Each day, individuals set behavioral goals such as eating healthier, exercising regularly, or increasing productivity. While psychological frameworks (i.e., goal setting and implementation intentions) can be helpful, they often need structured external support, which interactive technologies can provide. We thus explored how large language model (LLM)-based chatbots can apply these frameworks to guide users in setting more effective goals. We conducted a preregistered randomized controlled experiment (N = 543) comparing chatbots with different combinations of three design features: guidance, suggestions, and feedback. We evaluated goal quality using subjective and objective measures. We found that, while guidance is already helpful, it is the addition of feedback that makes LLM-based chatbots effective in supporting participants’ goal setting. In contrast, adaptive suggestions were less effective. Altogether, our study shows how to design chatbots by operationalizing psychological frameworks to provide effective support for reaching behavioral goals.

When Artificial Intelligence (AI) reasoning is explained via causal graphs for human oversight, the human-computer interface is the performance bottleneck for decision-supported actions. As explanations grow more complex, humans’ interpretation ability degrades, resulting in ineffective oversight. This paper contributes a quantitative model of human causal reasoning bounds and demonstrates their utility for interpretable AI explanations.

Integrating augmented reality (AR) into industrial assembly processes has great potential, yet most studies use simplified lab tasks and rarely benchmark AR against commercial systems, limiting their industrial relevance. To address this gap, we present a comparative study of immersive AR and non-immersive touchscreen-based assembly support systems on a manufacturing shop floor, with 16 participants assembling two industry-grade assets of different complexity. The results reveal that while AR's immersive capabilities excelled in spatial guidance for complex tasks, the simplicity and reliability of the touchscreen interface proved more effective for simple assemblies. We derive three design implications for deployment practice.

In decision-making problems, the outcome of an intervention often depends on the causal relationships between system components and is highly costly to evaluate. In such settings, causal Bayesian optimization (CBO) exploits the causal relationships between the system variables and sequentially performs interventions to approach the optimum with minimal data. Extending CBO to the multi-outcome setting, we propose *multi-objective Causal Bayesian optimization* (MO-CBO), a paradigm for identifying Pareto-optimal interventions within a known multi-target causal graph. Our methodology first reduces the search space by discarding sub-optimal interventions based on the structure of the given causal graph. We further show that any MO-CBO problem can be decomposed into several traditional multi-objective optimization tasks. Our proposed MO-CBO algorithm is designed to identify Pareto-optimal interventions by iteratively exploring these underlying tasks, guided by relative hypervolume improvement. Experiments on synthetic and real-world causal graphs demonstrate the superiority of our approach over non-causal multi-objective Bayesian optimization in settings where causal information is available.

Implicit Neural Representations (INRs) approximate discrete data through continuous functions and are commonly used for encoding 2D images. Traditional image-based INRs employ neural networks to map pixel coordinates to RGB values, capturing shapes, colors, and textures within the network’s weights. Recently, GaussianImage has been proposed as an alternative, using Gaussian functions instead of neural networks to achieve comparable quality and compression. Such a solution obtains a quality and compression ratio similar to classical INR models but does not allow image modification. In contrast, our work introduces a novel method, MiraGe, which uses mirror reflections to perceive 2D images in 3D space and employs flat-controlled Gaussians for precise 2D image editing. Our approach improves the rendering quality and allows realistic image modifications, including human-inspired perception of photos in the 3D world. Thanks to modeling images in 3D space, we obtain the illusion of 3D-based modification in 2D images. We also show that our Gaussian representation can be easily combined with a physics engine to produce physics-based modification of 2D images. Consequently, MiraGe allows for better quality than the standard approach and natural modification of 2D images.

A key problem in the adoption of exoskeletons in industry is that workers are incorrectly fitting the device, leading to discomfort and suboptimal functioning of the exoskeleton. Although biomechanical modeling and design optimization strategies have tried to resolve this issue, we propose a user-centric, real-time fitting aid to guide and control the correct fitting process, which has not been achieved in practice. Inspired by previous work that uses exergame-like feedback to instruct a user, we compared augmented reality (AR)-based visual cues to guide the accurate fitting of a passive upper limb exoskeleton. We selected visual cues through a systematic literature review and evaluated their efficacy and usability for different aspects of exoskeleton fitting in a study with sixteen participants. The study outcome suggests a statistically significant preference for a semi-transparent overlay instead of a more abstract arrow-based method. Moreover, the results indicate high usability and satisfaction with our approach, improved user acceptance, and potentially enhanced fitting accuracy. These findings advance understanding of the viability of exergame-like real-time guidance as a means to increase exoskeleton acceptance and adoption in industrial settings.

The successful deployment of augmented reality (AR) in the industry for on-the-job guidance depends heavily on factors such as the availability of required expertise, existing digital content and other deployment-related criteria such as a task's error-proneness or complexity. Particularly in idiosyncratic manufacturing situations involving customised products and diverse complex and non-complex products and its variants, the applicability and attractiveness of AR as a worker assistance system is often unclear and difficult to gauge for decision-makers. To address this gap, we developed a decision support tool to help prepare customised deployment strategies for AR-based assistance systems utilising manual assembly as the main example. Consequently, we report results from an interview study with sixteen domain experts. Furthermore, when analysing captured expert knowledge, we found significant differences in criteria weighting based on task complexity and other factors, such as the effort required to obtain data.

Collaborative robots (cobots) are a promising technology for frontline workers in industry. They can support tasks that cannot be fully automated but are repetitive, fatiguing, boring, or dangerous for humans. Although cobots are explicitly designed to work with humans, they remain primarily non-intuitive and difficult to collaborate with. Thus, there is a need for new interaction approaches to facilitate efficient human-robot collaboration. Recently, we could see emerging examples of using augmented reality (AR) to assist a worker in collaborative task execution with a cobot. However, for such an approach to provide truly efficient support for the seamless bimanual task execution, we need to first investigate interaction methods offered by an AR interface. To that end, we performed a study with sixteen participants to compare eye-gaze and hand-ray pointing methods for part selection in collaborative, manual assembly tasks. The results of our study show that both techniques provide similar perceived usability, with the eye-gaze selection leading to significantly shorter completion times.

The primary goal behind the system presented in this paper is to investigate the efficacy of using virtual reality (VR) for showcasing sizable medical equipment. Specifically, we focused on a mobile magnetic resonance imaging (MRI) scanner mounted on a truck trailer. The latter is integral to the mobile MRI setup and must be presented as part of the immersive experience. Therefore, we not only have to depict the medical apparatus but also provide the means of understanding its surroundings. This is especially important to radiologists and other medical personnel to ascertain if a given mobile medical facility fulfills their needs and wants. Furthermore, despite such MRI devices being designed for mobility, their long-distance transportation can be time-consuming, troublesome and expensive. Therefore, we can observe the need for showcasing such mobile MRI units without additional cost and burden related to transportation. To achieve this, we designed an immersive environment in which the users can interact with the real-life scale 3D model of a mobile MRI. In addition, we also verified the usability and expressiveness of our system using established heuristical approaches.

Numerous prior studies have investigated real-time assembly instructions using Augmented Reality (AR). However, most such experiments were conducted in laboratory settings with simplistic assembly tasks, failing to represent real-world industrial conditions. To ascertain to what extent results obtained in a laboratory environment may differ from studies in actual industrial environments, we carried out a user study with 32 manufacturing apprentices. We compared assembly task execution results in two settings, a classroom and an industrial workshop environment. To facilitate the experiments, we developed AR-guided manual assembly systems for simple and more complex assets. Our findings reveal a significantly improved task performance in the industrial workshop, reflected in faster task completion times, fewer errors, and subjectively perceived higher flow. This contradicted participants' subjective ratings, as they expected to perform better in the classroom environment. Our results suggest that the actual manufacturing environment is critical in evaluating AR systems for real-world industrial applications.

In recent years, additive manufacturing (AM) techniques have transcended their typical rapid prototyping role and become viable methods to directly manufacture end products in a highly versatile manner. Due to its low cost and relative ease of use, fused deposition modeling (FDM) has become the most universally applied AM technology. Nonetheless, skilled operators are often still required to perform maintenance, diagnostic, and repair tasks. Such operators need to be adequately trained. Here, Augmented reality (AR) technology could be used to automate this training and help to promptly provide new operators with the necessary skills to perform specific tasks as required. However, the most effective approach to designing such AR-based assistance systems has not yet been fully explored. Consequently, we address this need by reporting on how to design such guiding systems using well-known design engineering methodologies. We then further assess the applicability of our approach through a user study with domain experts. In addition, we complete our assessment with heuristical verification of system expressiveness to reason about the influence of cognitively important components of the AR interface on the operators.