As use of generative AI grows across industries, educators must adapt curricula to prepare students for AI-augmented workplaces. While prior research has documented AI adoption among software developers, less is known about how domain-specific engineers (e.g., mechanical, electrical, civil) use GenAI for coding-related work. This pilot study surveyed and interviewed professional engineers to examine employer expectations and current GenAI coding workflows. Our findings suggest that domain-specific engineers use GenAI less frequently than software developers due to safety-critical contexts and legacy systems. Participants emphasized verification and ownership as the most critical professional skills.

Researchers frequently collect position and orientation data from tracking hardware during virtual reality research studies. These data capture important information about participants' movements during experiments, but often result in large and complex datasets that can be challenging to analyze and interpret. We explore the potential of standard statistical measures that could be used to interpret position and orientation tracking data, and discuss advantages and uses of each measure. We further present three new measurement techniques - 95% range, time spent at mode, and coefficient of determination. We then evaluate the effectiveness of each statistical measure to detect differences in position and orientation on two existing data sets. We find that all of the tested measures are effective in discerning known main effects in position and orientation with varying degrees of sensitivity. This work is intended to guide researchers on future analysis and interpretation of motion tracking data sets.

Subjective measures, such as the Simulator Sickness Questionnaire (SSQ), Fast Motion Sickness Questionnaire (FMS), and discomfort scores, are widely used to assess cybersickness, but they often interrupt the user experience and are prone to bias. To overcome these limitations, researchers have also investigated objective indicators, though some approaches, such as using physiological data, can be cumbersome and impractical. Based on the loss of complexity hypothesis, which suggests that certain conditions, such as disease or aging, can produce a reduction of complexity in physiological system dynamics, we conducted an initial investigation of the relationship between movement complexity and cybersickness. We analyzed motion tracking collected from two previous cybersickness studies using the d95 score, a complexity metric derived using principal component analysis. The results revealed a systematic relationship between movement complexity and cybersickness across both experiments. Higher discomfort scores were associated with a reduction in complexity, thereby supporting the loss of complexity hypothesis. Furthermore, the 9-DOF complexity measure, which includes both physical head movement and virtual camera motion, was a more sensitive indicator than the 6-DOF measure computed from physical movements alone. These initial findings suggest that movement complexity may be a useful objective indicator for future cybersickness research.