Enhancing Psychological Well-being Through AI-Assisted Nutrition: A Study on Female University Students

This study investigates the relationship between AI-assisted nutrition and psychological well-being among women, with a particular focus on female university students. A cross-sectional, descriptive-analytical design was employed, involving a sample of 100 participants aged 18 to 48 years. Data were collected purposefully from students across various academic disciplines, providing a comprehensive perspective on their nutritional and psychological conditions. The study utilized an AI-driven nutrition system to deliver personalized dietary recommendations to improve participants' psychological well-being. The Psychological Well-Being Scale, consisting of 12 items, was used to assess participants' emotional balance, social relationships, and coping abilities. Exploratory factor analysis revealed a robust unidimensional structure with a root mean square of 4.6, accounting for 83% of the variance. Confirmatory factor analysis confirmed a good model fit, and internal consistency was validated with Cronbach’s alpha of 0.860 and McDonald’s omega of 0.864. The average variance extracted (AVE) was 0.67, indicating acceptable construct validity. Although the factor structure was strong, items 11 and 12 were excluded from the final scale due to low factor loadings. The findings suggest that AI-assisted nutritional interventions have the potential to enhance psychological well-being, supporting the integration of AI tools into both mental health and dietary management for women. This study underscores the value of personalized AI-driven dietary recommendations as a tool for promoting emotional and psychological stability.