Industrial and academic laboratories are undergoing a paradigm shift in process technology from batch to modular flow. Implementation of modular flow processes can enable more efficient operation with superior throughput, scalability, and safety factors owing to superior transport and reaction kinetics. However, both fine chemical and pharmaceutical industries have historically run exclusively batch processes, highlighting a substantive knowledge gap in industrial workforce training in designing and building modular flow systems. To address this gap in knowledge, we developed an augmented reality (AR) program, FlowAR, that aims to teach users how to build modular flow packed bed reactors. FlowAR is a voice-activated program in which users assemble physical objects to build the modular flow reactor that are observed in the FlowAR program with accompanying narration of each step. To compare the learning experience and effectiveness of FlowAR when compared to traditional assembly methods, 34 participants were randomly assigned to AR (experimental)and non-AR (control, i.e., written instructions) conditions to learn and assemble a flow chemistry packed bed column. We obtained pre-and post-questionnaires from the participants with quantitative and qualitative questions on their learning experience. Inspection of the data from our assessments revealed that users in the experimental condition recall the procedure and parts at a higher level when compared to the control condition. In addition, FlowAR resulted in more content clarity and a much more enjoyable and effective experience compared to the control conditions. This provides evidence that voice-activated AR programs can be used to enable students to better assemble complex instruments in an asynchronous format.