## 1 Introduction
### Personalized Learning  
Personalized learning involves matching students with materials at an appropriate difficulty level, tailoring contents to suit their unique interests, thereby enhancing comprehension and engagement, and supporting vocabulary acquisition, skill development, and overall academic success [11; 12]. When students perceive learning materials as relevant and aligned with their interests, they are more likely to persist through challenges and actively engage in learning activities, even when the content is difficult [13; 14]. Moreover, students engaged in personalized learning experiences demonstrate improved knowledge retention and higher gains in mathematics and reading performance compared to those in traditional learning settings [15; 16]. For example, tailoring algebra problems based on students' interests has been shown to enhance student's motivation, engagement, and learning outcomes [17].  
Personalized learning has emerged as a strategy to align text readability and writing style appropriate for individual needs [18]. Reading texts suited to students' abilities also fosters interest, motivation, and a passion for learning [19; 20]. Prior research has demonstrated the importance and efficacy of matching text to readers' abilities to optimize comprehension and learning outcomes [21; 22]. To effectively align reading materials with students' reading capabilities, the Lexile Framework is a widely used text-reader matching tool that determines the reading skills of children and matches them with texts based on a readability formula [23; 24]. This framework quantifies text difficulty based on factors such as syntactic complexity, sentence length, and word frequency, while reader skill is assessed using standardized assessments [24; 25].  
While established frameworks like Lexile [23; 24; 25] have demonstrated effectiveness, advancements in artificial intelligence (AI) provide promising tools to implement personalized learning at scale. Generative AI (GenAI) plays a crucial role in advancing personalized learning by dynamically tailoring learning materials specifically to students' needs and abilities. Large Language Model (LLM) applications have been applied to adapt writing style, simplify content, and generate materials tailored to individual needs [26; 27]. For instance, in educational contexts, LLMs were leveraged to simplify content and adjust lexical and syntactic complexity to enhance accessibility for students with learning disabilities [28]. Similarly, researchers have leveraged LLMs and OpenAI's API to automatically generate personalized reading materials and comprehension assessments within the learning management system [29; 30; 31]. These personalized experiences resulted in higher student engagement and increased their study time, ultimately enhancing academic performance and outcomes [32; 33; 34].  
Although the positive impact of personalization on learners' engagement and performance is well-established, implementation of GenAI-powered personalization in the classroom remains challenging. While text adaptation using LLMs offers a scalable solution to personalize learning, evaluating the effectiveness of modifications can be complicated. Effective text personalization requires rapid validation and iterative refinements to ensure that materials continuously align with students' evolving needs and learning goals. Traditional assessment (e.g., human comprehension study) is both time-consuming and resource-intensive, which makes it challenging and impractical to implement on a large scale. To address this challenge, an automated and scalable validation method is necessary to evaluate the extent to which personalized content aligns with students' various needs and assets.