It is difficult to imagine a society that does not utilize teams. At the same time, teams need to evolve to meet today’s challenges of the ever-increasing proliferation of data and complexity. It may be useful to add artificial intelligent (AI) agents to team up with humans. Then, as AI agents are integrated into the team, the first study asks what roles can AI agents take? The first study investigates this issue by asking whether an AI agent can take the role of a facilitator and in turn, improve planning outcomes by facilitating team processes. Results indicate that the human facilitator was significantly better than the AI facilitator at reducing cognitive biases such as groupthink, anchoring, and information pooling, as well as increasing decision quality and score. Additionally, participants viewed the AI facilitator negatively and ignored its inputs compared to the human facilitator. Yet, participants in the AI Facilitator condition performed significantly better than participants in the No Facilitator condition, illustrating that having an AI facilitator was better than having no facilitator at all. The second study explores whether artificial social intelligence (ASI) agents can take the role of advisors and subsequently improve team processes and mission outcome during a simulated search-and-rescue mission. The results of this study indicate that although ASI advisors can successfully advise teams, they also use a significantly greater number of taskwork interventions than teamwork interventions. Additionally, this study served to identify what the ASI advisors got right compared to the human advisor and vice versa. Implications and future directions are discussed.

With the ongoing development of simulation technology, classic barriers to social interactions are beginning to be dismantled. One such exchange is encapsulated within education—instructors can use simulations to make difficult topics more manageable and accessible to students. Within simulations that include virtual humans, however, there are important factors to consider. Participants playing in virtual environments will act in a way that is consistent with their real-world behaviors—including their implicit biases. The current study seeks to determine the impact of virtual humans’ skin tone on participants’ behaviors when applying engineering concepts to simulated projects. Within a comparable study focused on a medical training simulation, significantly more errors and delays were made when working for the benefit of dark-skinned patients in a virtual context. In the current study, participants were given a choose-your-own-adventure style game in which they constructed simulated bridges for either a light- ordark-skinned community, and the number of errors and time taken for each decision was tracked. Results are expected to be consistent with previous study, indicating a higher number of errors and less time taken for each decision, although these results may be attenuated by a
lack of time pressure and urgency to the given situations. If these expected results hold, there may be implications for both undergraduate engineering curriculum and real-world engineering endeavors.

Human-agent teams (HATs) are expected to play a larger role in future command and control systems where resilience is critical for team effectiveness. The question of how HATs interact to be effective in both normal and unexpected situations is worthy of further examination. Exploratory behaviors are one that way adaptive systems discover opportunities to expand and refine their performance. In this study, team interaction exploration is examined in a HAT composed of a human navigator, human photographer, and a synthetic pilot while they perform a remotely-piloted aerial reconnaissance task. Failures in automation and the synthetic pilot’s autonomy were injected throughout ten missions as roadblocks. Teams were clustered by performance into high-, middle-, and low-performing groups. It was hypothesized that high-performing teams would exchange more text-messages containing unique content or sender-recipient combinations than middle- and low-performing teams, and that teams would exchange less unique messages over time. The results indicate that high-performing teams had more unique team interactions than middle-performing teams. Additionally, teams generally had more exploratory team interactions in the first session of missions than the second session. Implications and suggestions for future work are discussed.

Traditional usability methods in Human-Computer Interaction (HCI) have been extensively used to understand the usability of products. Measurements of user experience (UX) in traditional HCI studies mostly rely on task performance and observable user interactions with the product or services, such as usability tests, contextual inquiry, and subjective self-report data, including questionnaires, interviews, and usability tests. However, these studies fail to directly reflect a user’s psychological involvement and further fail to explain the cognitive processing and the related emotional arousal. Thus, capturing how users think and feel when they are using a product remains a vital challenge of user experience evaluation studies. Conversely, recent research has revealed that sensor-based affect detection technologies, such as eye tracking, electroencephalography (EEG), galvanic skin response (GSR), and facial expression analysis, effectively capture affective states and physiological responses. These methods are efficient indicators of cognitive involvement and emotional arousal and constitute effective strategies for a comprehensive measurement of UX. The literature review shows that the impacts of sensor-based affect detection systems to the UX can be categorized in two groups: (1) confirmatory to validate the results obtained from the traditional usability methods in UX evaluations; and (2) complementary to enhance the findings or provide more precise and valid evidence. Both provided comprehensive findings to uncover the issues related to mental and physiological pathways to enhance the design of product and services. Therefore, this dissertation claims that it can be efficient to integrate sensor-based affect detection technologies to solve the current gaps or weaknesses of traditional usability methods. The dissertation revealed that the multi-sensor-based UX evaluation approach through biometrics tools and software corroborated user experience identified by traditional UX methods during an online purchasing task. The use these systems enhanced the findings and provided more precise and valid evidence to predict the consumer purchasing preferences. Thus, their impact was “complementary” on overall UX evaluation. The dissertation also provided information of the unique contributions of each tool and recommended some ways user experience researchers can combine both sensor-based and traditional UX approaches to explain consumer purchasing preferences.

Learners' attitudes and beliefs during the initial stages of learning have a profound impact on their future decisions, practice habits, and persistence. In music education, however, surprisingly little research has explored how physical equipment design might influence novices' attitudes and beliefs. The current study addresses this gap by examining how novices' motivation and perception differ based on the physical design of the musical instrument they interact with while learning. Fifty-two adult participants completed an online survey measuring their expectancies (e.g., confidence), value beliefs (e.g., enjoyment, interest, and social merit), and anticipated persistence while attempting to learn the electric guitar. Afterward, participants attempted to learn and perform several beginner-level tasks while using a conventionally designed or ergonomically designed guitar. The conventionally designed guitar was a commercially available model marketed toward beginner and intermediate-level guitarists. In contrast, the ergonomic guitar was a custom model based on expert design recommendations to improve ease of use, comfort, and user experience. Participant learning expectations and values were assessed before and after a one-hour practice session. Results revealed that novices who used the ergonomic guitar reported significant gains in anticipated learning enjoyment. Alternatively, novices who used the conventional guitar exhibited no such change. Beyond this relationship however, the ergonomic guitar was not found to meaningfully affect participants' confidence, interest, physical discomfort, and task difficulty perceptions. Additionally, the ergonomic guitar did not have a statistically significant influence on learning persistence ratings. One important implication extracted from this study is that a single practice session may not provide enough time or experience to affect a novices' attitudes and beliefs toward learning. Future studies may seek to remedy this study limitation by using a longitudinal design or longer practice task trials. Despite this limitation however, this exploratory study highlights the need for researchers, music educators, and instrument manufacturers to carefully consider how the physical design of a musical instrument may impact learning attitudes, choices, and persistence over time. Additionally, this study offers the first attempt at extending the equipment design literature to music education and Expectancy-Value Theory.

Universities and community organizations (e.g., nonprofit organizations, schools, government, and local residents) often form partnerships to address critical social issues, such as improving service delivery, enhancing education and educational access, reducing poverty, improving sustainability, sharing of resources, research, and program evaluation. The efficacy and success of such collaborations depends on the quality of the partnerships. This dissertation examined university-community partnership (UCP) relationships employing stakeholder theory to assess partnership attributes and identification. Four case studies that consisted of diverse UCPs, oriented toward research partnerships that were located at Arizona State University, were investigated for this study. Individual interviews were conducted with university agents and community partners to examine partnership history, partnership relationships, and partnership attributes. The results revealed several aspects of stakeholder relationships that drive partnership success. First, university and community partners are partnering for the greater social good, above all other reasons. Second, although each entity is partnering for the same reasons, partnership quality is different. University partners found their community counterparts more important than their community partners found them to be. Third, several themes such as credibility, institutional support, partner goodwill, quality interpersonal relationships have emerged and add descriptive elements to the stakeholder attributes. This study identifies aspects of UCPs that will be contextualized with literature on the subject and offer significant contributions to research on UCPs and their relational dynamics.