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.

What makes a human, artificial intelligence, and robot team (HART) succeed despite unforeseen challenges in a complex sociotechnical world? Are there personalities that are better suited for HARTs facing the unexpected? Only recently has resilience been considered specifically at the team level, and few studies have addressed team resilience for HARTs. Team resilience here is defined as the ability of a team to reorganize team processes to rebound or morph to overcome an unforeseen challenge. A distinction from the individual, group, or organizational aspects of resilience for teams is how team resilience trades off with team interdependent capacity. The following study collected data from 28 teams comprised of two human participants (recruited from a university populace) and a synthetic teammate (played by an experienced experimenter). Each team completed a series of six reconnaissance missions presented to them in a Minecraft world. The research aim was to identify how to better integrate synthetic teammates for high-risk, high-stress dynamic operations to boost HART performance and HART resilience. All team communications were orally over Zoom. The primary manipulation was the communication given by the synthetic teammate (between-subjects, Task or Task+): Task only communicated the essentials, and Task+ offered clear and concise communications of its own capabilities and limitations. Performance and resilience were measured using a primary mission task score (based upon how many tasks teams completed), time-based measures (such as how long it took to recognize a problem or reorder team processes), and a subjective team resilience score (calculated from participant responses to a survey prompt). The research findings suggest the clear and concise reminders from Task+ enhanced HART performance and HART resilience during high-stress missions in which the teams were challenged by novel events. An exploratory study regarding what personalities may correlate with these improved performance metrics indicated that the Big Five trait taxonomies of extraversion and conscientiousness were positively correlated, whereas neuroticism was negatively correlated with higher HART performance and HART resilience. Future integration of synthetic teammates must consider the types of communications that will be offered to maximize HART performance and HART resilience.

The implementation of chatbots in customer service is widely prevalent in today’s world with insufficient research to appropriately refine all of their conversational abilities. Chatbots are favored for their ability to handle simple and typical requests made by users, but chatbots have proven to be prone to conversational breakdowns. The study researched how the use of repair strategies to combat conversational breakdowns in a simple versus complex task setting affected user experience. Thirty participants were collected and organized into six different groups in a two by three between subjects factorial design. Participants were assigned one of two tasks (simple or complex) and one of three repair strategies (repeat, confirmation, or options). A Wizard-of-Oz approach was used to simulate a chatbot that participants interacted with to complete a task in a hypothetical setting. Participants completed the task with this researcher-controlled chatbot as it intentionally failed the conversation multiple times, only to repair it with a repair strategy. Participants recorded their user experience regarding the chatbot afterwards. An Analysis of Covariance statistical test was run with task duration being a covariate variable. Findings indicate that the simple task difficulty was significant in improving the user experience that participants recorded whereas the particular repair strategy had no effect on the user experience. This indicates that simpler tasks lead to improved positive user experience and the more time that is spent on a task, the less positive the user experience. Overall, results associated with the effects of task difficulty and repair strategies on user experience were only partially consistent with previous literature.

Team communication facilitates team coordination strategies and situations, and how teammates perceive one another. In human-machine teams, these perceptions affect how people trust and anthropomorphize their machine counterparts, which in turn affects future team communication, forming a feedback loop. This thesis investigates how personifying and objectifying contents in human-machine team communication relate to team performance and perceptions in a simulated remotely piloted aircraft system task environment. A total of 46 participants grouped into teams of two were assigned unique roles and teamed with a synthetic pilot agent that in reality was a trained confederate following a script. Quantities of verbal personifications and objectifications were compared to questionnaire responses about participants’ perceived trust and anthropomorphism of the synthetic pilot, as well as team performance. It was hypothesized that verbal personifications would positively correlate with reflective trust, anthropomorphism, and team performance, and that verbal objectifications would negatively correlate with the same measures. It was also predicted that verbal personifications would decrease over time as human teammates interact more with the machine teammate, and that verbal objectifications would increase. Verbal personifications were not found to be correlated with trust and anthropomorphism outside of perceptions related to gender, albeit patterns of change in the navigator’s personifications coincided with a co-calibration of trust among the navigator and the photographer. Results supported the prediction that verbal objectifications are negatively correlated with trust and anthropomorphism of a teammate. Significant relationships between verbal personifications and objectifications and team performance were not found. This study provides support to the notion that people verbally personify machines to ease communication when necessary, and that the same processes that underlie tendencies to personify machines may be reciprocally related to those that influence team trust. Overall, this study provides evidence that personifying and objectifying language in human-machine team communication is a viable candidate for measuring the perceptions and states of teams, even in highly restricted communication environments.

The Luminosity Lab, located at Arizona State University, is a prototype for a novel model of interdisciplinary, student-led innovation. The model’s design was informed by the following desired outcomes: i) the model would be well-suited for the 21st century, ii) it would attract, motivate, and retain the university’s strongest student talent, iii) it would operate without the oversight of faculty, and iv) it would work towards the conceptualization, design, development, and deployment of solutions that would positively impact society. This model of interdisciplinary research was tested at Arizona State University across four academic years with participation of over 200 students, who represented more than 20 academic disciplines. The results have shown successful integration of interdisciplinary expertise to identify unmet needs, design innovative concepts, and develop research-informed solutions. This dissertation analyzes Luminosity’s model to determine the following: i) Can a collegiate, student-driven interdisciplinary model of innovation designed for the 21st century perform without faculty management? ii) What are the motivators and culture that enable student success within this model? and iii) How does Luminosity differ from traditional research opportunities and learning experiences?
Through a qualitative, grounded theory analysis, this dissertation examines the phenomena of the students engaging in Luminosity’s model, who have demonstrated their ability to serve as the principal investigators and innovators in conducting substantial discovery, research, and innovation work through full project life cycles. This study supports a theory that highly talented students often feel limited by the pace and scope of their college educations, and yearn for experiences that motivate them with agency, achievement, mastery, affinity for colleagues, and a desire to impact society. Through the cumulative effect of these motivators and an organizational design that facilitates a bottom-up approach to student-driven innovation, Luminosity has established itself as a novel model of research and development in the collegiate space.

Communications between air traffic controllers and pilots are critical to national airspace traffic management. Measuring communications in real time made by pilots and air traffic controllers has the potential to predict human error. In this thesis a measure for Deviations from Closed Loop Communications is defined and tested to predict a human error event, Loss of Separation (LOS). Six retired air traffic controllers were recruited and tested in three conditions of varying workload in an Terminal Radar Approach Control Facility (TRACON) arrival radar simulation. Communication transcripts from simulated trials were transcribed and coding schemes for Closed Loop Communication Deviations (CLCD) were applied. Results of the study demonstrated a positive correlation between CLCD and LOS, indicating that CLCD could be a variable used to predict LOS. However, more research is required to determine if CLCD can be used to predict LOS independent of other predictor variables, and if CLCD can be used in a model that considers many different predictor variables to predict LOS.

This action research study utilized a mixed-method approach to better understand the current situation of the research administration community with respect to addressing the training and development needs for new and junior staff within Arizona State University’s Fulton Schools of Engineering and encompass other departments and units at Arizona State University. The study extended on those efforts of support by implementing an innovative resource library as a foundation, to decipher the needs of the research administration community and better equip staff through successful training, development and learning experiences. This study assessed Arizona State University’s research administration training and development platforms and other institutional platforms (e.g., National Council of University Research Administrators, National Science Foundation, Grants.gov, and National Institutes of Health) – to garner the necessary ingredients and components to creatively design, develop and implement the innovative library. This study involved two naturally occurring groups consisting of a cohort of research administration staff with varying levels of experience. Specifically, a group of junior and a group of senior research staff were invited to participate in this study. The groups delivered on their experience, perceptions, evaluations, and ideas, which also aided in the necessary modifications to the library resource. For instance, following the delivery from the group of senior participants’ adjustments and modifications aided in the preparation of the junior participants' performance in the library portal. The junior participants performance experience in the library embodied and measured their perceptions, experience, confidence, and comfort levels. Performances within the site enabled the participants to clearly identify and clarify areas of need within the research administration infrastructure within Fulton Schools of Engineering and at Arizona State University overall. In addition, encouragement for future iterations of the library resource were strongly declared and proposed. The revelations brought about through the discussion modules from both groups gave insight through the eyes of participants (e.g., seniors and juniors); which heightened and strengthened the results of the study. Overall, the outcomes received and tracked through the discussion modules from both groups suggested that the current training and development research administration infrastructure within Arizona State University’s research community needed adjustments.

Analytics are being collected on a day to day basis on just about anything that you can think of. Sports is one of the recent fields that has started implementing the tool into their game. Analytics can be described as an abundance of statistical information that show situational tendencies of other teams and players. It is hypothesized that analytics provide anticipatory information that allows athletes to know what is coming; therefore, allowing them to perform better in real game scenarios. However, it is unclear how this information should be presented to athletes and whether athletes can actually retain the abundance of information given to them. Two different types of presentation methods (Numeric and Numeric plus Graph) and two different amounts of analytic information (High and Low) were compared for baseball players in an online based baseball specific retention survey: High Numeric (excess information shown in spreadsheet format), Low Numeric (key information shown in spreadsheet format), High Numeric plus Graph (excess information shown as a spreadsheet with hitting zone maps), and Low Numeric plus Graph (key information shown as a spreadsheet with hitting zone maps). Athletes produced different retention scores for the type of presentation method given across the whole study. Athletes presented analytic as Numeric plus Graph performed better than athletes in just Numeric condition. Additionally, playing experience had a significant effect on an athlete’s ability to retain analytic information. Athletes with 10 plus years of baseball experience performed better in every condition other than High Numeric plus Graph compared to athletes with less than 10 years of experience. Amount and experience also had an interaction effect that produced statistical significance; those with less experience performed better in conditions with less baseball information given whereas those with more experience were able to handle more baseball information at once. Providing analytic information gives athletes, especially baseball batters, a significant advantage over their opponent; however, ability to retain analytic information depends on how the information is presented and to whom the information is being presented.

Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS O&M safety and efficiency. Previous studies suggest that communication contexts and features could be indicators of communication errors and relevant CIS O&M risks. However, challenges remain for reliable prediction of communication errors to ensure CIS O&M safety and efficiency. For example, existing studies lack a systematic summarization of risky contexts and features of communication processes for predicting communication errors. Limited studies examined quantitative methods for incorporating expert opinions as constraints for reliable communication error prediction. How to examine mitigation strategies (e.g., adjustments of communication protocols) for reducing communication-related CIS O&M risks is also challenging. The main reason is the lack of causal analysis about how various factors influence the occurrences and impacts of communication errors so that engineers lack the basis for intervention.

This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.

This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.

Despite the prevalence of teams in complex sociotechnical systems, current approaches to understanding workload tend to focus on the individual operator. However, research suggests that team workload has emergent properties and is not necessarily equivalent to the aggregate of individual workload. Assessment of communications provides a means of examining aspects of team workload in highly interdependent teams. This thesis set out to explore how communications are associated with team workload and performance under high task demand in all-human and human–autonomy teams in a command and control task. A social network analysis approach was used to analyze the communications of 30 different teams, each with three members operating in a command and control task environment of over a series of five missions. Teams were assigned to conditions differentiated by their composition with either a naïve participant, a trained confederate, or a synthetic agent in the pilot role. Social network analysis measures of centralization and intensity were used to assess differences in communications between team types and under different levels of demand, and relationships between communication measures, performance, and workload distributions were also examined. Results indicated that indegree centralization was greater in the all-human control teams than in the other team types, but degree centrality standard deviation and intensity were greatest in teams with a highly trained experimenter pilot. In all three team types, the intensity of communications and degree centrality standard deviation appeared to decrease during the high demand mission, but indegree and outdegree centralization did not. Higher communication intensity was associated with more efficient target processing and more successful target photos per mission, but a clear relationship between measures of performance and decentralization of communications was not found.