Recent work on augmented reality (AR) has explored the use of adaptive agents to overcome attentional issues that negatively impact task performance. However, despite positive technical evaluations, adaptive agents have shown no significant improvements to user task performance in AR. Furthermore, previous works have primarily evaluated such agents using abstract tasks. In this paper, we develop an agent that observes user behaviour and performs appropriate actions to mitigate attentional issues in a realistic sense-making task in AR. We employ mixed methods to evaluate our agent in a between-subject experiment (N=60) to understand the agent's effect on user task performance and behaviour. While we find no significant improvements in task performance, our analysis revealed that users’ preferences and trust in the agent affected their receptiveness of the agent's recommendations. We discuss the pitfalls of autonomous agents and highlight the need to shift from designing better Human–AI interactions to better Human–AI collaborations.

AI systems are increasingly incorporated into human decision-making. Yet, human decision-makers are often affected by their cognitive biases. In critical settings, such as medical diagnosis, criminal judgment, or information consumption, these cognitive biases hinder optimal decision outcomes, thereby resulting in dangerous decisions and negative societal impact. The use of AI systems can amplify and exacerbate cognitive biases in their users. In this workshop, we seek to foster discussions on ongoing research around cognitive biases in human-AI collaboration and identify future research directions to understand, quantify, and mitigate the effects of cognitive biases. We will explore cognitive biases appearing in various contexts of human-AI collaboration: what can cause them?; how can we measure, model, mitigate, and manage cognitive biases?; and how can we utilise cognitive biases for the greater good? We will reflect on workshop discussions to form a research community around cognitive biases and bias-aware systems.

Attention management systems seek to minimize disruption by intelligently timing interruptions and helping users navigate multiple tasks and activities. While there is a solid theoretical basis and rich history in HCI research for attention management, little progress has been made regarding their practical implementation and deployment. Building sophisticated attention management systems requires a great variety of sensors, task- and user models, and multiple devices while considering the complexity of user context and human behavior. Novel AI technologies, such as generative systems, reinforcement learning, and large language models, open new possibilities to create intelligent, practical, and user-centered attention management systems. This proposed workshop aims to bring together researchers and practitioners from diverse backgrounds to discuss and formulate a research agenda to advance attention management systems using novel AI tools to manage and mitigate interruptions from computing systems effectively.

We present a fully-printable method to embed interactive information inside 3D printed objects. The information is invisible to the human eye and can be read using thermal imaging after temperature transfer through interaction with the objects. Prior methods either modify the surface appearance, require customized devices or not commonly used materials, or embed components that are not fully 3D printable. Such limitations restrict the design space for 3D prints, or cannot be readily applied to the already deployed 3D printing setups. In this paper, we present an information embedding technique using low-cost off-the-shelf dual extruder FDM (Fused Deposition Modeling) 3D printers, common materials (e.g., generic PLA), and a mobile thermal device (e.g., a thermal smartphone), by leveraging the thermal properties of common 3D print materials. In addition, we show our method can also be generalized to conventional near-infrared imaging scenarios. We evaluate our technique against multiple design and fabrication parameters and propose a design guideline for different use cases. Finally, we demonstrate various everyday applications enabled by our method, such as interactive thermal displays, user-activated augmented reality, automating thermal triggered events, and hidden tokens for social activities.