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<p>Following the emergence of disruptive technologies and technology-driven trends, academics and researchers in particular face consequential changes in how they can conduct their work. In this paper, I argue that as researchers incorporate Generative AI tools to varying degrees, the activities they undertake for research will be altered, potentially leading to new practices and skillsets. I describe the adoption of Large Language Models (LLMs) as a transformative experience to argue that, when individuals encounter significant changes in habits, skills, and tasks, it fundamentally alters qualitative features of research activities. This reveals a rational choice problem for researchers, academics, and students alike: the substantial changes in research activities complicate the comparison of current and future competencies. In the wake of significant technological shifts, they are not well-positioned to decide whether replacing the current methods of researching and writing with those augmented by LLMs is better and preferable, as they cannot envision what it would be like to lose competence in some of their current skills.</p>

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Decision-making for technology-led transformation in research practices

  • 期刊论文

  • 发表日期：

    2025年8月26日

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  • Öykü Ulusoy

摘要

Following the emergence of disruptive technologies and technology-driven trends, academics and researchers in particular face consequential changes in how they can conduct their work. In this paper, I argue that as researchers incorporate Generative AI tools to varying degrees, the activities they undertake for research will be altered, potentially leading to new practices and skillsets. I describe the adoption of Large Language Models (LLMs) as a transformative experience to argue that, when individuals encounter significant changes in habits, skills, and tasks, it fundamentally alters qualitative features of research activities. This reveals a rational choice problem for researchers, academics, and students alike: the substantial changes in research activities complicate the comparison of current and future competencies. In the wake of significant technological shifts, they are not well-positioned to decide whether replacing the current methods of researching and writing with those augmented by LLMs is better and preferable, as they cannot envision what it would be like to lose competence in some of their current skills.