Today's working society tries to integrate more and more impaired workers into everyday working processes. One major scenario for integrating impaired workers is in the assembly of products. However, the tasks that are being assigned to cognitively impaired workers are easy tasks that consist of only a small number of assembly steps. For tasks with a higher number of steps, cognitively impaired workers need instructions to help them with assembly. Although supervisors provide general support and assist new workers while learning new assembly steps, sheltered work organizations often provide additional printed pictorial instructions that actively guide the workers. To further improve continuous instructions, we built a system that uses in-situ projection and a depth camera to provide context-sensitive instructions. To explore the effects of in-situ instructions, we compared them to state-of-the-art pictorial instructions in a user study with 15 cognitively impaired workers at a sheltered work organization. The results show that using in-situ instructions, cognitively impaired workers can assemble more complex products up to 3 times faster and with up to 50% less errors. Further, the workers liked the in-situ instructions provided by our assistive system and would use it for everyday assembly.

Large language models like GPT and Codex drastically alter many daily tasks, including programming, where they can rapidly generate code from natural language or informal specifications. Thus, they will change what it means to be a programmer and how programmers act during software development. This work explores how AI assistance for code generation impacts productivity. In our user study (N=24), we asked programmers to complete Python programming tasks supported by a) an auto-complete interface using GitHub Copilot, b) a conversational system using GPT-3, and c) traditionally with just the web browser. Aside from significantly increasing productivity metrics, participants displayed distinctive usage patterns and strategies, highlighting that the form of presentation and interaction affects how users engage with these systems. Our findings emphasize the benefits of AI-assisted coding and highlight the different design challenges for these systems.

Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) empower users to experience digital realities. Known as distinct technology classes, the lines between them are becoming increasingly blurry with recent technological advancements. New systems enable users to interact across technology classes or transition between them—referred to as cross-reality systems . Nevertheless, these systems are not well understood. Hence, in this article, we conducted a scoping literature review to classify and analyze cross-reality systems proposed in previous work. First, we define these systems by distinguishing three different types. Thereafter, we compile a literature corpus of 306 relevant publications, analyze the proposed systems, and present a comprehensive classification, including research topics, involved environments, and transition types. Based on the gathered literature, we extract nine guiding principles that can inform the development of cross-reality systems. We conclude with research challenges and opportunities.