Background
Interventions targeting social media use show mixed results in improving well-being outcomes, particularly for persons with problematic forms of smartphone use. This study assesses the effectiveness of an intervention app in enhancing well-being outcomes and the moderating role of persons' perceptions about problematic smartphone use (PSU).

Methods
In a randomized controlled trial, N = 70 participants, allocated to the intervention (n = 35) or control condition (n = 35), completed weekly online surveys at baseline, post-intervention, and follow-up. Participants from the intervention condition received personalized full-screen nudges to reduce their social media app use. This secondary analysis focuses on the repeatedly assessed outcomes well-being, positive affect, negative affect, and perceived stress. Linear mixed models were computed.

Results
No significant time x group effects were found, but intervention condition participants reported higher well-being and lower negative affect and stress levels at follow-up. Only one significant moderation was found, indicating that participants reporting higher PSU levels benefited more from the intervention in reducing stress.

Conclusions
The intervention was partly effective and particularly beneficial in reducing stress among smartphone users with higher PSU, highlighting the need to tailor interventions. Present findings need to be replicated by future research using a larger sample size.@en

Researchers have proposed various approaches to increase motivation for learning. However, in many cases, they are designed to boost motivation but fall short of creating learning habits. Gamification and competition between peers are widely used to urge people to, for example, do more steps in a day. E-learning platforms often use leaderboards to visualize peer progress, but their effectiveness is limited to when users open the platform. In this work, we use notifications to inform peers of each other's progress in real-time to increase motivation for learning. In an in-the-wild study with 19 participants, we investigate the impact of peer-awareness on learning by comparing real-time notifications (sent immediately after peers' progress) with interval-based notifications (sent at random intervals). Contrary to our initial expectations, our findings reveal that interval-based notifications are more effective in promoting learning activities. We introduce the Peer-Aware Learning System (PALS), a mobile application designed to simulate peer effects and provide insights from our user study on notification timing strategies for enhancing learning motivation.@en

Object selection and manipulation are the foundation of VR interactions. With the rapid development of VR technology and the field of virtual object selection and manipulation, the literature demands a structured understanding of the core research challenges and a critical reflection of the current practices. To provide such understanding and reflections, we systematically reviewed 106 papers. We identified classic and emerging topics, categorized existing solutions, and evaluated how success was measured in these publications. Based on our analysis, we discuss future research directions and propose a framework for developing and determining appropriate solutions for different application scenarios.

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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.

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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.

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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.

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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.

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