This paper offers novel methodological insights into a meta-autoethnography of metadiscourse and implications for interdisciplinary qualitative research on generative artificial intelligence (AI) models. Two authors represent divergent subjectivities in science, technology, engineering, arts, and mathematics (STEAM), business, management, and economics (BME), and higher education research and development (HERD). Thus, the authors attempt to design the meta-autoethnography of metadiscourse on the use of generative AI models and demonstrate the specific research design procedures. The two authors articulated the research thesis, addressed research gaps and methodological issues of relevance and applicability, and employed hermeneutic data collection and analysis strategies. They discussed key concerns that may arise from recontextualization and the trustworthiness needed by qualitative researchers. This paper provides methodological implications by suggesting meta-auto ethnographic writings and potential themes within the meta discourse. Each of the co-authors revealed their relevant issues as critical pedagogues, academic editors, business entrepreneur trainers, and developed scholarly discourses on potential human capital crises in STEAM academia and the information and communication technology (ICT) industry, as well as pragmatist research and business ethics, and grounded rules. Therefore, this paper concludes by addressing the paradoxical use of Generative AI models, highlighting methodological insights and implications for diverse audiences in interdisciplinary qualitative research.
Bai et al. (Sat,) studied this question.