According to the chapter, what are the main limitations of using ChatGPT for qualitative data analysis as identified in the studies by Hamilton et al. (2023) and Morgan (2023)?

According to the chapter, Hamilton et al. (2023) found that ChatGPT-generated themes are too specific to participants' immediate circumstances, disregarding nuanced context and subtleties, whereas human coders used contextual knowledge to develop more comprehensive themes. Morgan (2023) found that ChatGPT tends to emphasize generic aspects of the data, lacking contextualization and the ability to recognize subtle, interpretative themes. Both studies conclude that ChatGPT should serve as a complementary tool rather than a substitute for researcher interpretation.

The chapter describes two studies that identify complementary limitations. Hamilton et al. (2023) used a phenomenological approach and compared emergent themes from human coders and ChatGPT. They found that ChatGPT's themes were narrowly tied to each participant's circumstances and immediate concerns, which led the model to disregard nuanced context and subtleties. In contrast, the human research team could identify comprehensive themes by applying broader contextual knowledge about the participants. Morgan (2023) compared previous analyses with ChatGPT output and acknowledged that ChatGPT offers valuable analytical insights, but also found that it tends to emphasize generic aspects of the data. This means ChatGPT lacks contextualization and cannot reliably recognize subtle and interpretative themes. In both studies, the authors viewed AI as useful for reducing time-consuming tasks, yet insisted that it should not be treated as an interpretive authority. The chapter concludes that LLMs are deprived of interpretative capabilities, and only researchers can supply a deeper, interpretive, and genuine understanding of social phenomena.