Proceedings of the 9th World Conference on Qualitative Research

Introduction (Context/Justification):
This paper explores integrating artificial intelligence (AI) with human intelligence (HI) in autoethnographic research. Reflecting on my experiences as a neurodivergent academic leader, I engage ChatGPT as a collaborator to deepen self-reflection. Building on McNiff’s (1995) concept of self-inquiry and Guzman’s (2022) research on AI in qualitative methods, this study explores how AI can enhance traditional autoethnography, addressing the blind spots in AI research highlighted by Crawford and Calo (2016).

Goals and Methods:
This study employs a five-stage autoethnographic process—Recalling, Writing, Conceptualizing, Dialogue, and Reflection—using ChatGPT as an active collaborator. AI’s input is analyzed and compared to personal reflections, challenging traditional researcher positionality and offering new interpretive possibilities. Potential biases in AI outputs are addressed through careful comparison with lived experiences, ensuring the AI’s contributions enrich rather than distort personal narratives.

Ethical Considerations:
Ethical oversight ensures AI’s involvement respects the integrity of human experiences. ChatGPT’s outputs are critically assessed to avoid misinterpretation or depersonalization of sensitive narratives related to neurodivergent leadership.

Results (Obtained or Expected):
Preliminary findings suggest that AI adds depth to the analysis of neurodivergent experiences. For example, ChatGPT's insights into hypervigilance reveal connections not immediately apparent in human reflections, supporting Guzman’s (2022) claim that AI can enhance human perception in qualitative research.

Contributions to the Field:
This study offers a novel framework for integrating AI with autoethnography, providing insights into neurodivergent academic leadership and expanding the scope of qualitative inquiry. It demonstrates that AI can serve as a sensitive and ethical collaborator, augmenting human reflection in qualitative research.

References

Crawford, K., & Calo, R. (2016). There is a Blind Spot in AI Research. Nature, 538(7625), 311–313.

Guzman, A. L. (2022). Artificial Intelligence in Qualitative Research. Journal of Technology in Society, 67(3), 100-115.

McNiff, J. (1995). Action Research: Principles and Practice. Routledge.