This short communication presents preliminary findings on the integration of Large Language Models (LLMs) and wearable technology to generate personalized recommendations aimed at enhancing student well-being and academic performance. By analyzing diverse student data profiles, including metrics from wearable devices and qualitative feedback from academic reports, we conducted sentiment analysis to assess students’ emotional states. The results indicate that LLMs can effectively process and analyze textual data, providing actionable insights into student engagement and areas needing improvement. This approach demonstrates the potential of LLMs in educational settings, offering a more nuanced understanding of student needs compared to traditional methods.