Interpretative Phenomenological Analysis (IPA) is a rigorous qualitative methodology grounded in deep, reflective engagement with participant's lived experiences. As qualitative datasets grow in size and complexity, researchers are increasingly exploring how artificial intelligence (AI) tools can support‚ without replacing‚ the interpretative work central to IPA.
This session examines how AI can be strategically integrated into specific phases of the IPA process, including data familiarization, initial coding support, theme refinement, reflexivity checks, and audit trail documentation. Drawing on applied examples from qualitative research practice, the session emphasizes methodological integrity, researcher reflexivity, and ethical boundaries when using AI in phenomenological inquiry. Attendees will gain a clear framework for leveraging AI as an analytic aid while preserving the interpretative depth foundations of IPA.
Learning Outcomes:
By the end of this session, participants will be able to:
- Identify appropriate points within the IPA workflow where AI tools can responsibly support analysis without undermining interpretative rigor.
- Apply AI-assisted techniques to enhance coding consistency, reflexive memoing, and theme development in qualitative datasets.
- Evaluate ethical and methodological considerations when integrating AI into phenomenological research practices.