Autoimmune encephalitis is increasingly recognized as a neurologic cause of acute mental status changes with similar prevalence to infectious encephalitis. Despite rising awareness, approaches to diagnosis remain inconsistent and evidence for optimal treatment is limited. The following Canadian guidelines represent a consensus and evidence (where available) based approach to both the diagnosis and treatment of adult patients with autoimmune encephalitis. The guidelines were developed using a modified RAND process and included input from specialists in autoimmune neurology, neuropsychiatry and infectious diseases. These guidelines are targeted at front line clinicians and were created to provide a pragmatic and practical approach to managing such patients in the acute setting.

Research on empathy has been surging in popularity in the engineering design community since empathy is known to help designers develop a deeper understanding of the users’ needs. Because of this, the design community has become more invested in devising and assessing empathic design activities. However, research on empathy has been primarily limited to individuals, meaning we do not know how it impacts team performance, particularly in the concept generation and selection stages of the design process. Specifically, it is unknown how the empathic composition of teams, defined here as the average (elevation) and standard deviation (diversity) of team members’ empathy, would impact design outcomes during nominal group concept generation and early concept screening. Therefore, the goal of the current study is to investigate the impact of team empathy on nominal group concept generation and early concept screening in an engineering design student project. This was accomplished through a computational simulation of 13,482 teams of non-interacting brainstorming individuals generated by a statistical bootstrapping technique. This simulation drew upon a design repository of 806 ideas generated by first-year engineering students. The main findings from the study indicated that the impact of the elevation and diversity of different components of team empathy varied depending upon the specific design outcome (number of ideas, overall creativity, elegance, usefulness, uniqueness) and design stage (concept generation and concept screening). The results from this study can be used to guide team formation in engineering design.

Mindfulness-based interventions (MBIs) for engineering design are promising for their stress management, cognition and well-being benefits. Prior work concluded that engineering design is stressful and that each engineering design stage has unique stressors. This non-randomized study investigated the effect of an MBI on students’ cognitive stress and final design creativity during a multistage, hands-on design assessment. Data were collected using surveys, project deliverables and follow-up interviews. While no significant increase was found in students’ measured state mindfulness due to the intervention, students in the MBI condition were more likely to perceive the intervention positively compared to students in the control condition (alternative use tasks). Students in both conditions were found to have similar levels of state stress, which indicates that the MBI had no observable effect on students’ measured stress during design. Although students in the MBI condition were found to produce higher-quality final designs, there were no differences in design creativity or novelty. When data were clustered to identify types of student experiences, state mindfulness was found to meaningfully contribute, but state stress was not. Future research should continue to investigate MBIs in engineering design as a potential approach to improve design education and outcomes.

Engineering design research has focused on developing and refining methods and evaluating design education in design education, design research and design in practice. One important aspect that is not thoroughly investigated is the influence of bias on design within these spaces of design. Bias is known to impact the interpretation of information, decision-making and practices in all areas. These factors are vital in engineering design education, practice and research, emphasizing the importance of investigating bias. The first goal of this study is to highlight and synthesize existing bias research in design education, research and practice. The second goal is to identify areas where bias may be under-researched or under-reported in design. To achieve these goals, a comparative analysis is performed against a comparable field: medicine. Many parallels exist between both fields. Patient–provider and designer–end-user relationships are comparable. Medical education is comparable to design education with the cooperative, inquiry-based and integrated learning pedagogy approaches. Lastly, physicians and design engineers both solve cognitively complex systems-oriented problems. Leveraging research on bias in medicine enables us to highlight gaps in engineering design. Recommendations are made to help design researchers address these gaps.

The evolution of Artificial Intelligence (AI) and Machine Learning (ML) enables new ways to envision how computer tools will aid, work with, and even guide human teams. This paper explores this new paradigm of design by considering emerging variations of AI-Human collaboration: AI used as a design tool versus AI employed as a guide to human problem solvers, and AI agents which only react to their human counterparts versus AI agents which proactively identify and address needs. The different combinations can be mapped onto a 2×2 AI-Human Teaming Matrix which isolates and highlights these different AI capabilities in teaming. The paper introduces the matrix and its quadrants, illustrating these different AI agents and their application and impact, and then provides a road map to researching and developing effective AI team collaborators.

Set-based concurrent engineering (SBCE), a process that develops sets of many design candidates for each subproblem throughout a design project, proposes several benefits compared to point-based processes, where only one design candidate for each subproblem is chosen for further development. These benefits include reduced rework, improved design quality, and retention of knowledge to use in future projects. Previous studies that introduce SBCE in practice achieved success and had very positive future outlooks, but SBCE encounters opposition because its core procedures appear wasteful as designers must divide their time among many designs throughout the process, most of which are ultimately not used. The impacts of these procedures can be explored in detail through open-source computational tools, but currently few exist to do this. This work introduces the Point/Set-Organized Research Teams (PSORT) modeling platform to simulate and analyze a set-based design process. The approach is used to verify statements made about SBCE and investigate its effects on project quality. Such an SBCE platform enables process exploration without needing to commit many projects and resources to any given design.

The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design.

Idea selection is crucial in design as it impacts the outcome of a project. A collaborative design activity could be considered as a social process where the interactions and individual states (such as the importance in the team and self-efficacy level) could affect decision-making. It is often seen in design teams that some individuals, referred to as ‘influencers’ in the article have more capacity to influence than others, hence they govern the team process for better or worse. Due to the limited work done in the past to study the effect of these influencers on design outcomes, the work aims at increasing the understanding by presenting some insights from its agent-based simulation. The simulation results show how different influencer team compositions affect design outcomes in terms of quality and exploration of the solutions. The idea selection starts with the agents who are ready with their solution in their ‘mind’. The work presented in this article describes a framework for simulating decision-making during idea selection by considering the influencer and majority effect. The empirical study presented in the article verifies the model logic, that is, the presence of influencer and the majority during idea selection and supports the assumption that individuals’ agreement on solutions proposed by other team members depends on the degree of influence and past agreement. The results of the simulation show that teams with well-defined influencers produced solutions with higher variety and had more uniform contributions from team members, but also produced solutions of lower quality.

Engineering design induces mental stress for students and the sources of stress for each stage of design are unique. Therefore, strategies are needed to manage the stress of engineering design that are applicable across the design process. This study investigated the effect of a brief mindfulness-based intervention on first-year students’ cognitive stress during concept generation, concept selection and physical modelling. It was found that the mindfulness-based intervention did increase one aspect of students’ state mindfulness (though the effect was small). While prior work indicates that increased mindfulness can lower perceived stress, the increase in students’ state mindfulness during this study was not found to have an observable impact on students’ stress experience. However, students were receptive to completing a mindfulness-based activity in-class and perceived multiple benefits. Physical modelling was the most stressful of the design tasks while concept generation and concept selection produced similar levels of stress. Students used five reoccurring mechanisms for coping with the stress of design including focusing on the task, minimising the importance of their performance, breathing, taking a break and avoidance/distraction. More research should be conducted with longer duration mindfulness-based interventions to understand their potential as a stress management strategy for engineering design.

Over the past decade, engineering design research has seen a significant surge of the discussion of empathy. As such, design researchers have been devoted in devising and assessing empathic design activities. While prior research has examined the utility of empathic design experiences on driving creative concept generation, little is known about the role of a designer's empathic tendencies in driving creative concept generation and selection in an engineering design project. Without this knowledge, we cannot be sure if, when, or how empathy influences the design process. Thus, the main goal of this paper was to identify the role of trait empathy in creative concept generation and selection in an engineering design student project. In order to achieve this objective, a study was conducted with 103 first-year engineering students during two design stages of an 8-week design project (concept generation and concept selection). The main findings from this paper highlighted that empathic concern tendencies positively impacted the generation of more ideas while personal distress tendencies negatively impacted the generation of more ideas. During concept selection, perspective-taking tendencies positively impacted participants’ propensity for selecting elegant ideas. This research took the first step in encouraging empirical investigations aimed at understanding the role of trait empathy across different stages of the design process.

It is known that wherever there is human interaction, there is social influence. Here, we refer to more influential individuals as “influencers”, who drive team processes for better or worst. Social influence gives rise to social learning, the propensity of humans to mimic the most influential individuals. As individual learning is affected by the presence of an influencer, so is an individual's idea generation . Examining this phenomenon through a series of human studies would require an enormous amount of time to study both individual and team behaviors that affect design outcomes. Hence, this paper provides an agent-based approach to study the effect of influencers during idea generation. This model is supported by the results of two empirical experiments which validate the assumptions and sustain the logic implemented in the model. The results of the model simulation make it possible to examine the impact of influencers on design outcomes, assessed in the form of exploration of design solution space and quality of the solution. The results show that teams with a few prominent influencers generate solutions with limited diversity. Moreover, during idea generation, the behavior of the teams with uniform distribution of influence is regulated by their team members' self-efficacy.

Human-artificial intelligent (AI) - assisted teaming is becoming a strategy for coalescing the complementary strengths of humans and computers to solve difficult tasks. Yet, there is still much to learn regarding how the integration of humans with AI agents into a team affects human behavior. Accordingly, this work begins to inform this research gap by focusing specifically on how the communication structure and interaction changes within AI-assisted human teams. The underlying discourse data for this work originates from a prior research study in which teams solve an interdisciplinary drone design and path-planning problem. Several metrics are employed in this work to study team discourse, including count, diversity, content richness, and semantic coherence. Results show significant differences in communication behavior in AI-assisted teams including more diversity and frequency in communication, more exchange of information regarding principal design parameters and problem-solving strategies, and more cohesion. Overall, this work takes meaningful steps towards understanding the effects of AI agents on human behavior in teams, critical for fully building effective human-AI hybrid teams in the future.

Previous research has shown that experienced and novice individuals behave and think differently. Although experienced individuals are better at solving problems, organisations are constantly forming teams of experienced and novice designers to work together on their projects. It is crucial to understand how these teams affect the design outcomes. Therefore, the aim of the study is to investigate how collaborative design teams perform when composed of varying numbers of experienced and novice agents. Specifically in this paper, teams work on a routine task and design outcomes are measured in terms of quality, variety, and exploration of the design space. Since the parameters in the empirical laboratory experiments are difficult to control, an agent-based model was used to simulate these teams. In general, the results show that a team of novice agents with a small number of experienced agents produces solutions of higher quality than an all novice team of agents. However, an all novice team of agents does provide a higher variety of solutions. These results have important implications when teams of experienced and novice designers are formed to work together in practice.

Prototypes are critical design artifacts, and recent studies have established the ability of prototypes to facilitate communication. However, prior work suggests that novice designers often fail to perceive prototypes as effective communication tools, and struggle to rationalize design decisions made during prototyping tasks. To understand the interactions between communication and prototypes, design pitches from 40 undergraduate engineering design teams were collected and qualitatively analysed. Our findings suggest that students used prototypes to explain and persuade, aligning with prior studies of design practitioners. The results also suggest that students tend to use prototypes to justify design decisions and adverse outcomes. Future work will seek to understand novice designers’ use of prototypes as communication tools in further depth. Ultimately, this work will inform the creation of pedagogical strategies to provide students with the skills needed to effectively communicate design solutions and intent.

Intelligent manufacturing (IM) embraces Industry 4.0 design principles to advance autonomy and increase manufacturing efficiency. However, many IM systems are created ad hoc, which limits the potential for generalizable design principles and operational guidelines. This work offers a standardizing framework for integrated job scheduling and navigation control in an autonomous mobile robot driven shop floor, an increasingly common IM paradigm. We specifically propose a multi-agent framework involving mobile robots, machines, humans. Like any cyberphysical system, the performance of IM systems is influenced by the construction of the underlying software platforms and the choice of the constituent algorithms. In this work, we demonstrate the use of reinforcement learning on a sub-system of the proposed framework and test its effectiveness in a dynamic scenario. The case study demonstrates collaboration amongst robots to maximize throughput and safety on the shop floor. Moreover, we observe nuanced behavior, including the ability to autonomously compensate for processing delays, and machine and robot failures in real time.

The engineering design process can produce stress that endures even after it has been completed. This may be particularly true for students who engage with the process as novices. However, it is not known how individual components of the design process induce stress in designers. This study explored the cognitive experience of introductory engineering design students during concept generation, concept selection and physical modelling to identify stress signatures for these three design activities. Data were collected for the design activities using pre- and post-task surveys. Each design activity produced distinct markers of cognitive experience and a unique stress signature that was stable across design activity themes. Rankings of perceived sources of stress also differed for each design activity. Students, however, did not perceive any physiological changes due to the stress of design for any of the design activities. Findings indicate that physical modelling was the most stressful for students, followed by concept generation and then concept selection. Additionally, recommendations for instructors of introductory engineering design courses were provided to help them apply the results of this study. Better understanding of the cognitive experience of students during design can support instructors as they learn to better teach design.

The performance of a design team is influenced by each team member’s unique cognitive style – i.e., their preferred manner of managing structure as they solve problems, make decisions, and seek to bring about change. Cognitive style plays an important role in how teams of engineers design and collaborate, but the interactions of cognitive style with team organization and processes have not been well studied. The limitations of small-scale behavioral experiments have led researchers to develop computational models for simulating teamwork; however, none have modeled the effects of individuals’ cognitive styles. This paper presents the Kirton Adaption–Innovation Inventory agent-based organizational optimization model (KABOOM), the first agent-based model of teamwork to incorporate cognitive style. In KABOOM, heterogeneous agents imitate the diverse problem-solving styles described by the Kirton Adaption-Innovation construct, which places each individual somewhere along the spectrum of cognitive style preference. Using the model, we investigate the interacting effects of a team’s communication patterns, specialization, and cognitive style composition on design performance. By simulating cognitive style in the context of team problem solving, KABOOM lays the groundwork for the development of team simulations that reflect humans’ diverse problem-solving styles.

Recently, design researchers have begun to use neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) to understand a variety of cognitive processes relevant to design. However, common neuroimaging analysis techniques require significant assumptions relating temporal and spatial information during model formulation. In this work, we apply hidden Markov Models (HMM) in order to uncover patterns of brain activation in a design-relevant fMRI dataset. The underlying fMRI data comes from a prior research study in which participants generated solutions for twelve open-ended design problems from the literature. HMMs are generative models that are able to automatically infer the internal state characteristics of a process by observing state emissions. In this work, we demonstrate that distinct states can be extracted from the design ideation fMRI dataset, and that designers are likely to transition between a few key states. Additionally, the likelihood of occupancy within these states is different for high and low performing designers. This work opens up the door for future research to investigate the patterns of neural activation within the discovered states.

Cognitive assistants such as IBM Watson and Siri are at the forefront of social and technological innovation and have the potential to solve many unique problems. However, the lack of standardization and classification within the field impedes critical analysis of existing cognitive assistants and may further inhibit their growth into more useful applications. This paper discusses the development of an ontology, its classes, and subclasses that may serve as a foundation for defining and differentiating CAs. Specifically, the four suggested classes include: learning, intelligence, autonomy, and communication. Various assistants are described and categorized using the proposed system. Our novel ontological framework is the first step towards a classification system for this burgeoning field.