This chapter examines connectionism, or parallel distributed processing (PDP), as a framework for understanding language acquisition. Connectionist models emphasize interconnected neural networks, viewing learning as a dynamic process of pattern recognition through repeated exposure to linguistic input. While offering insights into morphosyntax, speech recognition, and sentence processing, these models face limitations, such as reliance on large datasets and challenges in representing hierarchical structures. Despite these constraints, connectionism informs language teaching through adaptive learning technologies, enabling personalized, context-rich instruction. By integrating connectionist principles with modern tools, educators can create immersive environments that support diverse learners, fostering both foundational and advanced language skills. The chapter highlights the potential of connectionism to shape future research and teaching practices in second language acquisition.