There are many error correction tools to remove the base calling errors made by Illumina technology, but most do not update the quality scores, even after correcting the errors. The quality score is an important metric quantifying the trustworthiness of the corresponding base call that is used by many downstream sequence analysis tools. This research proposes a method to update quality scores of corrected errors when using PREMIER, a fully-probabilistic error correction method for Illumina sequencing data. I then test the quality of the updates to see if the updated quality scores better reflect the actual probability of error in an Illumina dataset.