PERSONALIZED LANGUAGE LEARNING IN THE AGE OF AI. Leveraging Large Language Models for Optimal Learning Outcomes.

Abstract

In a new era marked by technological advancements and the AI boom, language learning is no longer limited to the classrooms. The emergence of Large Language Models (LLMs) propels further advancements within language learning, as well as creates space where learners can engage in more personalized learning approaches, with the content dynamically adapted to their individual needs. The thesis conducts a series of experiments involving curated learner profiles with two prominent LLMs – ChatGPT and Gemini, to check whether the LLMs can be utilized in language learning and also to what extent LLMs can foster Personalized Language Learning (PLL) approaches. The experiments show that there is significant potential in implementing LLMs within language learning, and that the LLMs are capable of personalizing curriculum and teaching materials to accommodate diverse learner profiles. In addition, the thesis identifies potential drawbacks, risks, and ethical considerations associated with the integration of LLMs in PLL.