Research on the Cognitive Mechanism and Teaching Optimization of Second Language Acquisition Empowered by Intelligent Technology

Against the background of rapid iteration in artificial intelligence, big data, natural language processing, machine learning, and deep digital transformation in education, the research and teaching paradigms of second language acquisition are undergoing structural change. The essence of second language acquisition is a complex cognitive process involving perceptual attention, memory encoding, rule internalization, semantic construction, logical deduction, crosscultural integration, and communicative output. Traditional research and teaching approaches often have difficulty capturing implicit cognitive processes, dynamic acquisition evolution, and individual cognitive differences. This study analyzes how intelligent technology empowers the cognitive mechanisms and teaching optimization of second language acquisition. It defines an intelligent-technology cluster including NLP, AI speech evaluation, eye tracking, EEG observation, learning analytics, and adaptive learning systems, and explains its role in explicit cognitive observation, dynamic tracking, personalized diagnosis, and cognitive-adaptive teaching. In smart learning environments, wireless learning systems, signal acquisition devices, and electromagnetic-compatible terminals provide the technical basis for collecting and transmitting learning data. The study proposes a teaching optimization path of cognitive diagnosis, hierarchical supply, adaptive feedback, and communicative output training.