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.
