• Dickson Amexo

  Author

Second language acquisition (SLA) is important for international communication and education; but there has always been the problem of ignoring individual differences among learners in abilities, cognition, motivation, and requirements of learning. Current computerized language learning systems are helpful but they do not take into account the cognitive aspect of learners and their adaptive learning needs. This paper looks into the possibility of using cognitive computing to improve second language acquisition. This research proposes a Cognitive Computing Framework which is built around learner analysis, personalized learning planning, adaptive learning assistance, and continuous performance assessment. The cognitive computing framework is designed for the purpose of learning about the learner, determining learning difficulties and providing a personalized learning experience. A comparative evaluation has been made between the traditional learning approach, AI-based learning approach, and the cognitive computing approach proposed by us in terms of educational performance indicators like personalization, learner involvement, language improvement, feedback, adaptability, and knowledge retention. Experimental data prove the efficiency of the proposed framework in improving second language acquisition. While the proposed approach produced 95.0% learning performance, traditional learning methods provided 71.8% learning performance, and AI-assisted learning approaches offered 85.8% learning performance. The following learning enhancements have been gained using the framework: personalized learning support (95.2%), learner engagement (94.5%), language skill improvement (96.1%), feedback effectiveness (94.8%) and learning adaptability (95.6%). It proves that cognitive computing is able to support personalized learning processes and improve learner engagement. This research proves that cognitive computing-based methods can be an efficient approach in designing adaptive and personalized second language learning environments. With the help of the integration of cognitive awareness and intelligent educational solutions, the proposed framework allows achieving improved language proficiency, learning engagement, and effectiveness. Further research will pay attention to the development of immersive technologies, conversational agents, and emotional learning environments in intelligent language education.

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