The study introduces novel approaches for fine-tuning pre-trained LLMs to predict item response theory parameters directly from item texts and structured item attribute variables. The proposed methods were evaluated on a dataset over 1,000 English Language Art items that are currently in the operational pool for a large scale assessment.