In systems where agents teach skills in VR or digital applications, the focus is often on enhancing skill acquisition efficiency. This study focused on self-efficacy, defined as the belief in one’s ability to succeed in a specific behavior. Despite its significance, discussions in prior research have been qualitative and relied on subjective participant reports. This study aimed to quantitatively investigate the impact of successful personal experiences, observation of others’ successes, and positive reinforcement on self-efficacy in motor skills tasks. It also explores factors such as difficulty adjusting, performance differences between observing others and oneself, and experience sequences, which are often overlooked in self-efficacy discussions. By leveraging VR, we implemented Kendama juggling, which facilitated the observation of others’ successes, provided difficulty levels, and measured quantitative performance. Experiments examined whether modifying conditions such as performance differences, experience sequence, difficulty adjustment, and the presence of rewards would improve self-efficacy. The results suggested that self-efficacy improves when others demonstrate slightly better skills when observing others’ experiences before their own and with rewards, while lowering difficulty levels does not contribute significantly. This study established a methodology for designing assistive systems to improve self-efficacy through quantitative analysis.

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