# Multi-Agent LLMs for Personalizing STEM Texts
## 5 Discussion  
This RCT evaluated the ability of a two-agent GPT-4 framework to systematically adapt educational science-related texts to the unique learning preferences of individual middle school students. To evaluate this ability, participants in the experimental group selected their preferred paragraph between (i) a text aligned with their true profile and (ii) a generic text, while those in the control group chose between (i) a text aligned with their opposite profile and (ii) the same generic text. An ordinal logistic regression suggested that participants in the experimental group may have been more likely to select the personalized paragraphs during the test session (\(p\) = 0.085). These data suggest that LLMs may be able to effectively adapt educational texts to the preferences of individual students. This finding is consistent with the results of past research, which has used LLMs to adapt text to academic achievement levels [65] and has developed a multi-agent LLM framework to implement ITSs capable of identifying learner needs and providing tailored content [66]. With this in mind, we again caution the reader that this study was exploratory in nature and that more research is needed to confirm these findings. The definition of PL provided earlier notes that a personalized environment must be capable of both pacing and tailoring the content of instruction to individual students and their personal interests [12]. The Felder-Silverman model was used here to develop the training paragraphs and, by extension, the initial profile describing each student's learning preferences. The profile was therefore limited to the model's four dimensions, \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Selected Profile 1**} \\ \cline{2-3}  & **True Profile** & **Opposite Profile** \\ \hline Experimental (\(n\) = 12) & 9 (0.75) & 3 (0.25) \\ Control (\(n\) = 11) & 5 (0.455) & 6 (0.545) \\ \hline \hline \end{tabular} \({}^{\dagger}\)Counts are shown. Values in parentheses indicate proportions.  
\end{table} 
Table 4: Summary of data collected in profile evaluation phase of study. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Type**} & **Text** \\ \hline True Profile & You seem to like learning in a way that connects ideas together,  showing how things work in the big picture. You enjoy  explanations that make you feel involved in a story, bringing  science to life in a way that is less about lists and more about  the flow of ideas. You also appreciate clear, straightforward  facts that show how things work step by step at times.  
\begin{tabular}{c c} \hline \hline \multirow{4}{*}{Opposite Profile} & You seem to learn best when facts and steps are laid out clearly  for you. You like to see information presented in a sequence or  process that you can follow from beginning to end. You often  prefer descriptions that are detailed, explaining the how and  why of things in a straightforward way. However, sometimes  you also appreciate a clear demonstration of concepts, where  you can see how things change or behave under  different conditions.  
\end{table} 
Table 5: True and opposite student profiles generated by the Profiler agent. which do not explicitly account for other critically important characteristics such as a learners' preferred pace, specific interests, or existing knowledge. As discussed in Section 4, more data of more diverse types (e.g., additional training examples, student input, teacher input, formal questionnaires) should be incorporated to generate more accurate profiles. Moreover, the profiles remained static during the experiment, limiting the amount of information that could be gleaned about each student in relation to the four choices made during the training session. Despite these limitations, this study provides a starting point for developing adaptive (see [PERSON] [45]) LLM environments. NLP models, of which LLMs are a subset, have previously performed well when tasked with evaluating student performance and knowledge [44].