This paper aims at advancing the fundamental understanding of the affordances of Augmented Reality (AR) as a workplace-based learning and training technology in supporting manual or semi-automated manufacturing tasks that involve both complex manipulation and reasoning. Between-subject laboratory experiments involving 20 participants are conducted on a real-life electro-mechanical assembly task to investigate the impacts of various modes of information delivery through AR compared to traditional training methods on task efficiency, number of errors, learning, independence, and cognitive load. The AR application is developed in Unity and deployed on HoloLens 2 headsets. Interviews with experts from industry and academia are also conducted to create new insights into the affordances of AR as a training versus assistive tool for manufacturing workers, as well as the need for intelligent mechanisms that enable adaptive and personalized interactions between workers and AR. The findings indicate that despite comparable performance between the AR and control groups in terms of task completion time, learning curve, and independence from instructions, AR dramatically decreases the number of errors compared to traditional instruction, which is sustained after the AR support is removed. Several insights drawn from the experiments and expert interviews are discussed to inform the design of future AR technologies for both training and assisting incumbent and future manufacturing workers on complex manipulation and reasoning tasks.

本文旨在促进对增强现实 (AR) 作为一种基于工作场所的学习和培训技术的可供性的基本理解，以支持涉及复杂操作和推理的手动或半自动制造任务。涉及 20 名参与者的受试者间实验室实验在现实生活中的机电组装任务中进行，以研究通过 AR 的各种信息传递模式与传统培训方法相比对任务效率、错误数量、学习、独立性和认知负荷。AR 应用程序在 Unity 中开发并部署在 HoloLens 2 耳机上。还与来自工业界和学术界的专家进行了访谈，以对 AR 作为制造工人的培训与辅助工具的可供性产生新的见解，以及需要智能机制来实现员工和 AR 之间的自适应和个性化交互。研究结果表明，尽管 AR 组和对照组在任务完成时间、学习曲线和指令独立性方面的表现相当，但与传统指令相比，AR 显着减少了错误数量，在取消 AR 支持后，这种情况仍然存在。讨论了从实验和专家访谈中得出的一些见解，为未来 AR 技术的设计提供信息，以培训和协助现有和未来的制造工人处理复杂的操作和推理任务。研究结果表明，尽管 AR 组和对照组在任务完成时间、学习曲线和指令独立性方面的表现相当，但与传统指令相比，AR 显着减少了错误数量，在取消 AR 支持后，这种情况仍然存在。讨论了从实验和专家访谈中得出的一些见解，为未来 AR 技术的设计提供信息，以培训和协助现有和未来的制造工人处理复杂的操作和推理任务。研究结果表明，尽管 AR 组和对照组在任务完成时间、学习曲线和指令独立性方面的表现相当，但与传统指令相比，AR 显着减少了错误数量，在取消 AR 支持后，这种情况仍然存在。讨论了从实验和专家访谈中得出的一些见解，为未来 AR 技术的设计提供信息，以培训和协助现有和未来的制造工人处理复杂的操作和推理任务。