Code-generating Artificial Intelligence has gained popularity within both professional and educational programming settings over the past several years. While research and pedagogy are beginning to cope with this change, computing students are left to bear the unforeseen consequences of AI amidst a dearth of empirical evidence about its effects. Though pair programming between students is well studied and known to be beneficial to self-efficacy and academic achievement, it remains underutilized and further threatened by the proposition that AI can replace a human programming partner. In this paper, we present a controlled pair programming study with 22 participants who wrote Python code under time pressure in teams of two and individually with GitHub Copilot for 20 minutes each. They were incentivized by bonus compensation to balance performance with understanding and were retested individually on the programming tasks after a retention interval of one week. Subjective measures of workload and emotion as well as objective measures of performance and learning (retest performance) were collected. When first programming with AI, participants clearly performed better and had an easier time. On retest, they were not dramatically worse in raw score from the human-human condition, but human-AI pairs lost more of their initial advantage. Additionally, the emotional effect of the human teammate was significantly more positive and arousing as compared to working with Copilot. We recommend that educators strongly consider revisiting pair programming as an educational tool in addition to embracing modern AI.

Augmented reality (AR) may provide supplementary information to support tasks in the physical world, offering the advantage of displaying multiple windows with high flexibility in interface layout. In AR-physical world mixed scenarios, users often need to locate and retrieve target information from virtual multi-window displays. Understanding how to design effective layouts for these interfaces is critical to enhancing visual search performance, a key element of information retrieval. This study examines the effects of depth separation, information density and curvature of virtual multi-window displays on a conjunctive visual search task in AR. Results indicate that reducing information density and introducing curvature significantly reduced both search time and the time taken to decide that the target was absent (task quit time). Although depth separation did not significantly affect search time, it notably reduced quit time. The number of errors was not significantly influenced by any of the factors. Additionally, users preferred a curved display with lower information density that remained within the device's field of view, and their search time was fastest with this layout. Finally, we noticed variations in layout preference and performance changes among individuals, possibly influenced by differences in search strategies.

Optical see-through head-mounted displays (OST-HMDs) allow users to interact with both physical and virtual displays overlaid in the real world within augmented reality (AR) environments. When arranging these displays in space, spatial discontinuity-such as differences in viewing distance or lateral separation in the visual field-may occur. This study examined the effects of displays' spatial discontinuities on multitasking performance and perceived workload in AR, as well as how these effects were influenced by task load. Participants completed a multitasking simulation with four tasks distributed across physical and virtual displays under four display configurations: two configurations with spatial discontinuity, one cross-display configuration without discontinuity, and a baseline configuration where all tasks were shown on the physical display. Each configuration was tested under two task load levels. Results showed that OST-HMD-supported AR significantly impaired multitasking performance, regardless of whether spatial discontinuity between virtual and physical displays was present. Introducing display discontinuities through small depth separations or alignment offsets produced no additional significant influence on performance. The AR configurations also shown increased perceived workload than the baseline, but spatial discontinuity did not significantly affect workload ratings. Task load modulated the effect of display configurations on performance as well as physical and frustration ratings of the perceived workload.

Artificial Intelligence-driven Development Environments (AIDEs) offer developers revolutionary computer programming assistance. There is great potential in incorporating AIDEs into Computer Science education; however, the effects of these tools should be fully examined before doing so. Here, a within-subjects study was conducted to compare the programming performance, workload, emotion, and self-efficacy of seventeen novices coding with and without use of the GitHub Copilot AIDE under time pressure. Results showed that using the AIDE significantly increased programming efficiency and reduced effort and mental workload but did not significantly impact emotion or self-efficacy. However, participants' performance improved with more experience using the AI, and their self-efficacy followed. The results suggest that students who try AIDEs will likely be tempted to use them for time-sensitive work. There is no evidence that providing AIDEs will aid struggling students, but there is a clear need for students to practice with AI to become competent and confident using it.

In this paper, we present MoiréTag—a novel tag-like device that magnifies displacement without active components for indirect sensing of subtle tangible interactions. The device consists of two overlapping layers of stripe patterns with distinct pattern frequencies. These layers create Moiré fringes that can move faster than the actual movement of a layer. Using a customized image processing pipeline, we show that MoiréTag can reliably detect sub-mm movement in real-time (mean error = 0.043 mm) under varying lighting conditions, camera angles, and camera distances. We also demonstrate five applications of MoiréTag to showcase its potential as a low-cost solution to capture and monitor small changes in movement and other physical properties, such as force and volume, by converting them into displacement.

When users reach their arms to different locations in physical space, they often adapt how they move (i.e., kinematic properties of their reaches) depending on the: (1) direction they move, (2) hand they use, and (3) side of the body where the movement occurs. However, it is not yet clear if and how these three properties of reaching tasks may interact to influence users’ behavior when they reach to objects in VR. To address this question, we had users perform virtual hand reaches in five different directions, on both sides of their bodies, using both their dominant and non-dominant hands. The results revealed that users adapted their virtual hand reaching movements in response to changes in all three properties. The findings provide practitioners insights on how to measure and interpret users’ movements, which has applicability in emerging contexts that include detecting VR usability issues and using VR for stroke rehabilitation.

Changes in task demands can have delayed adverse impacts on performance. This phenomenon, known as the workload history effect, is especially of concern in dynamic work domains where operators manage fluctuating task demands. The existing workload history literature does not depict a consistent picture regarding how these effects manifest, prompting research to consider measures that are informative on the operator's process. One promising measure is visual attention patterns, due to its informativeness on various cognitive processes. To explore its ability to explain workload history effects, participants completed a task in an unmanned aerial vehicle command and control testbed where workload transitioned gradually and suddenly. The participants’ performance and visual attention patterns were studied over time to identify workload history effects. The eye-tracking analysis consisted of using a recently developed eye-tracking metric called coefficient K , as it indicates whether visual attention is more focal or ambient. The performance results found workload history effects, but it depended on the workload level, time elapsed, and performance measure. The eye-tracking analysis suggested performance suffered when focal attention was deployed during low workload, which was an unexpected finding. When synthesizing these results, they suggest unexpected visual attention patterns can impact performance immediately over time. Further research is needed; however, this work shows the value of including a real-time visual attention measure, such as coefficient K , as a means to understand how the operator manages varying task demands in complex work environments.

Research on multimodal information processing has seen a surge in recent years. Most of this research suffers from a significant shortcoming: the failure to perform crossmodal matching. Crossmodal matching refers to equating perceived intensities of stimuli across two sensory modalities. This step is critical for avoiding that modality is confounded with other signal properties. Very few studies include this step, and there is no agreed-upon matching technique. An experiment comparing two different approaches shows that even minor variations can lead to significant differences in match values and intraindividual match variability. Our findings highlight the need for developing and employing a valid crossmodal matching procedure for future studies.