# Multi-Agent LLMs for Personalizing STEM Texts
## 2 Related Work  
### Intelligent Tutoring Systems in Education  
In general, the goal of PL is to adjust both the learning process and educational materials to each student's preferences and interests. This goal, whether technology-enabled or human-driven, is not a modern concept. PL has a rich history in the United States that stretches as far back as the origins of its education system [6]. As legislation emerged during the Progressive era requiring school attendance for children [29], so too did several theories on how best to educate large groups of students. As [PERSON] [6] points out, the early 1800s competency-based approach to education was overwhelmed by rapidly rising enrollments. This led to calls for PL and increasingly numerous investigations into technology-enhanced learning as computers grew in popularity.  
Researchers have investigated the potential of technology-enabled PL since the 1960s [30]. After six decades of research, no single platform has been recognized as optimal. Thus, researchers have continued to develop and evaluate new technology-enabled PL frameworks, including ITSs, in K-12 and post-secondary education [31; 32; 33; 34; 35]. This earlier research has set the stage for PL using modern AI models. In fact, several of the principles used to develop early ITSs can still be found in more recent applications. As discussed in a foundational review by [PERSON] and [PERSON] [36], an ITS must satisfy the following three criteria: the system must (i) possess relevant content knowledge, (ii) be able to describe a student's existing understanding, and (iii) be able to implement different teaching strategies. As an early example, [PERSON] et al.'s [37] Lisp ITS met these three criteria, using an ideal-student model to provide the ITS with domain-specific knowledge, a model tracing module with a bug catalog to model errors and to track student progress, and a tutoring module to provide guidance to students. Although techniques and approaches have varied over time, the definition provided by [PERSON] and [PERSON] has remained essentially unchanged, as evidenced by more recent ITS developments [38; 39; 40].  
Modern ITSs have been shown to be effective learning tools when compared with human-led tutoring [41; 42]. For example, [PERSON] et al. [43] developed an adaptive learning environment for business students in an introductory statistics course and attributed modest grade improvements to the adaptive technology. In another study, [PERSON] et al. [44] demonstrated how a natural language processing (NLP) model could evaluate and improve high school students' reading comprehension. In their study, student comprehension was first scored using the NLP model. These scores were then used to select the next passage. This process was found to provide students with meaningful scaffolds as the difficulty increased [44].  
As [PERSON] [45] points out, however, modern PL systems often act as \"responsive\" rather than \"adaptive\". This is because modern systems assign students to predetermined materials based on performance, rather than actively generating unique content alignedwith their needs and learning preferences. Unlike responsive systems, LLMs can be prompted with critical information about a learner. This information can come in several forms, ranging from examples of past performance to specific hobbies or interests [46]. To move beyond the decision-tree approach of responsive systems, the present study deploys a two-agent GPT-4 system to both identify and adapt science texts to student preferences. Student preferences, as operationalized in this study, represent the types of educational texts a student would select when given the option [47]. We consider these preferences to be non-mutually exclusive and potentially dynamic.