students and supervisors. 2. Evidence and Balance While the article cites a range of recent scholarship to support concerns about superficiality, theorizing erosion, and missed themes, it rarely specifies what counts as adequate evidence for those outcomes in real doctoral settings. The discussion of AI "homogenising tendencies" and risks to marginalized perspectives is plausible but would be stronger if it engaged with at least one credible counterposition. For instance, could AI-supported exploration broaden rather than narrow analytic sensitivity when used under careful human oversight? Since this is an opinion article, the lack of primary data is acceptable, but the claims would still benefit from clearer warranting, explicit assumptions, and defined boundaries of applicability. 3. Clarity and Conceptual Precision Key terms like "overreliance," "outsourcing thinking," and "analytic complacency" are rhetorically strong but under-operationalized, making it difficult for readers to self-audit their own practice. The paper treats "AI" largely as ChatGPT-style LLM assistance but does not consistently distinguish between uses with very different risk profiles, such as grammar polishing, literature triage, coding suggestions, memo writing, versus interpretive claims about participant meaning. The authorship discussion is valuable but would be improved by explicitly connecting AI disclosure norms to concrete doctoral assessment practices (viva expectations, audit trails, appendices, examiner standards) rather than staying at principle level. SPECIFIC RECOMMENDATIONS To strengthen this contribution and make it more actionable for the professional doctorate community, I recommend: 1. Add a concrete "use-case matrix" with three columns: permitted with conditions, high-risk, and not appropriate, tailored to qualitative research stages (recruitment materials, interview guides, coding, theme development, interpretation, write-up). 2. Define thresholds for "overreliance" using observable indicators, such as: inability to justify a code, inability to trace a theme to data excerpts, or inability to explain why a prompt was constructed in a particular way. 3. Provide at least one worked example showing AI involvement plus the human audit trail, including prompts, AI outputs, what was rejected, and how final interpretations were grounded in data excerpts. Even a short illustrative vignette would significantly enhance the paper's practical utility. 4. Strengthen engagement with counterarguments by acknowledging limited, defensible benefits (e.g., generating alternative sensitizing concepts) and specifying the safeguards that prevent those benefits from becoming methodological substitution. 5. Expand the framework section into a step-by-step protocol that a supervisor can actually assess, including documentation templates and suggested disclosure language, building on the article's mention of the Integrated Prompt Framework and GAITA. Is the topic of the opinion article discussed accurately in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Partly Are arguments sufficiently supported by evidence from the published literature? Partly Are the conclusions drawn balanced and justified on the basis of the presented arguments? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Digital transformation, AI in higher education, international education, strategy, and change management. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT George B. Reviewer Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r449932 ) The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-449932 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 18 Dec 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 5 Version 1 18 Dec 25 read read read read read Babu George , Alcorn State University, Lorman, USA Angelo Leogrande , LUM University Giuseppe Degennaro, Casamassima, Italy Devilito Prasetyo Tatipang , Universitas Negeri Manado, Tondano, Indonesia Pathiyil Ravi Shankar , IMU University, Kuala Lumpur, Malaysia Ms Katarzyna Paczek , IMU University, Kuala Lumpur, Malaysia, Kuala Lumpur, Malaysia Chee Ling Thong , UCSI University, Kuala Lumpur, Malaysia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Thong C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 11 Feb 2026 | for Version 1 Chee Ling Thong , UCSI University, Kuala Lumpur, Malaysia 0 Views copyright © 2026 Thong C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Please see my comments below: "In a research culture increasingly shaped by automation, professional doctorate students must remain not just users of tools, but authors of meaning. This is the path to becoming a researching professional, and it cannot be taken on autopilot" "Professional doctorate programmes aim not merely to produce research outputs, but to foster critical, independent thinkers capable of engaging with the complexities of professional practice. Central to this development is the process of inquiry itself, posing difficult questions, analysing contradictions, and reflecting on one’s assumptions. Overreliance on AI shortcuts can obscure this process." Kindly provide the most recent references and citations pertaining to the current research culture in the utilization of artificial intelligence (AI) and automation. I would appreciate it if authors could elaborate on how AI is currently being employed in traditional doctoral programs, providing specific case studies across various domains and compare it with professional doctorates. In order to make the article scientifically sound, I am of the opinion that more detailed study should be provided by referring to the current literature - since there is no data associated with this article. Is the topic of the opinion article discussed accurately in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Partly Are arguments sufficiently supported by evidence from the published literature? Partly Are the conclusions drawn balanced and justified on the basis of the presented arguments? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise AI in education/doctoral education I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Thong CL. Peer Review Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r452058) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-452058 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Shankar P et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Feb 2026 | for Version 1 Pathiyil Ravi Shankar , IMU University, Kuala Lumpur, Malaysia Ms Katarzyna Paczek , IMU University, Kuala Lumpur, Malaysia, Kuala Lumpur, Malaysia 0 Views copyright © 2026 Shankar P et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This article is well written and presents an argument that the uncritical use of AI is a threat to the development of doctoral researcher capabilities. It cites the case of qualitative research, where these capabilities are developed through in-depth engagement in the qualitative research process. The article appropriately describes aspects of qualitative analysis including researcher reflexivity, contextual understanding, attention to nuance and rigour. It also well highlights implications of AI use in issues related to authorship, integrity and professional workplace and scholarly identity. It provides an alternative view of AI use by including published emerging frameworks to integrate and support use of AI in qualitative research. We are not comfortable with this statement ‘Unlike undergraduate or early postgraduate learners, professional doctorate students are not blank slates;’ No learner regardless of the level can be regarded as a blank slate according to modern learning theories. The author may need to better explain a professional doctorate and its difference from a PhD as this may not be a term in common use globally. Areas which can be strengthened: Although this is an opinion article, it provides limited evidence from published literature regarding how AI is currently used in qualitative research, or any desirable or undesirable effects of AI use in qualitative doctoral research identified by other researchers. Including more evidence will provide the reader with a comprehensive picture and help to make the presented argument more robust. The term ‘overreliance’ of AI can be better explained or defined and provide a practical example of overreliance. Provide a practical example for responsible use of AI in qualitative research. This will assist the reader in implementation. Is the topic of the opinion article discussed accurately in the context of the current literature? Partly Are all factual statements correct and adequately supported by citations? Yes Are arguments sufficiently supported by evidence from the published literature? Partly Are the conclusions drawn balanced and justified on the basis of the presented arguments? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Health professions education, research, educational research We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. reply Respond to this report Responses (0) Shankar PR and Paczek MK. Peer Review Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r452059) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-452059 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Tatipang D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Feb 2026 | for Version 1 Devilito Prasetyo Tatipang , Universitas Negeri Manado, Tondano, North Sulawesi, Indonesia 0 Views copyright © 2026 Tatipang D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This article is quite good in describing the context of AI and how this technology is used, basically the main criticism that the author needs to pay attention to is that it covers aspects regarding the lack of empirical support, a reductive view of AI, and the idealization of qualitative research. The author needs to add empirical evidence and a balance of argument in order for the article to be scientifically sound. Through the opinion of this article, AI (especially LLMs like ChatGPT) is reducively seen as a substitute for human thinking, ignoring its potential as augmented intelligence. This is outdated because recent research shows AI supports the research process through human-in-the-loop interactions. Therefore, the authors need to reformulate the research question from the perspective of augmented intelligence by including the latest empirical data (e.g. autonomous vs. assistive mode experiments). Add a systematic literature review of human-AI interaction and augmented cognition in the methods and discussion sections, and most importantly explicitly state at the limits that the risks of outsourcing thinking come from epistemic design, not the technology itself. In addition, the opinions in this article are mostly theoretical in nature with no empirical evidence, comparisons, or quantitative data, making hypothetical claims (e.g. AI lowers educational performance). Unfortunately, this will undermine scientific credibility. Therefore, the author needs to add a comparative research approach (experimental/quasi-experimental) comparing students with different levels of AI support, measuring educational performance, analysis, and reflexivity quantitatively, or conducting a systematic literature review with findings as hypotheses in the methodology. Furthermore, the author can also use an AI (permitted, high-risk, inappropriate) use case matrix at the qualitative stage to further support, complement the vignette examples with AI prompts, outputs, and audits. From the author's presentation in the article, it can be understood that the view of this article idealizes qualitative research as slow, reflective, exclusively human ignoring the advances of CAQDAS, topic modeling, NLP ethnography, and computational grounded theory over the past decade. In fact, it is a romantic epistemology in terms of method, not scientific. What the author needs to do is integrate this computational method into a theoretical-methodological framework, show via text/algorithm data, and measure the interpretive coherence of how AI supports reflection. However, if this is not possible, the author can outsmart it by adding a systematic review of contemporary literature in the methodology section, and there is a need for recognition/limitation that qualitative research has been digitally integrated. Is the topic of the opinion article discussed accurately in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Are arguments sufficiently supported by evidence from the published literature? Partly Are the conclusions drawn balanced and justified on the basis of the presented arguments? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise AI-Enchanted ELT, TESOL, Educational Technology, Plurallingual Education, Translanguaging I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Tatipang DP. Peer Review Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r452061) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-452061 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Leogrande A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Jan 2026 | for Version 1 Angelo Leogrande , LUM University Giuseppe Degennaro, Casamassima, Italy 0 Views copyright © 2026 Leogrande A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The article is well-written. However the following modifications are necessary: Technologically reductive conception of AI. A technologically reductive conception of AI is promoted by the article. This is because the article views the current state of AI, especially the current generation of LLMs, from the perspective of text generation and/or AI as the substitute for human reason. This is, however, very outdated given the current research being undertaken. This can be remedied by formulating the research question from the perspective of augmented intelligence. This means that the research question can be addressed from the point of view of the support provided by the current generation of LLMs to the research process. This should be accompanied by the provision of data and the latest technology to empirically support such claims. Confusion between misuse and the nature of the technology. The article has a conceptual error in confusing the impact of the technology itself and the impact of its use, in that the danger of “outsourcing thinking” is attributed to AI in general when, in fact, the danger is in its misuse design. In scientific terms, this is a causal error in that the error is in the technology but in the lack of epistemic standards in its application in learning and scientific inquiries. In order for this problem to be solved, empirical separation between the impact of AI and the impact of its misuse is needed, preferably through experiments or quasi-experiments that collect data on the impact of AI in autonomous, assistive, or human-in-the-loop modes of interaction in terms of quality, reflexivity, and learning. In the absence of this information, integration of the systematic literature review of human-AI interaction and augmented cognition in the method and discussion sections should be done in that the danger of outsourcing thinking is in epistemic design and not in AI itself, which should also be explicitly stated in the limitations and conclusion sections. Lack of empirical comparison. There is a great lack of empirical support in the article, which remains largely theoretical without experimental support in terms of empirical evidence, comparison, and quantitative data. Specifically, the article lacks the empirical support that the use of artificial intelligence in a structured manner by students results in lower levels of educational performance compared to non-AI-based educational and research contexts, which makes many findings largely hypothetical. This issue would be resolved by the addition of a comparative research approach, which would be based on empirical observations or experiments comparing students with different levels of support from AI (e.g., support, analysis, or automation). This would be done based on educational performance, analysis, and reflexivity, using both quantitative and qualitative approaches. Alternatively, a literature review would offer a more robust approach, and the findings would be considered hypotheses in the methodology, with the empirical limitations explored in the discussion and conclusion sections. Non-scientific idealization of qualitative research. The article has a weak, idealized, and non-scientific view of qualitative research, describing it as necessarily slow, reflective, exclusively human, and by definition unautomatable, and ignoring the last several decades of advances in computer-assisted qualitative data analysis software, topic modeling, NLP ethnographies, and computational grounded theory. This approach represents epistemological romanticism, rather than a scientifically grounded one. In order to overcome this weakness, the analysis should integrate these methods into its theoretical and methodological approach, showing through text data, algorithms, and measures of text coherence and interpretive validity how artificial intelligence can be used to support and extend, rather than supplant, the process of reflection and theoretical development. In the absence of the possibility of actual implementation, the article should include a systematic literature review, with the approach in the methodology and limitations acknowledging that contemporary qualitative research has already extensively integrated with digital and computational technologies. Epistemological error: confusion between meaning production and authoring. The epistemological error of confusing meaning production with authoring, and the assumption that only the human subject has the ability to produce meaning, has to be addressed in the analysis by incorporating a theoretical paradigm that aligns with the theories of extended and distributed cognition and the theory of sociomateriality, where meaning is produced as a result of the interaction of the hybrid system, where human actors and technological artifacts collaborate in the production of cognitive meaning. In this theoretical paradigm, artificial intelligence lacks intentional understanding but may play an operative role in meaning production as part of the distributed cognitive system. The epistemological error of confusing meaning production with authoring may be remedied by incorporating experimental data, human and AI interaction procedures, and meaning co-production measures into the analysis, or by incorporating a systematic review of the literature into the analysis, depending on the availability of the former. Failure to address the opposite risk: Cognitive poverty without AI. Artificial intelligence, when used appropriately, would serve not only as an aid to research but also as an instrument for cognitive amplification, leading to an increase in exploratory work, synthesis, and problematization, rather than the opposite effect. This would ideally need to be addressed with comparative research on the quality of the research produced with the aid of artificial intelligence, contrasted with the quality of research produced without the aid of artificial intelligence. Alternatively, the literature on cognitive amplification, workload, and research technology should be systematically reviewed. - Ethics of fear as opposed to the governance of ethics. The ethics framework described in the article is essentially one of fear as opposed to a governance framework of AI. From a scientific point of view, a good ethics framework needs to be converted into working and verifiable processes like model auditing, traceability of prompts, interaction versioning, and co-authoring mechanisms, which need to be backed by the use of metrics of transparency, reproducibility, and accountability. Ideally, this needs to be done through empirical implementation. If this is not feasible, a systematic review of the literature regarding the governance of AI, open science, and responsible research needs to be done. This needs to place the methodologies within the framework and address the absence of the methodologies in the limitations and conclusions sections. Is the topic of the opinion article discussed accurately in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Are arguments sufficiently supported by evidence from the published literature? Yes Are the conclusions drawn balanced and justified on the basis of the presented arguments? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Economics, Econometrics, Machine Learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Leogrande A. Peer Review Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r449935) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1411/v1#referee-response-449935 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 George B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Jan 2026 | for Version 1 Babu George , Alcorn State University, Lorman, Mississippi, USA 0 Views copyright © 2026 George B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This opinion article provides a timely and important warning about the uncritical use of large language models in professional doctorate research, particularly for qualitative inquiry. The central thesis is coherent and valuable: professional doctorates aim to develop "researching professionals," and heavy AI reliance risks short-circuiting the epistemic and ethical learning that qualitative research is meant to cultivate. The article appropriately foregrounds reflexivity, accountability, authorship, and epistemic agency as core stakes, moving beyond a productivity-focused view of AI tools. STRENGTHS The article makes a clear normative case for keeping human interpretation central to professional doctorate research. It appropriately identifies several distinct risks associated with AI use: pedagogical concerns about learning and development, methodological concerns about research quality, and integrity concerns related to authorship and accountability. The article constructively gestures toward structured frameworks for AI use, including the Integrated Prompt Framework (planning, prompting, evaluating, procedural use) and Guided AI Thematic Analysis (GAITA). This signals a pragmatic intent to guide rather than simply prohibit AI use. The paper successfully contextualizes the issue within professional doctorate education, recognizing that these programs serve mid- to late-career professionals with specific developmental goals distinct from traditional research doctorates. AREAS FOR IMPROVEMENT 1. Argument Structure and Scope The article sometimes conflates several distinct claims without clearly separating which risks are primarily pedagogical versus methodological versus integrity-related. This makes it difficult for readers to prioritize concerns or identify which safeguards address which specific risks. The argument is framed broadly across professional doctorates and qualitative research, which risks overgeneralizing across different methods (e.g., grounded theory, thematic analysis, discourse analysis) that have different vulnerabilities and quality criteria. The conclusion reiterates the central warning but does not sharpen it into testable or decision-grade guidance about what to do, when, and why for students and supervisors. 2. Evidence and Balance While the article cites a range of recent scholarship to support concerns about superficiality, theorizing erosion, and missed themes, it rarely specifies what counts as adequate evidence for those outcomes in real doctoral settings. The discussion of AI "homogenising tendencies" and risks to marginalized perspectives is plausible but would be stronger if it engaged with at least one credible counterposition. For instance, could AI-supported exploration broaden rather than narrow analytic sensitivity when used under careful human oversight? Since this is an opinion article, the lack of primary data is acceptable, but the claims would still benefit from clearer warranting, explicit assumptions, and defined boundaries of applicability. 3. Clarity and Conceptual Precision Key terms like "overreliance," "outsourcing thinking," and "analytic complacency" are rhetorically strong but under-operationalized, making it difficult for readers to self-audit their own practice. The paper treats "AI" largely as ChatGPT-style LLM assistance but does not consistently distinguish between uses with very different risk profiles, such as grammar polishing, literature triage, coding suggestions, memo writing, versus interpretive claims about participant meaning. The authorship discussion is valuable but would be improved by explicitly connecting AI disclosure norms to concrete doctoral assessment practices (viva expectations, audit trails, appendices, examiner standards) rather than staying at principle level. SPECIFIC RECOMMENDATIONS To strengthen this contribution and make it more actionable for the professional doctorate community, I recommend: 1. Add a concrete "use-case matrix" with three columns: permitted with conditions, high-risk, and not appropriate, tailored to qualitative research stages (recruitment materials, interview guides, coding, theme development, interpretation, write-up). 2. Define thresholds for "overreliance" using observable indicators, such as: inability to justify a code, inability to trace a theme to data excerpts, or inability to explain why a prompt was constructed in a particular way. 3. Provide at least one worked example showing AI involvement plus the human audit trail, including prompts, AI outputs, what was rejected, and how final interpretations were grounded in data excerpts. Even a short illustrative vignette would significantly enhance the paper's practical utility. 4. Strengthen engagement with counterarguments by acknowledging limited, defensible benefits (e.g., generating alternative sensitizing concepts) and specifying the safeguards that prevent those benefits from becoming methodological substitution. 5. Expand the framework section into a step-by-step protocol that a supervisor can actually assess, including documentation templates and suggested disclosure language, building on the article's mention of the Integrated Prompt Framework and GAITA. Is the topic of the opinion article discussed accurately in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Partly Are arguments sufficiently supported by evidence from the published literature? Partly Are the conclusions drawn balanced and justified on the basis of the presented arguments? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Digital transformation, AI in higher education, international education, strategy, and change management. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) George B. Peer Review Report For: Knowing what you don’t know: why professional doctorate students should tread carefully with AI research assistants [version 1; peer review: 5 approved with reservations] . F1000Research 2025, 14 :1411 ( https://doi.org/10.5256/f1000research.190996.r449932) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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