arXiv subjects

Andrew L. Kun

Publications and source records attributed to Andrew L. Kun.

5 recordsLinked to original sources

arXiv2026

From 911 to Hospital: Challenges and Opportunities for AI Integration in Emergency Medical Services

Artificial Intelligence (AI) is increasingly introduced into healthcare settings, yet its integration into fast-paced, high-pressure domains such as Emergency Medical Services (EMS) remains limited. EMS work unfolds across distinct stages, each characterized by different information needs, constraints, and forms of collaboration. Designing effective AI support requires understanding how AI interventions align with, or disrupt, EMS work across its different stages. We conducted semi-structured interviews with 25 EMS clinicians across the United States to examine how existing technologies currently support emergency services workflows and how they envision opportunities for, and concerns about, future AI-based support across different stages of emergency response. Our analysis reveals the cognitive, social, and procedural factors that enable EMS team coordination, which is grounded in situational awareness across distributed roles. EMS clinicians expressed significant concerns about how AI integration threatens this coordination mechanism across multiple dimensions: legal and privacy issues, technical reliability, contextual sensitivity, professional autonomy, and workflow friction. We propose five design principles for AI systems that augment distributed cognition and situational awareness, enabling EMS teams to deliver effective care under extreme constraints.

cs.HC↗

arXiv2025

Can AI be Accountable?

The AI we use is powerful, and its power is increasing rapidly. If this powerful AI is to serve the needs of consumers, voters, and decision makers, then it is imperative that the AI is accountable. In general, an agent is accountable to a forum if the forum can request information from the agent about its actions, if the forum and the agent can discuss this information, and if the forum can sanction the agent. Unfortunately, in too many cases today's AI is not accountable -- we cannot question it, enter into a discussion with it, let alone sanction it. In this chapter we relate the general definition of accountability to AI, we illustrate what it means for AI to be accountable and unaccountable, and we explore approaches that can improve our chances of living in a world where all AI is accountable to those who are affected by it.

cs.AI↗

arXiv2024

Text a Bit Longer or Drive Now? Resuming Driving after Texting in Conditionally Automated Cars

In this study, we focus on different strategies drivers use in terms of interleaving between driving and non-driving related tasks (NDRT) while taking back control from automated driving. We conducted two driving simulator experiments to examine how different cognitive demands of texting, priorities, and takeover time budgets affect drivers' takeover strategies. We also evaluated how different takeover strategies affect takeover performance. We found that the choice of takeover strategy was influenced by the priority and takeover time budget but not by the cognitive demand of the NDRT. The takeover strategy did not have any effect on takeover quality or NDRT engagement but influenced takeover timing.

cs.HC↗

arXiv2024

AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation

The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.

cs.HC↗

arXiv2023

Designing an Inclusive and Engaging Hybrid Event: Experiences from CHIWORK

Conferences are a key place for training and education. Attendees learn about the state of the art of their field, about relevant methods, and acquire networking skills that can support their work. In the world that was changed by the COVID-19 pandemic, we often need to organize hybrid training and education events, including conferences, with both in-person and remote attendees. Both organizers and attendees are eager for events that are productive, safe, and that bring together a diverse group of our colleagues, across multiple fields of study, multiple countries, as well as with different capacities to travel and attend in-person and remote meetings. However, the best practices for such hybrid events are still under development. In this document we hope to contribute to this development of best practices: we report on our experiences in organizing a small, hybrid conference, and provide the lessons we learned through this process.

cs.HC↗