A data-driven insight to enhancing stress management through chatbot interaction among undergraduate students

Abstract

Student stress management at the undergraduate level is a significant issue in the educational world. Therefore, this stress is a challenging issue that needs to be dealt with. As higher educational requirements stack up and their challenges grow, most students struggle to keep the required balance between their studies and preparation. This study establishes a deeper investigation of stress management among undergraduate students using machine-learning algorithms to identify factors contributing to stress and provide solutions. The research aims to illuminate the fundamental causes and health implications of stress for students. Through surveys and questionnaires, the study categorizes stress stages, identifying patterns and enabling the use of new stress management strategies. The findings are used to address concerns shared by undergraduate students and determine interventions to help them cope effectively which aim to provide students with the strength, knowledge, resources, and support needed to manage stress effectively and live a fulfilling life during their university years and future years. The findings will be used to develop policies and programs backed by science to ensure emotional and academic success for students.