ACM Transactions on Computer-Human Interaction

Despite the benefits of expert interaction techniques, many users do not learn them and continue to use novice ones. This article aims at better understanding if, when and how users decide to learn and ultimately adopt expert interaction techniques. This ...

Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-...

While developing a story, novices and published writers alike have had to look outside themselves for inspiration. Language models have recently been able to generate text fluently, producing new stochastic narratives upon request. However, effectively ...

This work contributes a research protocol for evaluating human-AI interaction in the context of specific AI products. The research protocol enables UX and HCI researchers to assess different human-AI interaction solutions and validate design decisions ...

Robot Learning from Demonstration (RLfD) allows non-expert users to teach a robot new skills or tasks directly through demonstrations. Although modeled after human–human learning and teaching, existing RLfD methods make robots act as passive observers ...

Human-AI collaboration for decision-making strives to achieve team performance that exceeds the performance of humans or AI alone. However, many factors can impact success of Human-AI teams, including a user’s domain expertise, mental models of an AI ...

Innovations in machine learning are enabling organisational knowledge bases to be automatically generated from working people's activities. The potential for these to shift the ways in which knowledge is produced and shared raises questions about what ...

Human–computer integration is an HCI trend in which computational machines can have agency, i.e., take control. Our work focuses on a particular form of integration in which the user and the computational machine share agency over the user's body, that is,...

When seeking information not covered in patient-friendly documents, healthcare consumers may turn to the research literature. Reading medical papers, however, can be a challenging experience. To improve access to medical papers, we explore four features ...

Graduate students are facing a mental health crisis due to a combination of individual, community, and societal factors. Many existing stress management interventions engage with one factor at a time, typically focusing on providing a user with data about ...

Body ownership illusions (BOIs) occur when participants experience that their actual body is replaced by a body shown in virtual reality (VR). Based on a systematic review of the cumulative evidence on BOIs from 111 research articles published in 2010 to ...

Data breaches are prevalent. We provide novel insights into individuals’ awareness, perception, and responses to breaches that affect them through two online surveys: a main survey (n = 413) in which we presented participants with up to three breaches ...

This article examines the attentional mechanism of in-person collaboration by means of System Dynamics-based simulations using an eye tracking experiment. Three experimental conditions were tested: in-person collaboration, remote collaboration, and single ...

Hypotension during perioperative care, if undetected or uncontrolled, can lead to serious clinical complications. Predictive machine learning models, based on routinely collected EHR data, offer potential for early warning of hypotension to enable ...

Food practices have become an important context for questions around sustainability. Within HCI, sustainable HCI and human-food-interaction have developed as a response. We argue, nevertheless, that food practices as a social activity remain relatively ...