The advancements in AI/LLMs introduces potential opportunities and risks to the craft of qualitative research – in this symposium, panelists will first reflect on the foundations of qualitative research, and then consider if, how and when the use of AI can be a "friend" or "foe". Join us for exciting and informative debate in Chicago!
Panelists:
Kevin Corley (Imperial College London),
Vern Glaser (University of Alberta),
Karen Golden-Biddle (Boston University),
Hila Lifshitz-Assaf (Warwick Business School) and
Anne Smith (University of Tennessee)
Organizers: Susan Hilbolling (Aarhus University) and Renate Kratochvil (BI Norway)
When: Sunday, August 11, 2024, 11:30 – 13:00 CT (GMT-5/UTC-5)
Where: Fairmont: Rouge Room
Sponsors: SAP, CTO and RM
Abstract
With the rapid development in artificial intelligence (AI), specifically the capabilities of large language models (LLMs), the question may not be if but rather how AI will or can enhance the research process. In this panel, we focus specifically on the work of a qualitative researcher. Qualitative methods research is particularly suited for leveraging the power of LLMs, given its focus on words (as opposed to numbers). While these new technologies available may create new opportunities for innovating research methods, it may also challenge the fundamentals of qualitative methods. Therefore, before we can have an opinion on whether AI and qualitative methods are "friends or foes," in this panel we want to discuss the core skills and strengths of a qualitative research scholar. Reflecting on these foundations can help us assess which and how new technologies can help us learn and conduct qualitative research and when their use poses a risk of lowering the quality (in the short or long term). This session aims to seek informed answers to questions such as: What are the foundations of qualitative methods? What makes qualitative research and the researchers conducting it unique? How can AI complement, threaten, or augment qualitative data collection, data analysis (including coding), theorizing, and composing qualitative research? Is there a risk of AI/LLMs eroding the unique skills of qualitative researchers? How can we develop and maintain unique qualitative research skills, such as the creative leap, to produce absolute novelty? How do we consider using AI/LLMs in light of the multiplicity of ontologies, epistemologies, and methodologies in qualitative research? How should we (re-)consider the quality criteria used to assess qualitative research (e.g., credibility, transparency) when evaluating research that includes using LLMs?
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Susan Hilbolling
Aarhus University, Denmark
Aarhus
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