Spatial Cognition in Phonetic Acquisition: An AI-Enhanced LOCI Framework for IPA Symbol Mastery

Abstract

This study examines the intersection of spatial cognition, artificial intelligence, and linguistic learning through an innovative LOCI-AI framework for IPA symbol acquisition. While spatial memory techniques enhance retention across various domains, their integration with AI technologies for phonetic learning presents a groundbreaking approach. Through a mixed-methods approach with 78 English language majors, including neuroimaging, qualitative interviews, and real-time AI-assisted learning analytics, we tested this specialized adaptation of the classical LOCI method enhanced by machine learning algorithms. Results show significant improvements in symbol retention (p<0.001), transcription accuracy (+42% versus +37% in non-AI approaches), and reduced cognitive load as measured by EEG markers. Neuroimaging revealed enhanced activation between hippocampal and language processing regions, which was further optimized using personalized AI recommendations. The framework proved particularly effective for traditionally challenging vowel symbols, with intelligent adaptive sequencing yielding a 15% improvement over the standard spatial framework. These findings contribute to cognitive psychology, linguistic education, and AI-enhanced learning by demonstrating how specialized spatial cognition techniques augmented by machine learning can transform abstract symbol learning into robust, personalized mental representations with practical applications for language acquisition.