# Multi-Agent LLMs for Personalizing STEM Texts
## 1 Introduction  
Recent advancements in large language models (LLMs) and multi-agent systems (MASs) have opened new opportunities for tackling complex challenges in education, such as the on-demand personalization of learning materials. Traditionally, MAS designs have followed rule-based or probabilistic methods, assigning clearly defined roles and tasks to specialized agents that interact through predefined communication protocols [1]. At times, these inter-agent communications have lacked interpretability (e.g., [2]), limiting the potential for human-in-the-loop artificial intelligence (AI) in MASs. However, the development of powerful LLMs has revealed a new frontier in MASs. These so-called LLM-based MASs utilize multiple instances of an LLM, with each instance acting as an agent. These agents communicate by passing data in a natural language between their outputs and inputs [3, 4]. This greatly improves the interpretability of agent communications, allowing humans to understand the data that the MAS is processing and for them to join the conversation. Past works have recognized explainable AI as a critical area of research that fosters trust between humans and AI algorithms and ensures that model output and decisions are ethically aligned with human values and norms [5]. Given thenovelty of LLM-based MASs, there remains a lack of research on their ability to serve as personalization tools--especially in K-12 education. As an early step towards filling this gap, this paper presents and tests a two-agent GPT-4 framework for personalizing science texts. Specifically, we explore the MASs ability to identify and adapt texts to students' unique learning preferences through a pilot randomized controlled trial (RCT) in a middle school setting. To understand the significance of these advancements, we first consider the historical challenges and aspirations surrounding personalized learning (PL), which has long been recognized as an essential yet challenging educational goal. Because of this, the National Academy of Engineering named advancements in PL one of the fourteen grand challenges of the 21 st century, thus reinvigorating a centuries-old aspiration to individualize instruction [6,7]. PL is now a broad term that encompasses a diverse range of interventions and educational programs [8,9]. Numerous definitions have been proposed, each differing in its incorporation of learner characteristics, instructional designs, and learning outcomes [10,11]. Given this variability, this study defines PL as \"instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner,\" where \"learning objectives, instructional approaches, and instructional content...may all vary based on learner needs\" [12] (p. 9). In this paper, we use the term \"adapt\" to refer to the development of personalized materials. In defining PL, we note that the term \"personalization\" is closely related to but is also unique from terms like \"differentiation\" and \"individualization\". Differentiation provides varying instructional paths (e.g., learning materials, difficulty, assessments, and final products) to help all students reach the same learning objectives. Individualization, by contrast, adjusts the pace and method of instruction on the single-student level to suit their unique needs and to help them meet common learning goals [13]. Despite its potential, the implementation of PL in K-12 classrooms has often been burdened by constraints such as teacher workload and resource availability [14, 15, 16]. To overcome these challenges, some educators have turned to technologies like intelligent tutoring systems (ITSs), which use domain-specific knowledge and student achievement tracking to differentiate or individualize learning materials [17]. In some cases, an ITS may be implemented as an MAS (e.g., [18]), with multiple agents working together to create adaptive learning environments. This link between MASs and ITSs is also demonstrated by researchers like [PERSON] et al.