Generative artificial intelligence (GenAI) is increasingly becoming a part of work practices across the technology industry and being used across a range of industries. This has necessitated the need to better understand how GenAI is being used by professionals in the field so that we can better prepare students for the workforce. An improved understanding of the use of GenAI in practice can help provide guidance on the design of GenAI literacy efforts including how to integrate it within courses and curriculum, what aspects of GenAI to teach, and even how to teach it. This paper presents a field study that compares the use of GenAI across three different functions - product development, software engineering, and digital content creation - to identify how GenAI is currently being used in the industry. This study takes a human augmentation approach with a focus on human cognition and addresses three research questions: how is GenAI augmenting work practices; what knowledge is important and how are workers learning; and what are the implications for training the future workforce. Findings show a wide variance in the use of GenAI and in the level of computing knowledge of users. In some industries GenAI is being used in a highly technical manner with deployment of fine-tuned models across domains. Whereas in others, only off-the-shelf applications are being used for generating content. This means that the need for what to know about GenAI varies, and so does the background knowledge needed to utilize it. For the purposes of teaching and learning, our findings indicated that different levels of GenAI understanding needs to be integrated into courses. From a faculty perspective, the work has implications for training faculty so that they are aware of the advances and how students are possibly, as early adopters, already using GenAI to augment their learning practices.

In this research paper we examine undergraduate students' use of and perceptions of generative AI (GenAI). Although the initial hype around ChatGPT has subsided, GenAI applications continue to make inroads across learning activities. Like any other emerging technology, there is a lack of consensus around using GenAI within higher education. Students are early adopters of the technology, utilizing it in atypical ways and forming a range of perceptions and aspirations about it. To understand where and how students are using these tools and how they view them, we present findings from an open-ended survey response study with undergraduate students pursuing information technology degrees. Students were asked to describe 1) their understanding of GenAI; 2) their use of GenAI; 3) their opinions on the benefits, downsides, and ethical issues pertaining to its use in education; and 4) how they envision GenAI could ideally help them with their education. Thirty-seven students provided responses ranging in length from 20 to 300 words for each question. Responses were iteratively coded by researchers to uncover patterns in the data and then categorized thematically. Findings reveal that students' definitions of GenAI differed substantially and included many misconceptions - some highlight it as a technique, an application, or a tool, while others described it as a type of AI. There was a wide variation in the use of GenAI by students, with two common uses being writing and coding. They identified the ability of GenAI to summarize information and its potential to personalize learning as an advantage. Students identified two primary ethical concerns with using GenAI: plagiarism and dependency, which means that students do not learn independently. They also cautioned that responses from GenAI applications are often untrustworthy and need verification. Overall, they appreciated that they could do things quickly with GenAI but were cautious as using the technology was not necessarily in their best long-term as it interfered with the learning process. In terms of aspirations for GenAI, students expressed both practical advantages and idealistic and improbable visions. They said it could serve as a tutor or coach and allow them to understand the material better. We discuss the implications of the findings for student learning and instruction.

Cooperative Systems promise increased performance by enriching environmental perception through shared data. Conversely, the entailed openness of the individual system architectures threatens their safety. Recent works focus on a runtime safety assessment to address this threat and thereby aim for high abstractions to provide general interfaces. On the other hand, uncertainty models of shared data, which are necessary inputs to such approaches, aim for low abstractions to provide detailed representations. The present work addresses the resulting incompatibilities by proposing a Lyapunov-based method to estimate so-called Regions of Safety. We show that these enable analyzing low-level uncertainty models to interface with state-of-the-art run-time safety assessment methods and thereby facilitate self-adaptivity and guaranteed safety of cooperative systems. The approach is evaluated in the simulated scenario of Cooperative Adaptive Cruise Control.