# Multi-Agent LLMs for Personalizing STEM Texts
## 2 Related Work
### Multi-Agent Large Language Models  
LLMs have been applied in diverse fields including psychology [48], neuroscience [49], and medicine [50]. Studies in these fields have largely been successful, reflecting the diversity of knowledge captured in LLMs' training data [51]. In addition to their training data, the multi-disciplinary success of these models can be attributed to the transformer architecture, which allows the LLM to parse context and generate relevant responses [23; 52]. While LLMs can make mistakes, their accuracy has continued to improve with newer releases [53; 54]. While many studies have investigated teachers' use of LLMs (e.g., grading [55], assessment development [56]), fewer have assessed how students can benefit from these models. In one recent work, [PERSON] et al. [57] tested whether integrating LLMs in science and engineering education would improve middle school students' knowledge acquisition. The results of their controlled study showed that students who used the LLM achieved significantly higher post-test scores than students who learned in a traditional format. Similar work integrating ChatGPT-4o into first-year undergraduate computer science education showed a significant improvement in student knowledge following an eight-week period [58].  
Much research has focused on understanding the relationships between user-provided prompts and the outputs that LLMs generate. Research of this kind has led to the new field of prompt engineering [59], which focuses on crafting inputs that elicit the best possible outputs [60]. While some characteristics of a good prompt are transferable between use cases, they are often task-specific. This makes prompt design and LLM-powered MAS architectures an important area of ongoing research. Prompt engineering is especially important in education, where prompts must be designed to be interpretable and ensure that accurate information is generated across subject areas. Chain-of-thought prompting, which decomposes large tasks into smaller steps, has been found to improve model performance [61] and to let users inspect the LLMs' reasoning. This prompting strategy could be valuable to both teachers and students, allowing them to better understand the model's internal logic and the output it generates. For students, the combination of chain-of-thought prompting with LLMs' rapid feedback may help scaffold the learning process [62; 63].  
LLMs have also demonstrated improved performance when complex tasks are decomposed among multiple specialized agents. For instance, in education, we may envision one model that describes the status of a student's learning, another that focuses on the content to be taught, and a third that decides on an optimal teaching strategy for the student. By passing the output of one of these models to the others, we may achieve a framework capable of on-demand personalization. Reminiscent of ITSs, we recognize that this structure--where multiple LLMs serve as different agents in an ITS--is not unique to the present work [64; 65]. In just one example, [PERSON] et al. [66]'s GenMentor ITS uses five LLM agents to develop a goal-oriented learning environment.  
Despite the existing research on LLMs in education, there is a notable lack of experimental literature exploring the ability of multi-agent LLM systems to identify and adapt science texts to students' learning preferences. This is especially true of research in K-12,which is scarce when compared to post-secondary settings. Thus, the present study systematically tests the feasibility of using a two-agent LLM system to adapt educational materials to individual middle school students' learning preferences. We intend our findings to serve as a starting point for future applications of LLMs in K-12 education.