The integration of human–robot teams is increasingly essential in dynamic task environments, particularly in sectors like warehouse management, assembly lines, search and rescue operations, material handling, and autonomous driving. This trend leverages the complementary strengths of humans and robots to enhance efficiency and tackle complex objectives. However, significant challenges arise due to differences in task execution, communication modes, empathy, mental model understanding, and adaptability between humans and robots. This survey article examines the complexities of human–robot collaboration (HRC), focusing on the “3Cs” of teamwork: collaboration, communication, and cognition. It introduces a novel 3Cs rating system to evaluate HRC systems, offering a comprehensive analysis of current research trends and identifying key challenges. The findings highlight a prevalent lack of robot adaptation based on human states and performance, underscoring the need for improved communication metrics and consistent definitions of collaborative frameworks. Key contributions include the development of the 3Cs rating system, an in-depth analysis of HRC research trends, and the identification of critical areas requiring further investigation to realize the full potential of human–robot teams. This article aims to guide future research and development, promoting more effective human–robot collaborations.

We report on a trans-Atlantic PhD student mobility program that connects two graduate research training initiatives in the US and Ireland, centered on developing future researchers in artificial intelligence (AI) and machine learning (ML). We discuss both the structure of the student exchange experiences and share key learnings from this international collaboration. The most important lesson learned is that providing a structured mobility program and matched visiting pairs is a highly effective way to improve learning outcomes compared to more typical ad-hoc individual visits.

Our AI-focused traineeships for graduate students integrate research and education components to contribute to diversifying the AI research workforce. We describe the program and introduce multiple strategies to achieve interdisciplinarity, diversity, equity, inclusion, and accessibility. Early evaluation results are included.

Intrusion detection (ID) is one of network security engineers' most important tasks. Textual (command-line) and visual interfaces are two common modalities used to support engineers in ID. We conducted a controlled experiment comparing a representative textual and visual interface for ID to develop a deeper understanding about the relative strengths and weaknesses of each. We found that the textual interface allows users to better control the analysis of details of the data through the use of rich, powerful, and flexible commands while the visual interface allows better discovery of new attacks by offering an overview of the current state of the network. With this understanding, we recommend designing a hybrid interface that combines the strengths of textual and visual interfaces for the next generation of tools used for intrusion detection.

Abstract One of the main factors in all aviation accidents is human error. Therefore, the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP) has identified several human factors safety technologies to address this problem. Some technologies directly address human error either by attempting to reduce the occurrence of errors or by mitigating the negative consequences of errors. However, new technologies and system changes may also introduce new error opportunities or even induce different types of errors. Consequently, a thorough understanding of the relationship between error classes and technology “fixes” is crucial for the evaluation of intervention strategies outlined in the AvSP so that resources can be effectively directed to maximize the benefit to flight safety. This article summarizes efforts to map intervention technologies onto error categories and describes creation of a conceptual framework, identification of applicable taxonomies for each dimension of the framework, and construction of a usable prototype database. The framework consists of a three‐dimensional matrix with axes for the human operator, the task, and the environment. Human errors and technologies cohabit molecules in the matrix linking them. The database allows for taxonomic development in all three areas pertaining to human performance by keeping the taxonomies dynamic.