As many entities aim to participate in the ongoing AI race to gain competitive advantages, there is a risk of creating knowledge gaps by overlooking fundamental steps in the research and development processes. This paper aims to bridge the knowledge gap in the domain of large language model (LLM) integrations for knowledge elicitation by performing a systematic literature review using the PRISMA workflow. Through an analysis of 17 research papers, this study identifies new directions, including tools for education, knowledge curation, and factual information. Additionally, the research highlights key benefits and concerns associated with each direction, providing further understanding of the potential and challenges of LLM integrations for knowledge elicitation.