Four versions of science and history texts were tailored to diverse hypothetical reader profiles (high and low reading skill and domain knowledge), generated by four Large Language Models (i.e., Claude, Llama, ChatGPT, and Gemini). Natural Language Processing (NLP) technique was applied to examine variations in Large Language Model (LLM) text personalization capabilities. NLP was leveraged to extract and quantify linguistic features of these texts, capturing linguistic variations as a function of LLMs, text genres, and reader profiles. An approach leveraging NLP-based analyses provides an automated and scalable solution for evaluating alignment between LLM-generated personalized texts and readers’ needs. Findings indicate that NLP offers a valid and generalizable means of tracking linguistic variation in personalized educational texts, supporting its use as an evaluation framework for text personalization.
Huynh et al. (Wed,) studied this question.
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