## 5 Conclusions  
The novel contribution of this research is the demonstration of an NLP-based validation method that systematically and iteratively assesses GenAI-modified educational texts. Unlike traditional human assessment, which is resource-intensive and time-consuming, leveraging NLP analyses provides a scalable and efficient validation process. Specifically,NLP metrics objectively quantify text complexity and readability, allowing for immediate feedback loops that support rapid iteration and content refinement. In the two experiments, NLP analyses provided objective and quantifiable evidence to evaluate linguistic alignment between LLM-generated modifications and readers' individual needs. Experiment 1 illustrated that LLMs varied in their capacity to adjust readability appropriately for different reader profiles. Experiment 2 demonstrated how augmented prompting techniques improved alignment with readers' characteristics, further emphasizing the effectiveness of iterative refinements using NLP-based feedback.  
Leveraging NLP to validate LLM modifications has strong potential to support continuous and iterative refinements to enhance personalization. NLP analyses can provide real-time assessment regarding the extent to which content is appropriately tailored to students' needs and abilities. As a result, it allows for iterative refinements to increase the likelihood that the LLM will generate content aligned with expectations. Modifications can be adjusted based on quantitative analyses and further improved without requiring costly and time-consuming studies with human participants. By quickly tailoring text features with individual needs and validating the content, NLP-based validation approaches have the potential to enhance personalized learning [84; 85]. After initial LLM outputs are automatically analyzed, subsequent iterations can integrate NLP-generated feedback. This data-driven iterative cycle allows the system to continuously and dynamically adapt educational content as learners' skills and knowledge evolve, significantly enhancing personalized learning experiences over time. The current research demonstrated that NLP can serve as a scalable, real-time, and efficient evaluation method for automatically validating LLM-generated personalized content. NLP-based validation can be applied to create adaptive, personalized learning environments that evolve in response to learner growth. Investigating the impact of learner-aligned LLM adaptation on academic performance and engagement represents a critical next step.  
Conceptualization, L.H. and D.S.M.; methodology, L.H. and D.S.M.; formal analysis, L.H.; investigation, L.H. and D.S.M.; resources, D.S.M.; data curation, L.H.; writing--original draft preparation, L.H.; writing--review and editing, L.H. and D.S.M.; visualization, L.H.; supervision, D.S.M.; project administration, D.S.M.; funding acquisition, D.S.M. All authors have read and agreed to the published version of the manuscript.  
The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305T240035 to Arizona State University. The opinions expressed are those of the authors and do not represent the views of the Institute or the U.S. Department of Education.  
Not applicable.  
The data presented in this study are available in Google Drive at: [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) qkk46 OwtpfJRQENKNoDr5 Uj1 NGABL2 Hm?usp=sharing]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) qkk46 OwtpfJRQENKNoDr5 Uj1 NGABL2 Hm?usp=sharing), accessed on 10 June 2025.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PK & Prior knowledge \\ RS & Reading skills \\ GenAI & Generative AI \\ LLM & Large Language Model \\ \end{tabular}