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Artificial Intelligence to Support Qualitative Data Analysis: Promises, Approaches, Pitfalls.

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    Artificial intelligence (AI) shows promise for qualitative data analysis (QDA), but current large language models like ChatGPT-4 require significant prompt engineering for tasks like summarization. While AI has a long history in QDA, researchers must understand its limitations and ethical implications.

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    Area of Science:

    • Computer Science
    • Social Sciences
    • Research Methodology

    Background:

    • Artificial intelligence (AI) has a long history of application in qualitative data analysis (QDA), spanning over 25 years.
    • Recent advancements in AI, particularly large language models, offer new possibilities for supporting QDA tasks.
    • However, the effective integration and limitations of current AI tools in QDA require careful examination.

    Purpose of the Study:

    • To investigate the capabilities and limitations of AI, specifically ChatGPT-4, in performing various qualitative data analysis tasks.
    • To conduct a scoping review of existing literature on AI-supported QDA to understand current trends and methodologies.
    • To examine the potential benefits, drawbacks, and ethical considerations associated with AI in qualitative data analysis.

    Main Methods:

    • Direct application of ChatGPT-4 to analyze narrative datasets, involving iterative prompt engineering to assess task success.
    • A comprehensive scoping review of scholarly articles on AI-supported QDA published up to May 2024.
    • Literature synthesis and expert examination of AI's role in QDA, including its historical context, current applications, and future implications.

    Main Results:

    • Initial attempts with ChatGPT-4 for thematic analysis and complex tasks were unsuccessful, though summarization and keyword counting improved with prompt refinement.
    • The scoping review identified 130 articles, with a significant increase in publications in 2023-2024, highlighting diverse AI methods like unsupervised learning, thematic analysis, and sentiment analysis.
    • AI has been utilized for various QDA approaches including coding, thematic analysis, discourse analysis, and handling large datasets, with methods like unsupervised learning being prevalent.

    Conclusions:

    • AI has a well-established history in supporting diverse QDA methods, with evolving technologies increasing accessibility.
    • Current AI tools, while promising, have limitations in complex QDA tasks, necessitating "human in the loop" oversight and researcher understanding.
    • The integration of AI in QDA presents both opportunities and challenges, requiring careful consideration of ethical implications, data privacy, and potential workforce impacts.