AI Driven Wearables and Large Language Models for Student Well-Being: A Preliminary Study.

Journal: Studies in health technology and informatics
PMID:

Abstract

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.

Authors

  • Arfan Ahmed
    AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
  • Sarah Aziz
    AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
  • Alaa Abd-Alrazaq
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Rawan AlSaad
    AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
  • Javaid Sheikh
    AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.