Natural Language-Based Interface Design for Early Childhood Education Applications: A Case Study of English Language Learning
Natural Language-Based Interface Design for Early Childhood Education Applications: A Case Study of English Language Learning
Keywords:
Multimodal Sentiment Analysis, Hate Speech Detection, Deep Learning, VisualBERT, Indonesian Social Media, Text and Image Integration, Natural Language Processing, Computer VisionAbstract
This study explores the design and effectiveness of a Natural Language Interface (NLI)-based application for English language learning targeted at early childhood education. Employing a qualitative case study approach, the research investigates how children aged 4 to 6 interact with the application, focusing on engagement, language development, and system responsiveness. Data were collected through observations, interviews with teachers and parents, and assessment of children’s language proficiency before and after the intervention. Results indicate that the NLI-based application significantly enhances children’s motivation and ability to use English in a natural, interactive context. The application’s responsive feedback, powered by advanced Natural Language Processing (NLP) algorithms, supports real-time correction and fosters active learning. Challenges related to speech recognition accuracy for young children and the necessity of adult guidance during initial use are discussed. Findings suggest that NLI technology, combined with child-friendly design, offers promising potential to improve early language education, with implications for further development and integration in educational settings.
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