AI and Second Language Acquisition in Multilingual Scenarios

Abstract

Introduction: This article focusses on the application of artificial intelligence in language learning, particularly in multilingual communities. It discusses how interest is generated to learn languages and increase the accessibility with the help of machine learning (ML) and Natural Language Processing (NLP). With the help of various examples, the article shows how there is a huge scope for AI in language learning and also touches upon future implications and implementation.
Objectives:
To explore the role of Artificial Intelligence (AI) in enhancing second language acquisition, particularly in multilingual contexts.
To examine the contributions of key AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and speech recognition in language learning.
Methods: This study adopts a qualitative, exploratory approach to understand the intersection of Artificial Intelligence (AI) and second language acquisition within multilingual settings. The methodology involved:
1. Literature Review:
A comprehensive review of existing academic literature, research papers, and industry reports related to AI applications in language learning was conducted. Key sources include peer-reviewed journals, conference proceedings, and credible organizational publications (e.g., UNESCO, Duolingo, Microsoft).
2. Case Study Analysis:
Specific AI tools and platforms—such as Duolingo, Google BERT, and Microsoft Translator—were examined as case studies to evaluate their practical use in supporting second language learners across multilingual scenarios.
3. Thematic Content Analysis:
The collected data were analyzed thematically to identify recurring trends, technological innovations, user engagement patterns, and challenges in AI-driven multilingual education.
4. Comparative Evaluation:
Features of AI-powered tools were compared across different platforms to assess their adaptability, feedback mechanisms, and cultural inclusivity.
Results: AI enhances multilingual language acquisition through personalized learning, real-time feedback, and translation tools, while challenges like linguistic bias, data scarcity, and cultural insensitivity still persist in low-resource language contexts.
Conclusions: AI revolutionizes second language acquisition in multilingual settings by offering adaptive, efficient learning tools. Addressing biases and supporting low-resource languages will ensure inclusive, equitable, and culturally sensitive language education.