Artificial intelligence (AI) is rapidly reshaping higher education. Tools such as ChatGPT are increasingly being used in research, teaching, and academic writing. For professional doctorate students, who often juggle demanding professional roles alongside doctoral study, the appeal is obvious: AI seems to offer speed, efficiency, and support with tasks ranging from literature summarising to theme generation in qualitative analysis.

Yet for all its apparent benefits, uncritical use of AI poses serious risks, particularly for those undertaking practice-based and qualitative inquiry. Professional doctorates are not just about producing a thesis. They are about becoming a scholarly professional: someone who can generate, interpret, and apply knowledge in complex, real-world contexts. This process requires reflexivity, epistemological awareness, and a willingness to grapple with ambiguity. These are precisely the areas where AI cannot substitute for human thought.

Professional Doctorates and the learning journey

Unlike traditional PhDs, which are often designed to advance disciplinary theory, professional doctorates focus on developing professionals as scholar-practitioners. Students typically bring significant experience and expertise from their fields. The challenge is to transform that professional knowing into applied scholarly knowing: to move from “knowing-in-action” to “knowing-through-research.”

Qualitative inquiry plays a central role in this transformation. It requires researchers to sit with uncertainty, to interpret meaning, and to reflect critically on their own assumptions. As Morgan (2023) argues, qualitative analysis is not a mechanical process but a dialogue between researcher, data, and theory. Wachinger et al. (2024) add that it demands sensitivity to context, attentiveness to less obvious insights, and ethical discernment.

In other words, qualitative research is not just a methodology, it is a developmental process. It teaches students how to engage with complexity and ambiguity, skills that are essential to becoming a researching professional.

The temptation of AI

Against this backdrop, it is easy to see why AI is attractive. Tools like ChatGPT can generate fluent text, propose analytical categories, and even code datasets at speed. For busy professionals, these capabilities can feel like a lifeline.

But here lies the danger. Professional doctorates are about the labour of interpretation: the slow, iterative, and sometimes uncomfortable process of making sense of data, theory, and practice. If AI is allowed to take over too much of this intellectual labour, students risk outsourcing the very learning that the doctorate is supposed to cultivate.

As Chubb et al. (2022) note, the pressure to “speed up to keep up” in academia can encourage shallow engagement. AI makes it easy to produce something that looks coherent, but coherence is not the same as depth. Kulkarni et al. (2024) warn that scholars risk losing their capacity to theorise if they increasingly rely on machine-generated insights rather than grappling with the messiness of data themselves.

The result can be what van Veggel et al. (2024) call “analytic complacency”: a passivity where the researcher becomes a consumer of AI outputs rather than a producer of meaning. For doctoral researchers, this is a missed opportunity. The discomfort of wrestling with interpretation is not a bug of qualitative research, it is the very process through which intellectual growth occurs.

Authorship, integrity, and voice

There are also ethical and epistemological dimensions to consider. Authorship in qualitative research is not just about typing words on a page. It is about taking responsibility for interpretation, making your positionality visible, and articulating your scholarly voice.

AI, by contrast, has no positionality. It cannot justify its assumptions, disclose its reasoning, or take ethical responsibility. Using AI uncritically risks erasing the very qualities that make qualitative research trustworthy. Tang et al. (2024) argue that transparency in declaring AI use is essential to maintain academic integrity. Without disclosure, readers are misled about the origins of ideas and interpretations.

For professional doctorate students, whose work is closely tied to their professional identities, this issue is especially acute. If your findings are shaped primarily by AI outputs, what happens to the authenticity of your practitioner voice? The danger is that AI-generated text may be fluent and plausible, but it is also generic, reproducing dominant discourses rather than challenging them. This can stifle originality, silence marginal perspectives, and undermine the social relevance of your research.

Towards responsible use of AI

So, should professional doctorate students avoid AI altogether? Not necessarily. AI can have a role, but only when used critically and transparently. The key is to ensure that the researcher remains at the centre of knowledge production.

Emerging frameworks offer guidance. Colleagues and I (van Veggel et al., 2025) propose an Integrated Prompt Framework that structures AI use around planning, prompting, evaluating, and procedural reflection. This ensures that AI functions as a supportive scaffold rather than a replacement for human thought. Similarly, Nguyen-Trung’s (2024) Guided AI Thematic Analysis treats AI as a brainstorming partner while insisting that final interpretations remain firmly in the hands of the researcher.

Central to both approaches is the idea of AI literacy. Students must understand not only what AI can do, but also its limitations: its training data, biases, and inability to navigate ethical or cultural nuance. They must learn when not to use AI, particularly in tasks that require deep reflexivity or sensitivity to participant voices.

Final reflections

For professional doctorate students, the question is not simply whether to use AI, but how to use it responsibly. AI can help with efficiency, organisation, and even prompting new perspectives. But it cannot replace the hard, transformative work of interpretation.

The professional doctorate is designed to produce reflective, ethical, and epistemologically aware practitioners who can contribute to knowledge in their fields. This transformation cannot be achieved on autopilot. It requires curiosity, reflexivity, and a willingness to wrestle with complexity.

AI may assist with aspects of the journey, but it cannot take it for you. At the end of the day, the doctorate is about who you are becoming as a scholar-practitioner. That identity, voice, and responsibility are yours alone.

Key takeaways for Professional Doctorate students

• AI is a tool, not a substitute: it can support, but not replace, human interpretation.
• Qualitative inquiry demands reflexivity: do not let AI obscure your positionality and voice.
• Transparency is essential: always declare AI use to maintain scholarly integrity.
• Beware of analytic complacency: do not allow AI outputs to replace your critical engagement.
• Develop AI literacy: understand what AI can and cannot do, and use it selectively.

Further Reading

• Chubb, J., Cowling, P., & Reed, D. (2022). Speeding up to keep up: Exploring the use of AI in the research process. AI & Society, 37, 1439–1457. https://doi.org/10.1007/s00146-021-01259-0
• Kulkarni, M., Mantere, S., Vaara, E., van den Broek, E., Pachidi, S., Glaser, V. L., Gehman, J., Petriglieri, G., Lindebaum, D., Cameron, L. D., Rahman, H. A., Islam, G., & Greenwood, M. (2024). The future of research in an artificial intelligence-driven world. Journal of Management Inquiry, 33(3), 207–229. https://doi.org/10.1177/10564926231219622
• Morgan, D. L. (2023). Exploring the use of artificial intelligence for qualitative data analysis: The case of ChatGPT. International Journal of Qualitative Methods, 22, 1–10. https://doi.org/10.1177/16094069231211248
• Nguyen-Trung, K. (2024). ChatGPT in thematic analysis: Can AI become a research assistant in qualitative research? PsyArXiv Preprints. https://doi.org/10.31234/osf.io/gvcnp
• Tang, A., Li, K. K., Kwok, K. O., Cao, L., Luong, S., & Tam, W. (2024). The importance of transparency: Declaring the use of generative artificial intelligence in academic writing. Journal of Nursing Scholarship, 56(3), 314–318. https://doi.org/10.1111/jnu.12938
• van Veggel, N., Engward, H., Birks, M. and Mills, P.J. (2025) ‘Using AI in Grounded Theory research – a proposed framework for a ChatGPT-based research assistant’. SocArxiv. Available at: https://doi.org/10.31235/osf.io/a2dc4_v2.
• Wachinger, J., Bärnighausen, K., Schäfer, L. N., Scott, K., & McMahon, S. A. (2024). Prompts, pearls, imperfections: Comparing ChatGPT and a human researcher in qualitative data analysis. Qualitative Health Research. https://doi.org/10.1177/10497323241244669