Work

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

Year: 2025

Type: book-chapter

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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250044

Authors: Arfan Ahmed, Sarah Aziz, Alaa Abd-Alrazaq, Rawan Alsaad, Javaid Sheikh

Institutions: Weill Cornell Medical College in Qatar, Weill Cornell Medicine

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDGs: Quality education, Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4409266161

FWCI: 3.808

Cites:

Cited by: 1

Related to: 10