Purpose: This study explores the transformative impact of Artificial Intelligence (AI) on language acquisition, focusing on the integration of AI tools in language learning through sociolinguistic and semiotic perspectives. The aim is to assess AI’s influence on learner engagement, teacher-student rapport, linguistic diversity, and cultural representation. Subjects and Methods: A mixed-methods approach was employed, combining quantitative surveys and experiments with qualitative sociolinguistic and semiotic analyses. Surveys measured the effectiveness of AI tools in language learning, while content analysis and interviews provided deeper insights into cultural and contextual cues embedded in AI-generated texts. Results: The results demonstrate that AI tools significantly enhance learner engagement and communication willingness, offering personalized and interactive learning experiences. However, challenges regarding linguistic diversity and bias in AI models remain, with gaps in representing regional dialects and non-standard language forms. Semiotic analysis revealed that AI still struggles to incorporate cultural and contextual nuances, which are essential for meaningful communication. Additionally, biases in AI models, including gender and racial bias, were detected, emphasizing the need for diversified training data and bias mitigation strategies. AI’s role in shaping language change was also noted, with AI tools influencing the emergence of new linguistic forms and expressions. Conclusions: AI has the potential to revolutionize language acquisition, but its development must address challenges related to linguistic diversity, bias, and cultural representation. The study advocates for a more inclusive and contextually sensitive approach to AI integration in language education to ensure equitable, meaningful, and diverse learning outcomes.
Amira Dwiputri (Wed,) studied this question.
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