This qualitative study explores surgeon experiences using wearable biometric devices in the operating room to support surgeon wellbeing and optimize personal performance. Through semi-structured interviews with attending surgeons and trainees across surgical subspecialties, we identified four themes: (1) increased self-awareness for behavior modification, (2) integration into surgical workflows, (3) challenges with wearable biometric device usability, and (4) future opportunities and broader implications. Participants valued devices that offered intuitive and actionable insights with minimal workflow disruption. However, data complexity and fragmented app ecosystems limited participant engagement. A Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework was used to translate qualitative insights into device implementation considerations. Ethical concerns, especially regarding employee privacy and data governance, were noted as potential barriers. These findings highlight both the promise and pitfalls of integrating these devices into surgical practice and suggest the need for thoughtful design, institutional support, and ethical safeguards to maximize device utility and efficacy.
Assessments are used to help gather and analyze information to inform processes and outcomes and are rapidly being reshaped by AI. This systematic review investigates where, why, and when AI is used across the assessment life-cycle and further considers its core functions, design elements, and the ways users engage with them Thirty-eight peer-reviewed studies met our inclusion criteria, each embedding artificial intelligence directly into the assessment process. Together, government facilities and healthcare settings accounted for more than 70% of all documented use cases. Across sectors, the prevailing role of AI was that of a digital assistant, streamlining knowledge capture and evaluation supporting assessment in its role as an expert with a focus on goal-oriented collaboration. These patterns illuminate both the breadth of adoption and the potential of AI as an augmentative partner, offering a roadmap for future assessment design and research.
To prepare business students for an AI-enabled workforce, educators are increasingly integrating generative AI (GenAI) tools into the classroom. Yet little is known about how students actually use these tools for complex tasks. This study examines how undergraduate students use GenAI during a strategic decision-making activity. We analyzed 167 student prompts using a dual-framework approach: the AI-ICE model to assess cognitive engagement, and an inductively developed typology of GenAI co-pilot roles: Content Generator, Task Executor, Advisor, Thinking Partner, and Role-Shifting. While students demonstrated cognitive range, most used GenAI in limited functional roles. Even when tasks called for strategic thinking, students rarely used GenAI as a collaborator. This disconnect between what students think and how they use GenAI highlights a gap in current instructional practice. Our findings offer a functional typology of student-GenAI interaction and practical insights for designing GenAI-enabled learning experiences in business education.
Generative AI technologies like ChatGPT have transformed how people interact with information and services. However, it is unclear (1) what the public knew about chatbots before ChatGPT, (2) how those understandings have evolved, and (3) whether digital divides exist in these understandings. To explore this, we conducted a three-wave national online survey. Wave 1 data were collected just before ChatGPT's 2022 release; Waves 2 and 3 followed one and two years later. Each wave assessed chatbot familiarity (awareness, use, frequency), and Waves 2 and 3 included generative AI. We also measured trust in, support for, and intentions to use chatbots. We analyzed changes over time and examined differences by age, education, and income. Results suggest that people are more familiar with chatbots post-ChatGPT and use of these technologies is associated with more positive attitudes toward them. We further find evidence of digital divides across age and, increasingly, education and income.
Craving, or the subjective, strong desire to use a substance, is a central factor in addiction, and part of the diagnostic criteria for substance use disorders (SUDs). Cravings can also occur for other triggers such as food, and cravings for food and drugs have been found to activate distinct neural pathways in the brain. Recently, physiologic signals from wearable devices have been applied to digitally detect cravings in patients with SUDs. But to date, no studies have explored digital detection of cravings by subtype. We collected continuous physiologic sensor data from N = 12 participants with opioid use disorder (OUD), treated with extended-release buprenorphine (BUP-XR). Data were analyzed to assess whether sensor signals carried differential information that could distinguish between food-, drug- and mixed-craving types. Accelerometer, heart rate and heart rate variability features significantly differed between drug, food and mixed trigger cravings. Cross validated models trained with these features distinguished each type of craving with area under ROC curve ranging from 75%-80%. These findings support the ability of wearable sensor-based digital biomarkers to distinguish craving subtypes in individuals with OUD.
Reliable objective measures of a person's intoxication and impairment from alcohol consumption are not readily available to the public. Wearable biosensors have the potential to provide a ubiquitous on-demand tool to deliver this kind of objective assessment in real world settings. This study evaluated the feasibility of assessing ethanol intoxication in N=28 healthy participants in a police academy's intoxication lab using wrist-worn biosensors to continuously measure heart rate, skin temperature, electrodermal activity, and accelerometry. Participants consumed ad hoc standard alcoholic drinks in a controlled setting and had regular breath alcohol content assessments and underwent standard field sobriety testing. The analysis showed statistically significant changes in each physiologic parameter between the sober and intoxicated periods. An XGBoost model was applied to this data producing machine learning algorithms to identify impairment with an accuracy as high as 0.80. These results demonstrate that it is feasible to assess ethanol intoxication using wrist-worn biosensors.
This minitrack on Location Intelligence Research in System Sciences has the goal to stimulate research in the growing areas of location intelligence, location analytics, and GIS. It encourages research across broad areas of conceptual and empirical investigation in fields as diverse as marketing, logistics, transportation, healthcare, the sharing economy, sustainability, and ethics. Studies were sought that used a variety of theoretical bases, empirical methods, and geographic concepts. The fundamental contribution sought was to determine how geography, location, and place could be interwoven into the knowledge base of the system sciences. The seven papers accepted for the minitrack represent this wide breadth of the field and are summarized in this minitrack introduction.
This study explores the implications of the use of data scarcity solutions on fairness in machine learning, specifically in consumer credit interest rate prediction. We develop a comprehensive taxonomy of Data Scarcity Solutions (DSS) by analyzing academic literature, data science competitions, and practical implementations. We identify six distinct DSS clusters: Data Extension, Pre-Training, Public Data Inclusion, Data Sharing, Federated Learning, and Active Learning. Our evaluation shows that most DSS enhance both performance and fairness, with minimal negative correlation between the two. Notably, approaches incorporating external or synthetic data significantly improve fairness. This research contributes to understanding DSS beyond algorithmic performance, providing a framework for evaluating their societal impact. Furthermore, it offers practitioners a taxonomy to select the right method for tackling data scarcity and addresses fairness concerns in real-world scenarios.
The recent explosion in large language model (LLM) technology has highlighted the challenges of using public generative Artificial Intelligence (AI) tools in classified environments, especially for software analysis. Currently, software analysis falls on the shoulders of static analysis (SA) tools and manual code review, which tend to provide limited technical depth and are often time-consuming in practice. We show that LLMs can be used in unclassified environments to rapidly develop tools that accelerate software analysis in classified environments. Through LLM assistance, our work has produced several avenues for success, and preliminary experimentation has shown significant time savings (similar to 40%) and improved accuracy (similar to 10%) for certain software analysis tasks.
The possibility of hiding information inside a digital medium is often referred to as watermarking or steganography. Since various solutions for image, video, and audio files exist, keeping control over text is challenging due to its limited possibilities. In this paper, we present a new digital text watermarking algorithm to hide a byte-encoded sequence inside an unformatted text. By substituting conventional whitespaces with a set of five similar-looking Unicode spaces, the cover text's structure and length stay untouched while remaining imperceptible to humans. We propose a software design and proof-of-concept multiplatform implementation with a downstream experimental evaluation for robustness, capacity, and visibility. Our findings indicate a stronger concealment and application robustness with limited embedding capacity compared to existing solutions utilizing zero-width spaces.
Generative AI is transforming work for creative professionals. Text-to-image and text-to-video tools provide alternative methods for visual content creation, circumventing the traditional workflows creative professionals have developed. This transformation raises the pressing question of whether these new generative workflows will augment or supplant creative work. To investigate this question, we introduce Generative Disco, a text-to-video system for music visualization, and use it as a technology probe for creative professionals who freelance audiovisual work. In a mixed-methods study (n=12), we observe that professionals found Generative Disco easy to use, highly expressive, and capable of supporting many professional use cases. Its generative workflow enabled professionals to become digital "double hatters", expanding their creative range into adjacent domains. We conclude on how creatives can benefit from this new means of skill mobility.
A probabilistic extension of electricity production cost minimization tools that supports the use of high-fidelity models is introduced. These tools introduced are able to accurately simulate the temporal and spatial relationships affecting system physics and economics. The ability to use high-fidelity models enables accurate calculation of dual variables and their use in defining reliability metrics that accurately represent the economic and engineering characteristics of all resources. In particular, the use of dual variables captures impacts of time-coupled resources and constraints such as storage and limited fuel supply. By bringing economic metrics directly into reliability analysis, we can supplement traditional reliability metrics with economically justified reliability criteria for use by system planning and operations. These techniques and their computational performance are illustrated using a high-fidelity model of a real-sized US market, more specifically ERCOT (Electric Reliability Council of Texas). The model includes MIP based security-constrained unit commitment, realistic operational details, and co-optimization of energy and reserves.
The traditional top-down approach to hospital location decision-making often leads to inefficiencies and fails to address communities' evolving needs. This paper proposes a novel smart ecosystem planning model that leverages advanced technologies, including blockchain and smart contracts, to enhance hospital site selection processes. We propose a model based on the Decentralized Autonomous Organization (DAO) concept to manage healthcare location decisions. Our study focuses on implementing this model in New Zealand (NZ), by analyzing long-term healthcare needs and optimize resource allocation for new healthcare facilities. The proposed smart ecosystem empowers many stakeholders to participate actively in decision-making. This collaborative approach ensures hospital locations are chosen based on comprehensive, data-driven insights, leading to improved healthcare delivery, operational efficiency, and equitable service access. Our findings provide valuable managerial insights for healthcare administrators and policymakers and offer a scalable model that other regions and countries can adopt to enhance healthcare infrastructure planning.
Although video games continue to grow in number and popularity, our understanding of the impact of video game shutdowns on players remains scarce. This is regrettable, as the forceful and permanent removal of video games likely has significant and relevant effects, given how deeply intertwined with and important for players' lives video games can be. Therefore, in this exploratory study, we present the case of the shutdown of Battlefield: Bad Company 2 (BFBC2). Following a netnographic approach, we analyze posts and comments from the BFBC2-related Reddit forum r/badcompany2 as well as 21 interviews with members. Based on this, we present six themes that capture how the shutdown influenced players and explain the role of BFBC2's Reddit community during the shutdown. Thereby, we contribute to the literature on video game shutdowns, online communities, and video game nostalgia. Finally, we provide promising avenues for future research based on our findings.
Multi-Criteria Decision Analysis (MCDA) is an effective tool for decision-making in complex situations characterized by multiple, often conflicting, criteria. However, identifying and determining the relevance of individual criteria can be complicated, especially when the number of criteria is excessively high or when decision experts are not readily available. This paper presents LA-COMET, an innovative method for reducing redundant criteria in MCDA. LA-COMET combines the Characteristic Objects Method (COMET), based on expert preference modeling, with the Regression Lasso technique for automatically selecting relevant variables. Through this combination, our method effectively reduces the number of criteria while maintaining relevant decision-making information. In this method, the Lasso approach also plays the role of an artificial expert, which relies on previous sample evaluations to support COMET model identification. We also present the results of our study, in which we compare the effectiveness of LA-COMET with traditional MCDA methods. In a practical case, the problem of evaluating countries regarding their military potential is considered, where we attempt to re-identify such a model. Our experiments show that LA-COMET achieves significantly better results than classical methods, ensuring high accuracy and relevance of the decisions made.
Online (In)civility and Mental Health kicks off its inaugural year with two articles, quantitative and qualitative in approach, that address impacts and use cases of incivility online. This short article introduces the topic and the articles in the mini-track and points out areas of future research.
