Humans understand language rapidly and with remarkable ease. One way that humans do this is by predicting what a conversation partner might say next. But, there remain many unanswered questions about how different language backgrounds might help, or hinder, effective predictions. There is a critical need to understand proficient second-language comprehension. This project studies brain signals while bilinguals listen to an audiobook story in their first and second language. Computational modeling using artificial intelligence (AI) language systems are used to test the kinds of predictions people make, the information that guides those predictions, and how predictions are affected by differences in language background. This project offers insight into how AI can incorporate multiple languages in a realistic way and increases awareness of bilingual language with programs targeting future teachers and the public. Other benefits to society include increased transparency and reproducibility in language research by providing a large corpus of brain and behavioral data for other scientists and engineers. To meet these aims, the project collects electroencephalography (EEG) signals from three groups of bilingual participants with different levels of experience while they listen to an audiobook story. These signals reflect fast-changing brain responses and are highly sensitive to expectations in language. AI is used to capture the linguistic features of the story, such as the relat