AI-Driven Innovations in Qualitative Research and Data Analysis

The field of qualitative research and data analysis is undergoing significant transformations with the integration of artificial intelligence (AI) technologies. Researchers are exploring new ways to leverage AI to enhance data collection, analysis, and interpretation, while also addressing the challenges and limitations of these technologies. A key direction in this field is the development of human-AI collaboration frameworks, which aim to combine the strengths of human judgment and AI-driven insights to improve research outcomes. Another area of innovation is the application of AI to unstructured data, such as text, images, and audio, to extract latent structure and enable more effective computation and analysis. Noteworthy papers in this area include:

  • One paper introduces a pragmatic approach to data analysis, workflow, and computation in qualitative research, highlighting the potential of AI to support longstanding methodological commitments.
  • Another paper presents a case for computing on unstructured data, proposing a new paradigm that enables the analytical power of structured computation to be applied to unstructured data. Overall, these innovations have the potential to significantly advance the field of qualitative research and data analysis, enabling researchers to tackle complex problems and extract new insights from large and diverse datasets.

Sources

Data selves and identity theft in the age of AI

Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews

A Case for Computing on Unstructured Data

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