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.@en

Reading foreign language texts is a frequently used strategy for language learning. Visual text augmentation methods further support the learning experience, e.g., by annotating vocabulary or grammar. Common approaches are integrated dictionaries or static grammar highlights. This work investigates how we can further support grammar learning with the dynamic visualization and interaction opportunities offered by digital reading devices. In collaboration with teachers and potential learners, we identify difficulties learners experience with English grammar and gather ideas for suitable interactive text augmentations. Based on this, we design four different concepts that augment adjectives and adverbs in English-language texts using typographic cues and interactive information displays. The concepts are evaluated in a within-subject study (N = 16). Results show that participants preferred concepts that presented case-specific support, did not distract too much from the text, and gave details on demand. We conclude with design recommendations for designing text augmentation for language learning.@en

The ubiquity of mobile devices in peoples’ everyday life makes them a feasible tool for language learning. Learning anytime and anywhere creates great flexibility but comes with the inherent risk of infrequent learning and learning in interruption-prone environments. No matter the length of the learning break, it can negatively affect knowledge consolidation and recall. This work presents the design and implementation of memory cues to support task resumption in mobile language learning applications and two evaluations to assess their impact on user experience. An initial laboratory experiment (N = 15) revealed that while the presentation of the cues had no significant effect on objective performance measures (task completion time and error rate), the users still perceived the cues as helpful and would appreciate them in a mobile learning app. A follow-up study (N = 16) investigated revised cue designs in a real-world field setting and found that users particularly appreciated our interactive test cue design. We discuss strengths and limitations of our concept and implications for the application of task resumption cues beyond the scope of mobile language learning.@en

The ubiquity of mobile devices in peoples’ everyday life makes them a feasible tool for language learning. Learning anytime and anywhere creates great flexibility but comes with the inherent risk of infrequent learning and learning in interruption-prone environments. No matter the length of the learning break, it can negatively affect knowledge consolidation and recall. This work presents the design and implementation of memory cues to support task resumption in mobile language learning applications and two evaluations to assess their impact on user experience. An initial laboratory experiment (N = 15) revealed that while the presentation of the cues had no significant effect on objective performance measures (task completion time and error rate), the users still perceived the cues as helpful and would appreciate them in a mobile learning app. A follow-up study (N = 16) investigated revised cue designs in a real-world field setting and found that users particularly appreciated our interactive test cue design. We discuss strengths and limitations of our concept and implications for the application of task resumption cues beyond the scope of mobile language learning.@en

Distractions and interruptions often disrupt mobile learners. Luckily, task resumption (memory) cues can support users in resuming a learning task. These cues can have multiple forms and designs, but their effectiveness depends heavily on their adaptation to the specific learning use case. This work explores the causes of interruptions during mobile learning and outlines designs for task resumption support. We report findings from two focus groups with HCI experts (N = 4) and users of mobile learning applications (N = 3). Finally, we discuss these findings by drawing on literature, and we derive a research agenda of currently unexplored concepts. We state limitations and open questions in the domain of task resumption support for mobile learning.@en

The technological advances of smartphones facilitate the transformation of learning from the classroom to an activity that can happen anywhere and anytime. While micro-learning fosters ubiquitous learning, this flexibility comes at the cost of having an uncontrolled learning environment. To this point, we know little about the usage of mobile learning applications, particularly the occurrence of interruptions and the harm they cause. By diverting users' attention away from the learning task, interruptions can potentially compromise learning performance. We present a four-week in-the-wild study (N = 12) where we investigate learning behavior and the occurrence of interruptions based on device logging and experience sampling questionnaires. We recorded 276 interruptions in 327 learning sessions and found that interruption type as well as users' context influence learning sessions and the severity of the interruption (i.e., session termination likeliness). We discuss challenges and opportunities for the design of automated mechanisms to detect and mitigate interruptions in mobile learning.@en