This study reviews existing research on the use of large language models (LLMs) for automatic item generation (AIG). We performed a comprehensive literature search across seven research databases, selected studies based on predefined criteria, and summarized 60 relevant studies that employed LLMs in the AIG process. We identified the most commonly used LLMs in current AIG literature, their specific applications in the AIG process, and the characteristics of the generated items. We found that LLMs are flexible and effective in generating various types of items across different languages and subject domains. However, many studies have overlooked the quality of the generated items, indicating a lack of a solid educational foundation. Therefore, we share two suggestions to enhance the educational foundation for leveraging LLMs in AIG, advocating for interdisciplinary collaborations to exploit the utility and potential of LLMs.