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Paradigm shifts: exploring AI's influence on qualitative inquiry and analysis
1Teesside University International Business School, TU Online, Teesside University, Middlesbrough, United Kingdom.
Frontiers in Research Metrics and Analytics
|December 20, 2024
Summary
Artificial Intelligence (AI) offers faster qualitative data analysis but may conflict with interpretivism. Human researchers should retain final interpretation due to AI
Area of Science:
- Social Sciences
- Computer Science
Background:
- Technology integration in qualitative research is common.
- Artificial Intelligence (AI) in qualitative data analysis poses challenges to interpretivism.
Purpose of the Study:
- To explore the use of AI technologies like Natural Language Processing (NLP) in qualitative data analysis.
- To examine the potential conflicts between AI-driven analysis and the assumptions of interpretivism.
- To discuss the implications for research paradigms and the role of the human researcher.
Main Methods:
- Exploration of AI applications in qualitative research, specifically NLP for data analysis.
- Discussion of the debates surrounding the validity and ethics of automated qualitative analysis.
- Argument for retaining human oversight in interpretation within an interpretivist framework.
Main Results:
- AI, including NLP, is increasingly used for qualitative data analysis, offering speed.
- Concerns exist regarding AI's alignment with interpretivist assumptions due to its rule-based nature.
- AI may overlook nuances in human communication crucial for qualitative interpretation.
Conclusions:
- Human researchers should maintain contextual understanding and final interpretation in qualitative research.
- Partial automation with AI can enhance rigor and efficiency in qualitative studies.
- The rise of AI may necessitate new research paradigms, such as a 'computational' paradigm, for digital researchers.
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