Paradigm shifts: exploring AI's influence on qualitative inquiry and analysis.

Frontiers in research metrics and analytics Pub Date : 2024-12-05 eCollection Date: 2024-01-01 DOI:10.3389/frma.2024.1331589
Ryan Thomas Williams
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Abstract

Technology has mostly been embraced in qualitative research as it has not directly conflicted with qualitative methods' paradigmatic underpinnings. However, Artificial Intelligence (AI), and in particular the process of automating the analysis of qualitative research, has the potential to be in conflict with the assumptions of interpretivism. The short article aims to explore how AI technologies, such as Natural Language Processing (NLP), have started to be used to analyze qualitative data. While this can speed up the analysis process, it has also sparked debates within the interpretive paradigm about the validity and ethics of these methods. I argue that research underpinned by the human researcher for contextual understanding and final interpretation should mostly remain with the researcher. AI might overlook the subtleties of human communication. This is because automated programmes with clear rules and formulae do not work well-under interpretivism's assumptions. Nevertheless, AI may be embraced in qualitative research in a partial automation process that enables researchers to conduct rigorous, rapid studies that more easily incorporate the many benefits of qualitative research. It is possible that AI and other technological advancements may lead to new research paradigms that better underpin the contemporary digital researcher. For example, we might see the rise of a "computational" paradigm. While AI promises to enhance efficiency and rigor in data analysis, concerns remain about its alignment with interpretivism.

范式转变:探索人工智能对定性调查和分析的影响。
技术在定性研究中被广泛接受,因为它与定性方法的范例基础没有直接冲突。然而,人工智能(AI),特别是自动化定性研究分析的过程,有可能与解释主义的假设发生冲突。这篇短文旨在探讨自然语言处理(NLP)等人工智能技术如何开始用于分析定性数据。虽然这可以加快分析过程,但它也引发了解释范式内关于这些方法的有效性和伦理学的争论。我认为,由人类研究人员对语境的理解和最终解释所支持的研究,应该主要由研究人员来完成。人工智能可能会忽略人类交流的微妙之处。这是因为具有明确规则和公式的自动化程序在解释主义的假设下不能很好地工作。然而,人工智能可能会在定性研究中被部分自动化的过程所接受,这使得研究人员能够进行严格、快速的研究,更容易地结合定性研究的许多好处。人工智能和其他技术进步可能会带来新的研究范式,更好地支撑当代数字研究人员。例如,我们可能会看到“计算”范式的兴起。虽然人工智能有望提高数据分析的效率和严谨性,但人们仍然担心它与解释主义的一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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