It has been argued that when scientists employ algorithmic tools to assist in problem-solving, epistemic monocultures may emerge in which research tools, topics, findings, etc. are homogenized. As a result, fertile areas of research might be left unexplored, impeding scientific progress. To explore the nature of these epistemic monocultures, we develop an agent-based model where agents have the ability to query an AI system to assist in their search of an epistemic (NK) landscape. In general, we find that AI use negatively affects the community of researchers by reducing heterogeneity, but both the rate of AI queries and how AI is used impact the ultimate success of the community. We then implement a potential solution suggested in the literature, AI personalization, and find somewhat mixed results on its potential for mitigating homogenization in research communities.