AI vs Human Supported Qualitative Data Analysis: A Comparative Synthesis of Research Findings-

Introduction
The rapid use of generative artificial intelligence (AI) in qualitative analysis especially in education research, has driven comparison between AI and human-based qualitative analysis. These studies evaluate the performances of both approaches based on speed, efficiency and depth of insight among other metrics to determine the most efficient qualitative analysis method to optimize the qualitative analysis process. While a small number of studies have synthesized findings across these fragmented studies there remains a need to thematically synthesize these findings, to provide a more comprehensive picture and develop a new and deeper framework for understanding and improving AI and human-based qualitative analysis.

Goals and Methods

A qualitative research design will be used to analyze and synthesize findings from research papers that used and compared AI and human qualitative data analysis methods. Peer reviewed studies will be retrieved from ERIC, Web of Science (WOS) and SCOPUS databases and exported into NVivo for coding and thematic analysis of the research findings. Using the PCC framework this study will include only peer-reviewed studies that document comparison between AI and human qualitative data analysis methods applied to the same dataset within education research.

Expected Results

The study will reveal common themes, trends and patterns from comparative studies documenting the use of AI and human qualitative data analysis approaches in education research.

Conclusions Valuable insights to help clarify and inform best practices in the development of a comprehensive framework for qualitative analysis in education research will be derived. An understanding of the optimum qualitative analysis process will also be derived.