Investigating the Influence of Spatial Ability in Augmented Reality-assisted Robot Programming

TL;DR

This study investigates whether AR enhances learning in robot programming and how spatial ability moderates this effect. Using a randomized between-subjects design (N=71), it compares AR-assisted instruction to conventional methods, measuring learning experience with cognitive load subscales and SUS, and assessing spatial ability via the Mental Rotation Test. Findings show AR does not provide a universal advantage in learning experience, though spatial ability predicts lower extraneous load and higher usability in the conventional condition; importantly, AR attenuates these associations, suggesting a compensatory role for learners with lower spatial skills. The results highlight the potential of AR to reduce dependence on individual cognitive profiles and inform the design of personalized AR-based learning environments in spatially demanding domains like robot programming.

Abstract

Augmented Reality (AR) offers promising opportunities to enhance learning, but its mechanisms and effects are not yet fully understood. As learning becomes increasingly personalized, considering individual learner characteristics becomes more important. This study investigates the moderating effect of spatial ability on learning experience with AR in the context of robot programming. A between-subjects experiment ($N=71$) compared conventional robot programming to an AR-assisted approach using a head-mounted display. Participants' spatial ability was assessed using the Mental Rotation Test. The learning experience was measured through the System Usability Scale (SUS) and cognitive load. The results indicate that AR support does not significantly improve the learning experience compared to the conventional approach. However, AR appears to have a compensatory effect on the influence of spatial ability. In the control group, spatial ability was significantly positively associated with SUS scores and negatively associated with extraneous cognitive load, indicating that higher spatial ability predicts a better learning experience. In the AR condition, these relationships were not observable, suggesting that AR mitigated the disadvantage typically experienced by learners with lower spatial abilities. These findings suggest that AR can serve a compensatory function by reducing the influence of learner characteristics. Future research should further explore this compensatory role of AR to guide the design of personalized learning environments that address diverse learner needs and reduce barriers for learners with varying cognitive profiles.

Figures (7)

• Figure 1: Experimental setup of robotic arm UR5e and RG2 gripper, workpiece (grey cube), pickup (right blue box), and target area (left blue box).
• Figure 2: Teach pendant user interface. Movement tab (a) and program tab (b).
• Figure 3: Apparatus of the control group with tablet device (left) and teach pendant (right).
• Figure 4: User interface of the AR application. Instruction window, TCP and joint movement, emergency button, tool position information, home position, and gripper control, program interface (from left to right).
• Figure 5: Rotation visualization of base joint (a) and shoulder joint (b). Coordinate system at TCP position and waypoint and path visualization of a program (c).