## 1 Introduction
### Evaluating Text Differences Using Natural Language Processing  
Natural Language Processing (NLP) is a computational approach that systematically analyzes and extracts patterns from text [7]. In educational and psychological research,NLP tools have been widely applied to automatically evaluate linguistic and semantic features of texts. NLP analyses are extensively used in computer-based learning systems to evaluate text quality and assess student-generated responses (e.g., self-explanations, think-aloud responses, essays, and open-ended answers) [8]. These linguistic analyses provide insights into task performance, cognitive attributes, and individual differences such as literacy skills and prior knowledge [35; 36]. Another application of NLP analyses is text readability assessment. Text readability is the extent to which a reader can process and understand a text and it is quantifiable based on various linguistic indices (e.g., cohesion, noun-to-verb ratio, syntactic, and lexical complexity; [8; 37]). NLP tools (e.g., Coh-Metrix; [37]) have been leveraged to extract and analyze features that influence text readability based on discourse processing theories. For instance, cohesion, syntactic complexity, and lexical sophistication are associated with coherence-building processes and reading comprehension for different students [8; 37; 38]. Additionally, NLP analyses afford systematic distinguishing between different types of texts and discourse based on linguistic and semantic features and, in turn, evaluate the difficulty of educational materials [38; 39]. Scientific texts often contain dense information, abstract concepts, and complex sentence structures that are challenging for students to process and understand [40; 41]. Moreover, academic vocabulary, which is commonly used in academic text, increases comprehension difficulty because readers need to have relevant background knowledge to infer word meanings [41; 42]. Text cohesion (e.g., connectives, causal, referential, and coreference) or how well ideas are connected within a text is another property of text that is measured using NLP. Cohesion influences how readers synthesize information and construct meaning [43]. High-cohesion texts benefit low-knowledge readers who lack the necessary background knowledge to infer meanings due to conceptual gaps [43]. In contrast, low-cohesion texts are more suitable for high-knowledge readers, as they facilitate deep learning by encouraging inference generation using prior knowledge [43]. Comprehension difficulties arise not only from a lack of reading skills or prior knowledge but also from a mismatch between textual features and the individual characteristics of the reader [44; 45]. When readers lack requisite background knowledge or reading skills, linguistic features such as complex syntax or low cohesion can exacerbate comprehension challenges [8]. Skilled readers are able to comprehend advanced academic texts because they possess high-quality lexical representations and apply effective reading strategies [44]. These strategies enable them to efficiently decode complex syntactic structures, infer the meaning of unfamiliar vocabulary from context, and integrate prior knowledge to support deeper understanding. In contrast, readers with limited skills or knowledge may struggle with texts that are less cohesive or that contain sophisticated vocabulary and sentence structures [45; 46]. Additionally, less skilled readers may have difficulty processing complex syntax, as effective comprehension requires sustained attention and cognitive engagement [47; 48]. Table 1 describes several linguistic measures related to text complexity that have been shown to correspond to comprehension difficulties faced by students [37]. We measured these linguistic features using NLP because these linguistic patterns provide objective, quantifiable measures of text difficulty that correlate with expert judgments of readability [49].