From Assistance to Autonomy -- A Researcher Study on the Potential of AI Support for Qualitative Data Analysis
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910034976309248 |
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| author | Kirsten, Elisabeth Buckmann, Annalina Lassak, Leona Borgert, Nele Mhaidli, Abraham Becker, Steffen |
| author_facet | Kirsten, Elisabeth Buckmann, Annalina Lassak, Leona Borgert, Nele Mhaidli, Abraham Becker, Steffen |
| contents | The advent of Artificial Intelligence (AI) tools, such as Large Language Models, has introduced new possibilities for Qualitative Data Analysis (QDA), offering both opportunities and challenges. To help navigate the responsible integration of AI into QDA, we conducted semi-structured interviews with 15 Human-Computer Interaction (HCI) researchers experienced in QDA. While our participants were open to AI support in their QDA workflows, they expressed concerns about data privacy, autonomy, and the quality of AI outputs. In response, we developed a framework that spans from minimal to high AI involvement, providing tangible scenarios for integrating AI into QDA practices while addressing researchers' needs and concerns. Aligned with real-life QDA workflows, we identify potential for AI tools in areas such as data pre-processing, researcher onboarding, or conflict mediation. Our framework aims to provoke further discussion on the development of AI-supported QDA and to help establish community standards for responsible Human-AI collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Assistance to Autonomy -- A Researcher Study on the Potential of AI Support for Qualitative Data Analysis Kirsten, Elisabeth Buckmann, Annalina Lassak, Leona Borgert, Nele Mhaidli, Abraham Becker, Steffen Computers and Society The advent of Artificial Intelligence (AI) tools, such as Large Language Models, has introduced new possibilities for Qualitative Data Analysis (QDA), offering both opportunities and challenges. To help navigate the responsible integration of AI into QDA, we conducted semi-structured interviews with 15 Human-Computer Interaction (HCI) researchers experienced in QDA. While our participants were open to AI support in their QDA workflows, they expressed concerns about data privacy, autonomy, and the quality of AI outputs. In response, we developed a framework that spans from minimal to high AI involvement, providing tangible scenarios for integrating AI into QDA practices while addressing researchers' needs and concerns. Aligned with real-life QDA workflows, we identify potential for AI tools in areas such as data pre-processing, researcher onboarding, or conflict mediation. Our framework aims to provoke further discussion on the development of AI-supported QDA and to help establish community standards for responsible Human-AI collaboration. |
| title | From Assistance to Autonomy -- A Researcher Study on the Potential of AI Support for Qualitative Data Analysis |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2501.19275 |