A gamification training system designed according to a mental model structure: A case study of universal robots

As part of the industrial revolution, the collaborative robot (cobot) has become increasingly important in Industry 5.0. However, the most significant barrier for the industry to adopt the cobot is a lack of knowledge and skills. Therefore, e-learning should be designed to support high-performance learning. The purpose of this study is to incorporate the concept of gamification and a mental model learning structure into the design of a cobot training system and investigate its effectiveness. The experiments were divided into three conditions: current e-learning, gamification based on the current e-learning structure, and gamification based on a mental model structure. The results showed differences in effectiveness in terms of the success rate of using the cobot after training. Participants who were trained by current e-learning, gamification based on the current e-learning structure, and gamification based on the mental model structure achieved success rates of using the cobot for a basic pick-and-place task of 15%, 53%, and 74%, respectively. Besides, participants who learned through gamification based on the mental model structure spent less time on the task and made fewer errors than the participants who learned through the other conditions. Regression analyses supported the structural relationships proposed in Technology Acceptance Model, demonstrating the role of Attitude Towards, together with Perceived Usefulness, in predicting Behavioral Intention.