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.

E-learning platforms that personalise content selection with AI are often criticised for lacking transparency and controllability. Researchers have therefore proposed solutions such as open learner models and letting learners select from ranked recommendations, which engage learners before or after the AI-supported selection process. However, little research has explored how learners - especially adolescents - could engage during such AI-supported decision-making. To address this open challenge, we iteratively designed and implemented a control mechanism that enables learners to steer the difficulty of AI-compiled exercise series before practice, while interactively analysing their control's impact in a 'what-if' visualisation. We evaluated our prototypes through four qualitative studies involving adolescents, teachers, EdTech professionals, and pedagogical experts, focusing on different types of visual explanations for recommendations. Our findings suggest that 'why' explanations do not always meet the explainability needs of young learners but can benefit teachers. Additionally, 'what-if' explanations were well-received for their potential to boost motivation. Overall, our work illustrates how combining learner control and visual explanations can be operationalised on e-learning platforms for adolescents. Future research can build upon our designs for 'why' and 'what-if' explanations and verify our preliminary findings.

Identifying explanation needs based on user characteristics has been the focus of human-centred research within XAI for some time.In Ribera et al. 's proposal of user-centred XAI, expertise was used as a proxy for characterising the user, and in turn guide explanation design.Since then, the research landscape has evolved to include a broader notion of stakeholders, ranging from AI developers to external regulators to the affected users of AI decisions.However, with this broadening of stakeholder roles, there emerged a pattern of conflating expertise and role, such as the term "end user" being used interchangeably for domain experts using (X)AI for decisionmaking and lay users impacted by AI decisions, with both having vastly different explanatory needs.In this work, we revisit previous surveys with the aim to identify and classify stakeholders in the XAI ecosystem.We propose to consistently categorise stakeholders along separate expertise and role dimensions.By disentangling both, we present a framework that highlights the diversity of stakeholder goals and the challenges of aligning explanation design with varied user requirements.Our analysis maps stakeholders onto these dimensions and discusses how using both expertise and role can inform the development of more tailored and effective XAI solutions.

sponsorship: We would like to thank ZAVO, and Joke Vandepitte in particular, for allowing us to collaborate. We would like to thank FWO by facilitating this interdisciplinary research. Additionally, we would like to thank all participants for their time and valuable insights. This research is part of the research projects funded by KU Leuven (grant C14/21/072) and the Flanders AI Research Program (FAIR). (KU Leuven|C14/21/072, Flanders AI Research Program (FAIR))

While 33.6% of college students suffer from mental health problems, only 24.6% of these students with symptoms would seek professional help due to their personal attitudes or costs associated with therapy. Psychotherapy chatbots may offer a solution as they are always available, anonymous, and cost-effective. Research has shown that these chatbots can significantly reduce symptoms of anxiety and depression. However, there is a lack of understanding about the personalization preferences of users and the effects of personalization on health outcomes. To investigate this, we developed a personalizable psychotherapy chatbot designed to provide personalized help. In a randomized controlled trial (n = 54), participants were either assigned to a personalizable condition or a non-personalizable control condition. After 1 week of usage, participants had a significantly higher therapeutic bond with the personalized version compared to the baseline. In fact, the therapeutic bond was similar to that between a psychologist and his client. This is a promising result, as a high therapeutic bond has been linked to therapeutic success in psychotherapy. Participants reported that the therapy style, personality, and avatar were the most important personalizable aspects of the chatbot. Participants also liked the chatbot’s usage of their name and the transparency about what the chatbot had learned about them. These features are likely important for establishing a strong therapeutic bond with users. However, the ability to personalize the chatbot had no impact on the usage intentions of the participants. This can be explained by the fact that users from both conditions equally reported that the chatbot was able to help them with their mental health. 53 participants also indicated that they would be willing to use a psychotherapy chatbot when integrated with a human therapist. These findings indicate the potential of psychotherapy chatbots and the need for further research on their integration with traditional psychotherapy.

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.

Artificial intelligence (AI) has seen a steady increase in use in the health and medical field, where it is used by lay users and health experts alike. However, these AI systems often lack transparency regarding the inputs and decision making process (often called black boxes), which in turn can be detrimental to the user’s satisfaction and trust towards these systems. Explainable AI (XAI) aims to overcome this problem by opening up certain aspects of the black box, and has proven to be a successful means of increasing trust, transparency and even system effectiveness. However, for certain groups (i.e. lay users in health), explanation methods and evaluation metrics still remain underexplored. In this paper, we will outline our research regarding designing and evaluating explanations for health recommendations for lay users and domain experts, as well as list a few takeaways we were already able to find in our initial studies.

As the use of AI algorithms keeps rising continuously, so does the need for their transparency and accountability. However, literature often adopts a one-size-fits-all approach for developing explanations when in practice, the type of explanations needed depends on the type of end-user. This research will look at user expertise as a variable to see how different levels of expertise influence the understanding of explanations. The first iteration consists of developing two common types of explanations (visual and textual explanations) that explain predictions made by a general class of predictive model learners. These explanations are then evaluated by users of different expertise backgrounds to compare the understanding and ease-of-use of each type of explanation with respect to the different expertise groups. Results show strong differences between experts and lay users when using visual and textual explanations, as well as lay users having a preference for visual explanations which they perform significantly worse with. To solve this problem, the second iteration of this research focuses on the shortcomings of the first two explanations and tries to minimize the difference in understanding between both expertise groups. This is done through the means of developing and testing a candidate solution in the form of hybrid explanations, which essentially combine both visual and textual explanations. This hybrid form of explanations shows a significant improvement in terms of correct understanding (for lay users in particular) when compared to visual explanations, whilst not compromising on ease-of-use at the same time.