ABSTRACT
This study reported on the impact of artificial intelligence (AI) and internet tools on language translation and acquisition. Motivated by a desire to explore a novel area of research, distinct from the typical focus on specific languages, this study aimed to investigate the role of AI in simplifying and enhancing the accessibility of language translation and acquisition.The study's specific objectives were to examine the accuracy and effectiveness of AI-powered translation tools, investigate the role of AI in language learning and acquisition, assess the challenges and limitations of AI-driven language translation and learning tools, and evaluate the impact of AI on the accessibility of linguistic resources for underrepresented languages.A mixed-methods approach was employed, combining primary and secondary data sources. A Google Form was used to collect data through a survey, which was distributed to language learners and users of AI-powered translation tools.The study was guided by Cognitive Load Theory (CLT), which provided a framework for understanding how AI influences the way learners process and retain information.The major findings of this study revealed that AI-powered tools, such as Duolingo, improved language acquisition, particularly for beginners and intermediate learners. AI translation tools, like Google Translate and DeepL, demonstrated improved fluency and contextual accuracy, although challenges persisted with idiomatic expressions, cultural nuances, and low-resource languages. Additionally, the study found that AI-driven tools improved access to language learning, benefiting diverse learners, including those with disabilities.The study concluded that AI and internet tools significantly enhanced language translation and acquisition by simplifying learning processes, improving accessibility, and increasing engagement. However, the findings also highlighted the limitations of AI-driven tools in cultural accuracy, deep comprehension, and advanced linguistic structures.Based on the findings, the study recommended that developers improve AI translation models to better handle idiomatic expressions, cultural nuances, and low-resource languages. Additionally, the study suggested that AI-driven platforms enhance adaptive learning, emphasize conversational AI, and expand accessibility to underprivileged communities. Furthermore, the study emphasized the importance of integrating human interaction in AI learning to ensure deep comprehension and critical thinking.
