AI-Driven Cross-Cultural Communication Enhancer for English Learners: Bridging Language and Cultural Understanding

In today's globalized world, effective cross-cultural communication is a critical skill, especially for English learners striving to navigate diverse cultural contexts. This research presents the development of an AI-driven Cross-Cultural Communication Enhancer, which leverages Graph Neural Networks (GNN) to bridge language barriers and promote cultural understanding among English language learners. Unlike conventional language learning tools, this model incorporates cultural nuances, idiomatic expressions, and context-specific communication styles to improve not only language proficiency but also intercultural competence. By employing GNN, the model captures complex relationships between linguistic elements, cultural features, and communication patterns. This neural network architecture excels in representing structured data, allowing the AI to learn from a rich set of interconnected cultural and linguistic inputs. The model is trained using a diverse corpus of English communication in various cultural contexts, capturing how different cultures interpret meaning, respond to tone, and use non-verbal cues. The result is a highly personalized learning experience where learners can practice and refine their communication skills in culturally sensitive ways. These experiments demonstrate that the GNN-based model significantly enhances learners' ability to comprehend and engage in cross-cultural conversations compared to traditional language learning platforms. Additionally, the system fosters a deeper understanding of cultural subtleties, improving the learner's overall communicative competence. This research opens new avenues in language education by integrating AI-driven cultural intelligence into English learning, making language acquisition more relevant and practical in today's multicultural environments. The findings underscore the potential of GNN in transforming language learning through advanced AI applications, promoting better communication and collaboration across cultural divides.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.1109/cisct62494.2024.11134221
Primary Topic
Robotics and Automated Systems
Type
article
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article

AI-Driven Cross-Cultural Communication Enhancer for English Learners: Bridging Language and Cultural Understanding

K. R. Praneeth, P. Kavitha, Fredrick Ruban Alphonse, K. Chandra Sekhar et al.
Robotics and Automated Systems
article

AI-Driven Cross-Cultural Communication Enhancer for English Learners: Bridging Language and Cultural Understanding

K. R. Praneeth, P. Kavitha, Fredrick Ruban Alphonse, K. Chandra Sekhar, Mahasweta Devi, Bhuvaneswari Pagidipati
article en

Abstract

In today's globalized world, effective cross-cultural communication is a critical skill, especially for English learners striving to navigate diverse cultural contexts. This research presents the development of an AI-driven Cross-Cultural Communication Enhancer, which leverages Graph Neural Networks (GNN) to bridge language barriers and promote cultural understanding among English language learners. Unlike conventional language learning tools, this model incorporates cultural nuances, idiomatic expressions, and context-specific communication styles to improve not only language proficiency but also intercultural competence. By employing GNN, the model captures complex relationships between linguistic elements, cultural features, and communication patterns. This neural network architecture excels in representing structured data, allowing the AI to learn from a rich set of interconnected cultural and linguistic inputs. The model is trained using a diverse corpus of English communication in various cultural contexts, capturing how different cultures interpret meaning, respond to tone, and use non-verbal cues. The result is a highly personalized learning experience where learners can practice and refine their communication skills in culturally sensitive ways. These experiments demonstrate that the GNN-based model significantly enhances learners' ability to comprehend and engage in cross-cultural conversations compared to traditional language learning platforms. Additionally, the system fosters a deeper understanding of cultural subtleties, improving the learner's overall communicative competence. This research opens new avenues in language education by integrating AI-driven cultural intelligence into English learning, making language acquisition more relevant and practical in today's multicultural environments. The findings underscore the potential of GNN in transforming language learning through advanced AI applications, promoting better communication and collaboration across cultural divides.

Vignan's Foundation for Science, Technology & Research (IN), Shriram Institute for Industrial Research (IN), Apollo Institute of Medical Sciences & Research (IN), Hindu College of Pharmacy (IN)
Quality Education
Openalex Percentile: Top 61%
Robotics and Automated Systems
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