Knowing What You Don’t Know: Why Professional Doctorate Students Should Tread Carefully with AI Research Assistants

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.

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