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

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Student Well-being

Background:

• Traditional methods for assessing student well-being and academic performance are often limited.
• There is a growing need for personalized interventions to support students effectively.
• Wearable technology generates rich data streams that can inform student support.

Purpose of the Study:

• To explore the integration of Large Language Models (LLMs) with wearable technology data.
• To generate personalized recommendations for enhancing student well-being and academic performance.
• To assess the effectiveness of LLMs in analyzing student data for actionable insights.

Main Methods:

• Collected diverse student data, including wearable device metrics and academic report feedback.
• Utilized Large Language Models (LLMs) for sentiment analysis of qualitative student data.
• Analyzed student data profiles to identify patterns related to emotional states and engagement.

Main Results:

• LLMs demonstrated effectiveness in processing and analyzing textual data from academic reports.
• Sentiment analysis provided insights into students' emotional states and engagement levels.
• The integrated approach offered a more nuanced understanding of individual student needs.

Conclusions:

• LLMs combined with wearable technology show significant potential for personalized student support.
• This approach can enhance student well-being and academic performance through data-driven insights.
• LLM-based analysis offers a more sophisticated alternative to traditional student assessment methods.