EUNOIA: An AI-Powered Platform for Psychological Well-Being Assessment Using the Ryff Scale
Year: 2026
Type: conference-paper
Abstract: This study presents an AI-powered platform designed to automate psychological well-being assessments using the Ryff Scale of Psychological Well-Being. The primary purpose of this study is to address the challenges of manual administration, scoring, and interpretation of the Ryff Scale of PWB by developing an intelligent and automated platform that ensures accuracy, consistency, and accessibility in evaluating students’ psychological well-being. The system implements both the 42-item and 84-item Ryff Scales, performing automated reverse scoring, subscale and total score computation, and classification into At-Risk, Moderate, and Healthy levels of well-being using the fixed-criteria method. The platform integrates an AI-powered module using Mistral 7B Instruct, which generates personalized feedback and recommendations subject to counselor validation before dissemination. The system underwent black-box, white box, and UTAUT-based user acceptance testing, demonstrating functional accuracy of over 95% and an efficient completion time of 10 minutes for the 42-item scale. Results reveal that the platform effectively streamlines assessment workflows, minimizes human error, and enhances the delivery of data-driven insights for counselors. Although the system faces limitations related to internet dependency, response latency, and device compatibility, findings indicate its strong potential to support educational institutions in automating psychological well-being assessments.
Institution: University of the Immaculate Conception
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7204597451