Dual Cognitive Pathway Architecture for Robust and Dialect-Aware English Reading Comprehension

Reading comprehension models frequently struggle to accommodate linguistic diversity, especially dialectal variations within the English language that disrupt semantic alignment and fairness. In order to overcome these drawbacks, this study proposes the Dual Cognitive Pathway-Based Dialect-Aware Cognitive Twin Framework (NeuroTwin-DialectaLearn) as a new framework that combines the principles of cognitive science, sociolinguistic expertise, and adaptive learning methods. The framework also has two parallel understanding routes, one is a Lexico-Semantic Pathway that processes normal English, and the other is a Dialectal-Semantic Pathway that is involved in normalizing the dialect and aligning the semantics. Such pathways interact with each other by a process of Adaptive attention fusion in a Cognitive Twin model, which instantiates important cognitive processes, including lexical processing, syntactic parsing, semantic integration, inductive reasoning, and answer generation. It uses Python and PyTorch to implement the system and is tested on the English Classroom QA Dataset with the addition of synthetic dialectal variants to enhance the system. The accuracy of comprehension is 98.1 per cent, the average response time is 14.3 seconds, and the rate of learners’ improvement is 18.9 per cent, which is much higher than the baseline QA systems and improved BERT models. The framework has shown consistent performance in the context of dialects; the number of vocabulary-related mistakes is lower, and the consistency of inference is higher, proving to be an effective tool in dialect-conscious and cognitively based reading comprehension. In general, the present research provides a linguistically encompassing, versatile, and understandable next-generation smart educational system.