Exploring language acquisition in children through digital media: A BAMNN and BERT-Based approach

As digital media becomes an integral part of children’s lives, especially through platforms like YouTube and OTT services, it is essential to understand how this exposure impacts language acquisition. This study introduces Felicity, a deep learning model that combines Bidirectional Associative Memory Neural Networks (BAMNN) and BERT to analyze how digital content influences children’s vocabulary development. The model aims to examine not only the acquisition of formal language skills but also how children pick up informal language, slang, and idiomatic expressions through media consumption. The research utilizes a variety of Natural Language Processing (NLP) techniques, such as tokenization and semantic pattern detection, to enable the model to process a wide array of language data. This includes both written transcripts from digital media and spoken language from children’s interactions with the media. The use of BERT enhances the model’s ability to capture contextual meaning, while BAMNN aids in memory association and the long-term retention of acquired vocabulary. Through a comparative analysis, the study shows that BAMNN, when combined with BERT, outperforms traditional language models in areas such as slang detection, vocabulary retention, and noise resilience. This research offers valuable insights into how digital media can shape children’s language learning and suggests that future educational content should incorporate these findings to create more engaging and effective learning experiences for young audiences. The results also have significant implications for content creators, educators, and policymakers, helping them design media content that promotes language development.