## 2 Experiment 1: LLM Text Personalization
### Results Experiment 1  
#### 2.3.1 Main Effect of Reader Profile on Variations in Linguistic Features of Modified Texts  
The goal of this analysis was to examine the extent to which the linguistic features of LLM-modified texts aligned with different reader profiles. A 4 \(\times\) 4 MANCOVA was conducted to examine the effects of reader profiles (High RS/High PK, High RS/Low PK, Low RS/High PK, and Low RS/Low PK) and LLMs (Claude, Llama, Gemini, and ChatGPT) on linguistic features of modifications, with word count included as a covariate. The main effect of reader profiles on linguistic features was significant. See Table 5 for  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Reader Profiles**} & \multicolumn{5}{c}{**Linguistic Features Aligned for Reader**} \\ \cline{2-6}  & & **Overall Readability** & **Conceptual Density and Cohesion** & **Syntax and Lexical Complexity** \\ \hline Reader 1 & \(\bullet\) & High Flesch–Kincaid Grade & \(\bullet\) & Low cohesion to encourage & \(\bullet\) & Complex sentence structures, \\ (High RS/High PK) & \(\bullet\) & Level (FKGL \(\star\)), high complexity & \(\bullet\) & active engagement & \(\bullet\) & Sophisticated vocabulary \\  & \(\bullet\) & High academic writing & \(\bullet\) & High information density & \(\bullet\) & Varied language and syntax \\  & & & & \(\bullet\) & Introduce scientific concepts & \(\bullet\) & Complex sentence structures, \\ Reader 2 & \(\bullet\) & Moderate text complexity & \(\bullet\) & Moderate academic writing & \(\bullet\) & and examples & \(\bullet\) & Sophisticated vocabulary \\  & & & & \(\bullet\) & Moderate cohesion & \(\bullet\) & Varied language and syntax \\  & & & & & & \\ Reader 3 & \(\bullet\) & Moderate text complexity & \(\bullet\) & Elaborated explanations of & \(\bullet\) & Simplify sentence structures, \\ (Low RS/High PK) & \(\bullet\) & Moderate academic writing & \(\bullet\) & key concepts & \(\bullet\) & Miminal technical vocabulary \\  & & & \(\bullet\) & High cohesion & \(\bullet\) & Clear and concise language \\  & & & & & & \\ Reader 4 & \(\bullet\) & Low FKGL \(\star\), low text complexity & \(\bullet\) & Elaborated explanations of & \(\bullet\) & Simplify sentence structures, \\ (Low RS/Low PK) & \(\bullet\) & High academic writing & \(\bullet\) & key concepts & \(\bullet\) & short sentence length \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hypothesized linguistic features of adapted texts aligned to reader profiles. Source: authors’ contribution.  
descriptive statistics and F-values of the main effects of reader profiles. Results showed that linguistic features of modifications significantly differed across reader profiles.  
As expected, the modifications for Profile 1 (High RS/High PK) had the highest syntactic and lexical complexity measures, followed by Profile 2, then 3 and 4. Texts modified for Profile 1 had significantly higher Flesch-Kincaid Grade Level (FKGL) (\(M=16.97\), \(SD=2.36\)) compared to texts modified for Profile 2 (High RS/Low PK) (\(M=10.63\), \(SD=1.76\)), \(p<0.001\), Profile 3 (Low RS/High PK) (\(M=9.84\), \(SD=1.89\)), \(p<0.001\), and Profile 4 (Low RS/Low PK) (\(M=8.50\), \(SD=1.39\)), \(p<0.001\). Pairwise comparisons also revealed significant differences across reader profiles for measures of text complexity, including academic writing, noun-to-verb ratio, sentence length, language variety, sophisticated wording, and academic vocabulary, aligning with the hypothesized difficulty levels for each profile. As intended, texts modified for Profile 1 had the highest complex syntax and lexical measures, followed by Profiles 2, 3, and 4 (all \(p<0.05\)). Contrary to expectations, cohesion was higher for Profile 1, which has high knowledge, compared to Profiles 2 and 4, which both have low prior knowledge (\(p<0.05\)).