  Large Language Models (LLMs) have emerged as powerful generative Artificial
Intelligence solutions which can be applied to several fields and areas of
work. This paper presents results and reflection of an experiment done to use
the model GPT 3.5-Turbo to emulate some aspects of an inductive Thematic
Analysis. Previous research on this subject has largely worked on conducting
deductive analysis. Thematic Analysis is a qualitative method for analysis
commonly used in social sciences and it is based on interpretations made by the
human analyst(s) and the identification of explicit and latent meanings in
qualitative data. Attempting an analysis based on human interpretation with an
LLM clearly is a provocation but also a way to learn something about how these
systems can or cannot be used in qualitative research. The paper presents the
motivations for attempting this emulation, it reflects on how the six steps to
a Thematic Analysis proposed by Braun and Clarke can at least partially be
reproduced with the LLM and it also reflects on what are the outputs produced
by the model. The paper used two existing datasets of open access
semi-structured interviews, previously analysed with Thematic Analysis by other
researchers. It used the previously produced analysis (and the related themes)
to compare with the results produced by the LLM. The results show that the
model can infer at least partially some of the main Themes. The objective of
the paper is not to replace human analysts in qualitative analysis but to learn
if some elements of LLM data manipulation can to an extent be of support for
qualitative research.
