Research on Emotional Experience Strategies of AI Learning Machines Based on Self-Determination Theory

Abstract

Aiming at the problem of insufficient user motivation caused by AI learning machines prioritizing functions over experience, this study conducts an in-depth analysis of the entire learning process based on Self-Determination Theory and User Experience Map. The research reveals three core pain points: lack of goal guidance, insufficient emotional companionship, and weak review mechanisms. Based on this, the paper constructs an emotional experience strategy grounded in Self-Determination Theory: meeting the need for autonomy through dynamic learning situation diagnosis and flexible goal setting, satisfying the need for relatedness via emotional interaction of virtual assistants, and fulfilling the need for competence by utilizing multi-dimensional attribution and visualized growth records. This strategy aims to promote the transformation of AI learning machines from a single tutoring tool to a personalized emotional partner, and effectively improve users' online learning experience.