Frontiers in Virtual Reality (Feb 2024)
Features of adaptive training algorithms for improved complex skill acquisition
Abstract
Training complex skills is typically accomplished by means of a trainer or mediator who tailors instruction to the individual trainee. However, facilitated training is costly and labor intensive, and the use of a mediator is infeasible in remote or extreme environments. Imparting complex skills in applications like long-duration human spaceflight, military field operations, or remote medicine may require automated training algorithms. Virtual reality (VR) is an effective, easily programmable, immersive training medium that has been used widely across fields. However, there remain open questions in the search for the most effective algorithms for guiding automated training progression. This study investigates the effects of responsiveness, personalization, and subtask independence on the efficacy of automated training algorithms in VR for training complex, operationally relevant tasks. Thirty-two subjects (16M/16F, 18–54 years) were trained to pilot and land a spacecraft on Mars within a VR simulation using four different automated training algorithms. Performance was assessed in a physical cockpit mock-up. We found that personalization results in faster skill acquisition on average when compared with a standardized progression built for a median subject (p = 0.0050). The standardized progression may be preferable when consistent results are desired across all subjects. Independence of the difficulty adjustments between subtasks may lead to increased skill acquisition, while lockstep in the progression of each subtask increases self-reported flow experience (p = 0.01), fluency (p = 0.02), and absorption (p = 0.01) on the Flow Short Scale. Data visualization suggests that highly responsive algorithms may lead to faster learning progressions and higher skill acquisition for some subjects. Improving transfer of skills from training to testing may require either high responsiveness or a standardized training progression. Optimizing the design of automated, individually adaptive algorithms around the training needs of a group may be useful to increase skill acquisition for complex operational tasks.
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