Leveraging LLMs and wearables to provide personalized recommendations for enhancing student well-being and academic performance through a proof of concept

Traditional one-size-fits-all recommendations for student well-being and academic success may not be optimal. Personalized recommendations based on individual data hold promise. This study explores the potential of Large Language Models (LLMs) to generate personalized recommendations for 12 high school students to enhance their well-being and academic performance. We analyzed data from 12 students, including Fitbit data (activity levels, sleep and stress scores), PSQI surveys (sleep quality), and school reports (grades, teacher observations). An LLM model was used to analyze this data and create personalized recommendations for each student. Validator scoring assessed the clarity, actionability, and alignment of recommendations with student data. The LLM generated various recommendations based on different student data profiles (e.g., low activity levels, poor sleep quality). Validation results indicated that the recommendations were generally clear and actionable, with high ratings in both areas, though alignment with student data showed more variability, suggesting areas for improvement. This study demonstrates the potential of LLMs to generate personalized recommendations based on student data, acknowledging the need for further validation with initial validator feedback indicating their value. However, improvements are needed at every stage, including enhancing prompts, refining models, and incorporating advanced data analytics and continuous feedback. Future research, particularly with intervention groups and potentially RCT studies, is crucial to establish causal relationships and validate the recommendations' impact. As this technology evolves, ensuring ethical considerations and data privacy remains essential.