This article presents an augmented reality-based instruction (ARBI) system for maintenance tasks. A traditional manual instruction method and a computer-assisted instruction method were compared. Three maintenance instruction methods, three task difficulty levels (low, medium, and high), and the user's gender (male and female) were specified as the independent variables in the experimental design. The dependent variables included task completion time and error rate as objective measures, and system usability scale (SUS) and NASA-task load index (NASA-TLX) scores as subjective measures. There were 30 participants (15 males and 15 females) in the experiment. The results indicated that the instruction method and task difficulty significantly affected the task completion time, error rate, SUS, and NASA-TLX. Among the instruction methods, the ARBI method exhibited the highest SUS score, lowest NASA-TLX score, shortest task completion time, and minimum error rate. In conclusion, the proposed ARBI method was beneficial for assisting iPhone maintenance tasks.

中文翻译：

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基于增强现实的维修指导系统可用性评估

本文介绍了一种用于维护任务的基于增强现实的指令 (ARBI) 系统。比较了传统的手工教学方法和计算机辅助教学方法。实验设计中将三种维护指导方法、三种任务难度级别（低、中、高）和用户性别（男性和女性）指定为自变量。因变量包括作为客观指标的任务完成时间和错误率，以及作为主观指标的系统可用性量表 (SUS) 和 NASA 任务负载指数 (NASA-TLX) 得分。实验共有30名参与者（15名男性和15名女性）。结果表明，教学方式和任务难度显着影响任务完成时间、错误率、SUS和NASA-TLX。在教学方法中，ARBI 方法的 SUS 得分最高，NASA-TLX 得分最低，任务完成时间最短，错误率最低。总之，所提出的 ARBI 方法有利于协助 iPhone 维护任务。