Computational Modeling of Language Learning in the Era of Generative Artificial Intelligence: A Response to Open Peer Commentaries

Abstract

In the last few years, researchers have realized that bilingualism is not a unitary concept but a phenomenon on a continuum (DeLuca et al., 2019). As Marian (2022) also noted in her commentary, bilingualism is not an isolated island but rather a captivating component within a vast and interconnected landscape of other cognitive functions. How can researchers offer a theoretical account of the complex bilingual learning and representation across individuals who learn their first language (L1) and second language (L2) in different contexts, for different purposes, and with different people (Grosjean, 2013; Li & Jeong, 2020)? This question entails that researchers need to understand a number of key questions such as: How can bilinguals, especially late bilinguals, integrate new knowledge without disrupting or interfering with the old? What mechanisms allow for rapid learning for early but perhaps not for late L2 learners? What role does statistical learning play in the dynamic language acquisition of two languages? On the sociocultural front: How and why does active learning and immersion in a L2 environment facilitate easier acquisition and mitigate interference from the L1? How do the dynamic interactions between the individual, the language, and the environment shape the unique linguistic profiles of bilingual speakers?