Christopher Flathmann

1. Mentorship plays an important role in personal growth, skill acquisition, and long-term career development, especially in modern workplaces and organizations (Burke, 1984; Tong & Kram, 2013; Ive...

This study examines how systemic inefficiencies in the Department of Juvenile Justice (DJJ) constrain care for youth and burden staff. Through 15 semi-structured interviews with DJJ employees and subcontractors, analyzed using reflexive thematic analysis and informed by Critical Race Theory, we surface breakdowns in inter-agency coordination, case management processes, and fragmented documentation across tools, all exacerbated by workforce shortages. Participants described how these conditions contribute to misdiagnoses, unsafe placements, delayed responses, and missed opportunities to recognize youth progress, envisioning future Artificial Intelligence (AI) tools to mitigate the negative impacts. We situate participants’ cautious hopes for AI within a Human-Centered Responsible AI lens and present researcher-generated design fictions as provocations that imagine advisory tools for pattern detection, trend identification, and documentation support while preserving human judgment. By centering staff experiences and systemic inequities, this work lays the groundwork for the design of future socio-technical interventions in juvenile justice.

As Artificial Intelligence (AI) technologies begin to become more fundamental to society, their design and integration as teammates that work alongside humans will become commonplace. Recently, research into these human-AI teams has shown that the perceptions humans form of the capabilities and goals of their AI and human teammates differ. However, while humans perceive their teammates differently, it is still unclear whether these perceptual differences extend to the actual teamwork humans have with their teammates. To address this gap, this article reports on a mixed-methods research study of 32 human-AI teams with 2 human teammates and 1 AI teammate. Human and AI teammates occupied similar roles and were tasked with completing a team task in an esports environment. Based on this team task, participants rated the quality of teamwork they experienced with their human and AI teammate, and an interview was conducted to identify why these perceptions may have developed during the team task. Quantitative results show that when working with both another human and an AI teammate, participants perceived a stronger level of teamwork (i.e., interdependence, information certainty, and mutual dependence) and less conflict with their other human teammate compared to their AI teammate. Additionally, qualitative insights highlight that these perceptual differences were formed due to participants having greater expectations for their AI teammates, which may not have been met, and finding it easier to form a strong mental model of their human teammate compared to their AI teammate. Based on these results, this article discusses the potential downstream impacts that teamwork differences could have on human-AI teams and how to address the root causes of these differences to improve team effectiveness.

AI systems are rapidly being placed in positions where they can directly influence decisions that require a strong consideration of ethics. This article reports on an experiment in which humans worked with a teammate acting as an expert advisor with expert-level knowledge, who informed and influenced their ethical decision-making. The identity of this expert advisor was manipulated to be either human or AI, and the influence exerted by the expert advisor was manipulated to be either low or high. Further, participants completed four scenarios, each exploring a different ethically charged decision. Results indicated that working with an AI teammate expert advisor led participants to perceive significantly lower levels of stress and responsibility when making ethical decisions, but AI expert advisors were also perceived as performing worse than human expert advisors. Additionally, when expert advisors exerted high influence levels, participants felt significantly less stress during the decision-making process. Finally, the scenario and ethical decisions made by participants had pervasive effects on trust, trustworthiness, perceived performance, and perceived power for both human and AI expert advisors. Future research efforts must ensure that the use of AI expert advisors in human-AI teams does not reduce the responsibility humans bear when making various ethical decisions.

Research to date in AI and dementia has primarily focused on medical and care-giver applications. This study complements prior work by collecting qualitative data from individuals with Mild Cognitive Impairment (MCI), and mild-to-moderate dementia, uncovering their perspectives, envisioned use cases, and anticipated challenges with generative AI applications. Findings uncover ways people with MCI and dementia want to use AI to engage in resilience building activities related to fostering wellness, building social connections, and finding purpose. However, each of these use cases came with tradeoffs in embracing healthy thinking. Based on these findings, we discuss directions for future work in AI for resilience building in dementia. We intend for this work to lay the groundwork for future research in AI design and development to empower people living with dementia to engage in resilience building activities, which may have lasting positive impacts on their mental health and well-being.

With the emergence of new AI technologies, research on the potential for AI to function as teammates alongside humans has expanded. The recent introduction of highly capable large language models (LLMs) is particularly noteworthy, showing strong potential in human–AI teaming where communication is crucial. However, this novel technology has yet to be validated in human–AI teaming or as a teammate, hindering its application in research and practice. This article presents an empirical online experiment (N = 778) where participants engaged in a real-time and interdependent interaction with a commercially available LLM, with the presentation of the LLM manipulated to be either a tool or a teammate. Results show that when compared to presenting an LLM as a teammate rather than a tool significantly increases trust and significantly impacts the sentiment humans have when talking with their AI, with LLM teammates seeing more positive sentiment. Perceptions of trust, acceptance, and performance were generally high for LLMs presented as teammates. Despite these impacts, participants’ prior experiences with AI technology were still shown to predict the perceptions they formed with their AI teammate. Based on these findings, this article presents an important empirical result, which is that presenting highly capable AI, such as LLMs, as teammates can improve perception and interaction compared to presenting an AI as a tool. In turn, a discussion is had on how future research can continue to identify when and how to introduce LLMs and other AI technologies as teammates.

This study examines the barriers and facilitators associated with the integration of artificial intelligence (AI) and extended reality (XR) technologies in aircraft inspection, aiming to enhance operational efficiency and ease of use as well as to reduce inspection time. Semi-structured interviews were conducted with 43 aircraft technicians, including 10 online and 33 in-person participants. The in-person interviews focused on understanding current challenges in aviation maintenance and perceptions about integrating AI, whereas the online interviews used four hypothetical inspection scenarios to examine specific barriers and facilitators to adopting AI and XR technologies. A thematic analysis revealed key challenges, including the need for explainable and transparent AI systems to foster user trust, the collection of historical data for predictive maintenance, subsurface defect detection, the impact of fatigue and high workload on task performance, and the cost of inspection training. The study also identified key facilitators, including reduced inspection time through AI and XR assistance, improved ease of inspection and training, and better transfer of expert knowledge to novices. These findings could be useful in developing evidence-based strategies for integrating AI and XR into aircraft inspection processes.

Creating reliable human-AI teams with strong team cognition requires a deeper understanding of how humans perceive and react to various information-sharing tactics employed by an AI teammate. The current study details a factorial survey investigating how participants’ trust in their AI and human teammates changes across various forms of AI information sharing. These information-sharing attributes included backup behavior, explainability, team member statuses, intra- and extra-team changes, augmenting team memory, and control. The study found that participants’ trust in their AI teammate benefited most from information about changes inside and outside the team, but all conditions outperformed the control. Further, the back-up behavior information negatively affected the participants’ trust in their human teammate as the AI corrected the human teammates’ actions. These findings demonstrate the utility of a specific form of situation awareness information, while highlighting that corrective back-up behavior information from an AI may harm perceptions between human teammates.