The amount of information we consume daily through smartphones has prompted the development of Personal Knowledge Management Systems (PKMS). However, these external memory aids can lead to overreliance and do not help to keep information in our organic memory. This paper presents the design and evaluation of MemoryMate, an app that fosters recall of saved content through interactive notifications. The app's design is based on insights from a focus group (N = 7) that inquired about people's expectations and preferences regarding the notification design. The results of a three-week in-the-wild study (N = 23) of MemoryMate suggest that notifications could trigger engagement with saved content, and two-thirds of participants felt notifications improved their memory and retention. Retrieval practice notifications with interactive tasks performed well and received positive feedback. Content priority and time since the last interaction influenced notification engagement, emphasising the need for periodic reminders to maintain user interest in saved content. We discuss potential implications for the design of PKMSs as a tool for lifelong learning.

Television has long since been a uni-directional medium. However, when TV is used for educational purposes, like in edutainment shows, interactivity could enhance the learning benefit for the viewer. In recent years, AR has been increasingly explored in HCI research to enable interaction among viewers as well as viewers and hosts. Yet, how to implement this collaborative AR (CoAR) experience remains an open research question. This paper explores four approaches to asynchronous collaboration based on the Cognitive Apprenticeship Model: scaffolding, coaching, modeling, and collaborating. We developed a pilot show for a fictional edutainment series and evaluated the concept with two TV experts. In a wizard-of-oz study, we test our AR prototype with eight users and evaluate the perception of the four collaboration styles. The AR-enhanced edutainment concept was well-received by the participants, and the coaching collaboration style was perceived as favorable and could possibly be combined with the modeling style.

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.

Common sources of career information like websites often provide a static overall picture of a job, yet lack personal insights into the daily working life. To address this problem, we present a novel mobile career guidance method: It enables users to remotely gain an impression of different work routines by receiving several short, scheduled chat messages from a persona throughout the day. These messages were previously collected from real professionals reporting on their tasks over a week. We implemented a smartphone application to compare our message-based approach to a traditional blog entry in a two-week within-subject field study (N = 17). Users highlighted that the scheduled messages (1) enhanced their understanding of work routines by integrating career information into their own daily context and (2) offered authentic insights into the jobs. We discuss design implications for mobile career guidance systems and future opportunities for presenting chunks of information in a temporal context.

The pervasive availability of media in foreign languages is a rich resource for language learning. However, learners are forced to interrupt media consumption whenever comprehension problems occur. We present BrainCoDe, a method to implicitly detect vocabulary gaps through the evaluation of event-related potentials (ERPs). In a user study (N=16), we evaluate BrainCoDe by investigating differences in ERP amplitudes during listening and reading of known words compared to unknown words. We found significant deviations in N400 amplitudes during reading and in N100 amplitudes during listening when encountering unknown words. To evaluate the feasibility of ERPs for real-time applications, we trained a classifier that detects vocabulary gaps with an accuracy of 87.13% for reading and 82.64% for listening, identifying eight out of ten words correctly as known or unknown. We show the potential of BrainCoDe to support media learning through instant translations or by generating personalized learning content.

This reflection paper takes the 25th IUI conference milestone as an opportunity to analyse in detail the understanding of intelligence in the community: Despite the focus on intelligent UIs, it has remained elusive what exactly renders an interactive system or user interface "intelligent", also in the fields of HCI and AI at large. We follow a bottom-up approach to analyse the emergent meaning of intelligence in the IUI community: In particular, we apply text analysis to extract all occurrences of "intelligent" in all IUI proceedings. We manually review these with regard to three main questions: 1) What is deemed intelligent? 2) How (else) is it characterised? and 3) What capabilities are attributed to an intelligent entity? We discuss the community's emerging implicit perspective on characteristics of intelligence in intelligent user interfaces and conclude with ideas for stating one's own understanding of intelligence more explicitly.

Due to the rising popularity of streaming services, television networks are experiencing pressure to keep the attention of the younger audience. Especially in the field of Edutainment, platforms like YouTube or TED are serious competitors and require broadcasters to come up with novel ideas to engage viewers in their program. In this work, we present the augmented reality (AR) SpaceStation application, designed to supplement the viewing of educational videos about the ISS. We evaluated users’ experience during the interaction with the app in a within-subject user study (N = 31) and assessed their workload. During the interaction with the SpaceStation App, participants experienced a higher workload compared to a video-only condition; nonetheless, they considered AR a valuable and enjoyable addition. This paper concludes with a discussion from the perspectives of viewers, content creators, and hosts, and states initial ideas on how to design television programs with AR content, without creating information overload.

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.

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.

Smartphones enable people to learn new languages whenever and wherever they want. This popularized mobile language learning apps (MLLAs) and in particular micro learning that offers simple and short learning units to keep the user on track. Due to the ubiquitous use of these applications, they have to adapt to the users' current situation to provide an optimal learning experience. To gain insights into how users perceive common usage scenarios, we conducted an online survey (N=74) and clustered all described learning scenarios into five categories of usage situations. We outlined internal and contextual factors which are characteristic for these situations and discussed those in a follow-up focus group with HCI experts (N=4). During this focus group, we collected four design recommendations to adapt MLLAs to situations of users' (a) high attention levels, (b) tiredness or exhaustion, (c) highly demanding environments, or (d) low motivation.

The recently finished EU project JuxtaLearn aimed at supporting students’ learning of STEM subjects through the creation, exchange and discussion of learner-made videos. The approach is based on an eight-stage activity cycle in the beginning of which teachers identify specific “stumbling blocks” for a given theme (or “tricky topic”). In JuxtaLearn, video comments were analyzed to extract information on the learners’ acquisition and understanding of domain concepts, especially to detect problems and misconceptions. These analyses were based on mapping texts to networks of concepts (“network-text analysis”) as a basis for further processing. In this article we use data collected from recent field trials to shed light on what is actually discussed when students share their own videos in science domains. Would the aspect of video-making dominate over activities related to a deepening of domain understanding? Our findings indicate that there are different ways of balancing both aspects and interventions will be needed to bring forth the desired blend.

Aiming at facilitating STEM learning through video-making, the JuxtaLearn project supports teachers in defining their own domain taxonomies as semantic building blocks for various learning activities. This paper reports on an evaluation with 17 teachers using a semantic recommender system for taxonomy building and lesson planning. Our results indicate that the system is perceived as supportive, but it also reveals problems caused by teachers’ individual preferences.