# Evaluating the capability of large language models to personalize science texts for diverse middle-school-age learners

arXiv: https://arxiv.org/abs/2408.05204 · HTML: https://mlsanity.com/paper/2408.05204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023)
- [Unveiling Security, Privacy, and Ethical Concerns of ChatGPT](https://mlsanity.com/paper/2307.14192/) (2023)
- [Fine-tuning ChatGPT for Automatic Scoring](https://mlsanity.com/paper/2310.10072/) (2023)
- [A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education](https://mlsanity.com/paper/2312.03173/) (2023)
- [Mind meets machine: Unravelling GPT-4's cognitive psychology](https://mlsanity.com/paper/2303.11436/) (2023)
- [Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams](https://mlsanity.com/paper/2303.17003/) (2023)
- [Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education](https://mlsanity.com/paper/2405.02985/) (2024)
- [Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE Questions](https://mlsanity.com/paper/2404.13307/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Benchmark for LLM's Understanding of Middle School and High School Science Topics](https://mlsanity.com/paper/2609.32020/) (2026)

## Abstract (arXiv)

Large language models (LLMs), including OpenAI's GPT-series, have made significant advancements in recent years. Known for their expertise across diverse subject areas and quick adaptability to user-provided prompts, LLMs hold unique potential as Personalized Learning (PL) tools. Despite this potential, their application in K-12 education remains largely unexplored. This paper presents one of the first randomized controlled trials (n = 23) to evaluate the effectiveness of GPT-4 in personalizing educational science texts for middle school students. In this study, GPT-4 was used to profile student learning preferences based on choices made during a training session. For the experimental group, GPT-4 was used to rewrite science texts to align with the student's predicted profile while, for students in the control group, texts were rewritten to contradict their learning preferences. The results of a Mann-Whitney U test showed that students significantly preferred (at the .10 level) the rewritten texts when they were aligned with their profile (p = .059). These findings suggest that GPT-4 can effectively interpret and tailor educational content to diverse learner preferences, marking a significant advancement in PL technology. The limitations of this study and ethical considerations for using artificial intelligence in education are also discussed.