Human autonomy is a core concept that helps explain the acceptance of and interaction with computer systems and AI technology. However, autonomy is often vaguely defined and conflated with related constructs. This paper disentangles autonomy by integrating the dualistic nature of positive and negative liberty from the perspective of political philosophy. Using an online vignette study with N=194 participants, we show that positive and negative liberty act as correlated but distinct dimensions of the autonomy foundation. While negative liberty predicts the sense of agency, positive liberty is a key dimension for people’s willingness to use technology. We argue that this dualistic stand - positive liberty as the freedom to pursue authentic goals, and negative liberty as the freedom from external constraints - offers a valuable and actionable perspective on human autonomy that can inform future system design and better answer the ambivalent question “how much autonomy is enough”?

In high-stakes decision-making, the acceptance of AI recommendations depends not only on system accuracy but also on how decisions are explained. While prior work on explainable AI has largely focused on transparency and interpretability, less attention has been paid to the persuasive dimension of explanations. To address this gap, we investigate how rhetorical strategies drawn from Cialdini’s persuasion theory, when embedded in natural language explanations generated by large language models (LLMs), influence user compliance and their ability to recognize persuasive intent. We conducted a controlled survey study with 129 participants in two application domains—finance and healthcare—where participants evaluated both a baseline and a persuasive explanation for an AI-generated decision across favorable and unfavorable outcomes. In a complementary task, participants rated ten short explanations on perceived persuasiveness and factual strength, enabling us to measure awareness of persuasive intent. Our results show that persuasive explanations significantly increased compliance in the healthcare scenario (p <.001), whereas baseline explanations were more effective in finance (p <.001), regardless of whether the AI decision was positive or negative. A notable proportion of participants rated explanations containing at least one of Cialdini’s persuasion techniques as highly persuasive, yet simultaneously judged them to be factually weaker. Importantly, we found no statistically significant evidence that participants’ ability to recognize persuasive intent influenced compliance. These findings highlight the dual role of persuasive explanations: they can enhance foster compliance in sensitive contexts such as healthcare but risk undermining trust in domains like finance. For HCI and IUI, our study underscores that explanations are not neutral vessels of information: their rhetorical form substantially shapes how users perceive and engage with AI-assisted decision-making. Designers of explainable AI systems should therefore carefully balance transparency and persuasion when developing interfaces for high-stakes applications.

Attention management systems aim to mitigate the negative effects of multitasking. However, sophisticated real-time attention management is yet to be developed. We present a novel concept for attention management with reinforcement learning that automatically switches tasks. The system was trained with a user model based on principles of computational rationality. Due to this user model, the system derives a policy that schedules task switches by considering human constraints such as visual limitations and reaction times. We evaluated its capabilities in a challenging dual-task balancing game. Our results confirm our main hypothesis that an attention management system based on reinforcement learning can significantly improve human performance, compared to humans’ self-determined interruption strategy. The system raised the frequency and difficulty of task switches compared to the users while still yielding a lower subjective workload. We conclude by arguing that the concept can be applied to a great variety of multitasking settings.

Studies across various task types suggest that collaboration between humans and AI leads to improved and more satisfying outcomes. However, the effects of prolonged use of AI on users’ skills and perceptions remain unclear. This study involved 12 participants using an AI-based assistant in a multitasking balancing game over five days. Our findings indicate that AI assistance improved participants’ performance, even for the condition that was not supported by AI, showing no deskilling effect. Additionally, participants experienced significantly lower cognitive load (measured via ocular pupil diameter) in the AI-supported condition. This suggests that AI-assisted training can enhance multitasking motor skills in a less stressful and cognitively demanding manner. We also found a positive correlation between users’ understanding of the AI assistant and its acceptance, highlighting the importance of transparency and effectively communicating the capabilities of AI applications.

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.

Notifications and interruptions have shown to significantly impede task performance while causing stress. Attention management systems aim at mitigating these negative effects, for example, by delaying interruptions to task boundaries or times of low mental load. However, while the theoretical benefits of such an approach are well-documented, it is quite unclear how holding back information from users is accepted, especially in times of the “always-on-mentality”. Thus, we conducted an online vignette experiment with N=163 participants, who were presented hypothetical private and work-related scenarios where interruptions are delayed by attention management systems. Participants rated how long they would allow particular interruptions to be delayed, as well as which data collection methods a system could use to perform these decisions. Our results show that interruption management is desired by potential users, provided they feel in control. We conclude with recommendations for the design of attention management systems.