Developing a Systematic Governance Framework to Assess and Manage AI for Qualitative and Mixed Methods Research

The use of artificial intelligence (AI) is growing rapidly, and researchers are increasingly considering AI to help with labor-intensive and mundane research tasks. With new technology, risks are present and scientists may have trepidation. AI is evolving rapidly, yet researchers have very little guidance to understand the strengths, limitations, and potential risks as they consider new AI tools, and AI policies are often inconsistent or absent. This research is designed to address that lack of guidance, focused on qualitative and mixed methods research, which is used across social sciences, health sciences, and education. Qualitative research involves systematically gathering and analyzing information directly from those most knowledgeable, often through interviews (e.g., with patients, nurses, or physicians to understand experiences and perspectives). Mixed methods research integrates these results with quantitative (numeric) data gathered through surveys, organizational repositories, or other sources for a better understanding. This type of research is resource intensive, and AI could help. Through this study, I will systematically categorize the research tasks where AI can assist; analyze limitations and benefits; and develop and broadly share a research methodology governance framework. This research is intended to overcome current roadblocks to broader adoption of AI for qualitative and mixed methods research.