Architecture that employs adaptive learning algorithms to adapt a data correction tool to user-specific behavior during runtime. The architecture includes a framework for training and measuring adaptive learning algorithms, adapting the current text correction tool codebase, and one or more different adaptive learning algorithms. This enables a text correction system to adapt the behavior of the text correction system to an individual user based on the user's interaction with the data correction system. This also facilitates the testing and improvements in an adaptive learning algorithm at the vendor before shipping in a product to the end-user. This reduces the risk of shipping a feature the precise behavior of which is different for each user.