Assessing the Suitability of AI-Generated Texts Across Proficiency Levels Using the Functional Adequacy Scale

This thesis explores the potential of large language models (LLMs) in educational settings by evaluating their ability to generate texts that align with human-written texts in terms of functional adequacy. Using a novel approach, this study utilizes the functional adequacy (FA) scale to assess the communicative effectiveness of LLM-generated texts, focusing on four dimensions: content, task requirements, comprehensibility, and coherence and cohesion. The results of this study will provide valuable insights into the practical implications of integrating LLMs in educational contexts, shedding light on their potential benefits and limitations for language learning and assessment.