<p>This paper explores the ways in which large language models (LLMs) can be used in experimental philosophy. LLMs can be used in different ways for different purposes in experimental philosophy. Among others, we focus on what we call “Non-Predictive Research”, in which LLMs’ “intuitive” responses to thought experiment cases are <i>not</i> expected to be predictive of, or analogous to, human subjects’ intuitive responses to them. In effect, Non-Predictive Research regards LLMs’ responses as <i>superior to</i> human subjects’ responses, at least for the purpose of experimental philosophy. We argue that the non-predictive use of LLMs in experimental philosophy is highly motivated. Firstly, human subjects’ intuitive responses tend to be vulnerable to irrelevant factors and conceptual misunderstandings, which raises a serious worry concerning the reliability of their responses. Secondly, it is possible in the near future that LLMs will overcome these problematic factors to which human subjects are susceptible. As a case study, we conducted an experimental study in which we examined LLMs’ “intuitions” about will and determinism. Our mixed results suggest that while current LLMs outperform human participants in terms of comprehension—especially in understanding deterministic scenarios—they remain highly sensitive to irrelevant factors such as framing and question order. These findings indicate that although LLMs have promising potential, they are not yet reliable sources of stable and unbiased philosophical intuitions. We conclude that substantial technical improvements are required before artificial intuitions can serve as superior alternatives to human intuitions in philosophical research.</p>