AI Enhances, Not Outdates, Self-Study Research: A Practical Guide to Integrating AI Into Research Methodology

The purpose of this paper is to explore how artificial intelligence (AI) enhances, rather than outdates, self-study research in physical education. Despite concerns about AI in qualitative research, self-study remains essential for advancing practice-based knowledge. This paper examines AI’s role in enhancing four core characteristics of self-study: streamlining data organization and reflection to focus on practice, refining themes through iterative qualitative analysis, augmenting collaboration as a critical friend, and validating findings through data collected from active teaching practices. A practical four-phase process—data generation, theme development, practice identification, and theme confirmation—demonstrates how AI can support researchers at each stage. A visual workflow diagram further highlights the integration of AI into self-study. This paper encourages educators and researchers to embrace AI as a tool for advancing self-study practices, ensuring that while the human element remains central, AI provides invaluable support in organizing and validating insights from lived experiences in physical education.