Modeling of the effects of pseudo-success experience through difficulty adjustment on self-efficacy

Abstract

Self-efficacy is a psychological term defined as feeling confident that "I can perform this action in the future. When AI robot systems assist care receivers such as care facilities and hospitals, it is desirable to improve users' self-efficacy by adjusting the difficulty of the target task according to the individual's state. This paper proposes a Kendama task in a VR environment to provide pseudo-successful experiences to model the relationship between difficulty level and self-efficacy. We performed an experiment with 24 participants to investigate the effects of the difficulty level adjustment on self-efficacy. The experimental results suggest that reducing the difficulty level is inappropriate only to improve self-efficacy. Furthermore, it is necessary to increase the difficulty level to leave positive effects on the recall of past and future expectations.