22q11.2 Deletion Syndrome (22q11 DS) is the most prevalent chromosomal microdeletion (∼ 1:4 500 live births), yet fewer than a third of parents feel equipped to discuss the condition with their child. Clinical encounters are brief and infrequent, suggesting that families need support that extends into daily life. We present GeneticPuzzle, a mobile psychoeducational application that digitises and extends a clinically validated physical puzzle tool used by over 50 families. Through two iterative studies, an expert evaluation and proof-of-concept with 8 families followed by a 6-month deployment with 43 families, we identified three design pillars: (1) interactive multimedia psychoeducation, (2) conversation scaffolding through a question logbook and puzzle-based recognition, and (3) longitudinal monitoring of evolving self-understanding. Our studies showcased the app’s ability to support family communication, with a supporting role in conversation scaffolding, and helping disease-related self-identification.

While substantial work has focused on explaining job–candidate matches in recruiting, a gap remains between these explanations and end-user goals, with visual explanations often being complex and overwhelming. Moreover, existing approaches are often static and provide limited interaction support, making it difficult for users to explore explanations in a way that matches their information needs. Guided by a human–centered design process, we present CAPTURE, a visual–conversational dashboard designed to support on-demand explanations in job–candidate matching. It integrates visual explanations with a conversational component powered by a multi-agent architecture that interprets user queries and orchestrates data retrieval and analysis workflows. By linking rich textual responses with relevant visualizations, CAPTURE enables flexible exploration and supports user-driven explanations in job–candidate matching algorithms.

Recommender systems are increasingly used in mobile health interventions, such as managing Chronic Musculoskeletal Pain (CMP). While researchers have highlighted the importance of explaining health-related recommendations to lay users, with benefits such as increased trust and a higher tendency to follow up on these recommendations, how to design explanations for lay users in critical contexts such as health remains largely unexplored. To address this gap, we develop a mobile health application to support users with CMP through coaching and personalised health recommendations delivered via a conversational rule-based recommender system. This article describes the three-phase iterative development of the RS, involving health experts and end users. In the first iteration, we conduct a preliminary validation study with \(N=282\) participants to ensure the app’s validity and improve the initial set of health recommendations. Next, two user studies are conducted centred around designing effective and understandable explanations for these recommendations. First, we design six explanation modalities tailored towards lay users, and through a qualitative study ( \(N=11\) ), extract initial design guidelines for explaining health recommendations, finding a strong preference towards feature importance explanations and identifying issues with modalities that highlight negative emotions. Given these results, we explore whether extending feature importance explanations with textual information into a ‘hybrid’ explanation could benefit end users, and whether these benefits depend on a user’s personal characteristics (need for cognition and ease-of-satisfaction). Through a mixed-methods study with \(N=262\) participants, we find that the hybrid modality significantly increased user trust, transparency, persuasiveness, usefulness and satisfaction compared to unimodal explanations. However, users with a higher need for cognition rate unimodal explanations more positively than hybrid ones.

Representation bias is one of the most common types of biases in artificial intelligence (AI) systems, causing AI models to perform poorly on underrepresented data segments. Although AI practitioners use various methods to reduce representation bias, their effectiveness is often constrained by insufficient domain knowledge in the debiasing process. To address this gap, this paper introduces a set of generic design guidelines for effectively involving domain experts in representation debiasing. We instantiated our proposed guidelines in a healthcare-focused application and evaluated them through a comprehensive mixed-methods user study with 35 healthcare experts. Our findings show that involving domain experts can reduce representation bias without compromising model accuracy. Based on our findings, we also offer recommendations for developers to build robust debiasing systems guided by our generic design guidelines, ensuring more effective inclusion of domain experts in the debiasing process.

Recent advancements in Educational AI have focused on models for automatic question generation. Yet, these advancements face challenges: (1) their "black-box" nature limits transparency, thereby obscuring the decision-making process; and (2) their novelty sometimes causes inaccuracies due to limited feedback systems. Explainable AI (XAI) aims to address the first limitation by clarifying model decisions, while Interactive Machine Learning (IML) emphasises user feedback and model refinement. However, both XAI and IML solutions primarily serve AI experts, often neglecting novices like teachers. Such oversights lead to issues like misaligned expectations and reduced trust. Following the user-centred design method, we collaborated with teachers and ed-tech experts to develop an AI-aided system for generating multiple-choice question distractors, which incorporates feedback, control, and visual explanations. Evaluating these through semi-structured interviews with 12 teachers, we found a strong preference for the feedback feature, enabling teacher-guided AI improvements. Control and explanations’ usefulness was largely dependent on model performance: they were valued when the model performed well. If the model did not perform well, teachers sought context over AI-centric explanations, suggesting a tilt towards data-centric explanations. Based on these results, we propose guidelines for creating tools that enable teachers to steer and interact with question-generating AI models.

Recommender systems are increasingly used in high-risk application domains, including healthcare. It has been shown that explanations are crucial in this context to support decision-making. This paper explores how to explain call recommendations to nursing home staff, providing insights into call priority, notifications, and resident information. We present the design and implementation of a recommender engine and a mobile application designed to support call recommendations and explain these recommendations that may contribute to residents’ safety and quality of care. More specifically, we report on the results of a user-centered design approach with residents (N=12) and healthcare professionals (N=4), and a final evaluation (N=12) after four months of deployment. The results show that our design approach provides a valuable tool for more accurate and efficient decision-making. The overall system encourages nursing home staff to provide feedback and annotate, resulting in more confidence in the system. We discuss usability issues, challenges, and reflections to be considered in future health recommender systems.

Learning analytics dashboards are at the core of the LAK vision to involve the human into the decision-making process. The key focus of these dashboards is to support better human sense-making and decision-making by visualising data about learners to a variety of stakeholders. Early research on learning analytics dashboards focused on the use of visualisation and prediction techniques and demonstrates the rich potential of dashboards in a variety of learning settings. Present research increasingly uses participatory design methods to tailor dashboards to the needs of stakeholders, employs multimodal data acquisition techniques, and starts to research theoretical underpinnings of dashboards. In this paper, we present these past and present research efforts as well as the results of the VISLA19 workshop on "Visual approaches to Learning Analytics" that was held at LAK19 with experts in the domain to identify and articulate common practices and challenges for the domain. Based on an analysis of the results, we present a research agenda to help shape the future of learning analytics dashboards.

The dynamics of the labor market and the tasks with which jobs are being composed are continuously evolving. Job mobility is not evident, and providing effective recommendations in this context has also been found to be particularly challenging. In this paper, we present Labor Market Explorer, an interactive dashboard that enables job seekers to explore the labor market in a personalized way based on their skills and competences. Through a user-centered design process involving job seekers and job mediators, we developed this dashboard to enable job seekers to explore job recommendations and their required competencies, as well as how these competencies map to their profile. Evaluation results indicate the dashboard empowers job seekers to explore, understand, and find relevant vacancies, mostly independent of their background and age.

This workshop paper discusses how we applied a user-centered, rapid-prototyping methodology to design and evaluate a Quantified Self dashboard for triathletes. Quantified Self barriers as discussed by Li and Forlizzi [1] and Choe et al. [2] are taken into account. A dashboard is designed for and evaluated with in total 25 triathletes and fourteen regular persons. Our results confirm that this methodology is successful and a well designed dashboard can be used to help users analyze their own data.