Reimagining Language Acquisition in the Age of Artificial Intelligence through Sociolinguistic and Semiotic Perspectives - Amira Ben Youssef; Mohamed Ali Trabelsi (LIER: Language Inquiry & Exploration Review, 2024)
Purpose: This study examines the effectiveness of Artificial Intelligence (AI) tools in language acquisition from sociolinguistic and semiotic perspectives, focusing on learner engagement, satisfaction, communication, and language representation. Subjects and Methods: A mixed-methods approach was employed involving learners engaged in AI-assisted language learning. Quantitative data were collected through Likert-scale surveys and quasi-experimental comparisons, while qualitative data were obtained through content analysis and interviews. Data were analyzed using descriptive and inferential statistics alongside thematic, sociolinguistic, and semiotic analysis. Results: The findings show that AI tools significantly improve learner engagement, satisfaction, and willingness to communicate, with ChatGPT demonstrating the strongest performance. AI environments foster interactive and adaptive learning experiences, enhancing learner confidence. Qualitative results reveal that AI-generated language remains predominantly standardized, with limited representation of linguistic diversity and moderate contextual sensitivity. Bias detection is uneven, reflecting underlying data imbalances, and AI contributes to shaping language use and evolution. Conclusions: AI offers substantial pedagogical benefits in language learning, yet its sociolinguistic and semiotic limitations highlight the need for more inclusive, context-aware, and ethically developed language models.