Children in upper primary education often struggle to understand complex and invisible human biology concepts (e.g., circulation, digestion), which are difficult to grasp at this developmental stage. While storytelling and analogies can support learning, their classroom use is often constrained by limited time, resources, and teacher expertise. This paper presents BioStories, an AI-supported co-creative storytelling system that integrates interactive activities to support children aged 9–11 in biology learning. The system enables children to engage in multimodal interactions, including creating personalised “organ buddy” characters through drawing and collaboratively constructing stories with an AI partner using guided prompts and visual cues. A mixed-method, user-centred design process informed the system, including a pre-design workshop, iterative prototyping, and evaluation with children and experts. Findings indicate that combining interactive drawing, visual scaffolding, and conversational co-creation enhances engagement, enjoyment, and perceived understanding of biology concepts. This work contributes design insights into AI-supported storytelling for playful and meaningful science learning.

Digital storytelling has been widely explored in mathematics education to situate abstract problems within meaningful narrative contexts. However, most existing systems present stories through static media such as text and images, offering limited opportunities for active engagement. In this paper, we present MathsTale, a storytelling web application we developed for teaching children abstract mathematical problems through interactive multimodal stories. The system adopts a multi-agent architecture and leverages generative artificial intelligence to produce narrative text, images, and executable code for interactive hands-on activities. To explore its potential, we conducted a preliminary user study involving children and teachers using semi-structured interviews. Findings suggest that interactive multimodal stories can enhance children’s understanding, enjoyment, and engagement in learning mathematical problems.

Students in Higher Education (HE) with Attention Deficit Hyperactivity Disorder (ADHD) often face persistent challenges in video-based e-learning, including difficulties with sustained attention, comprehension, and information retention. Existing lecture platforms typically offer limited embedded support, while external tools for focus and note-taking operate outside the learning context and rely heavily on self-regulation. This paper presents the design, development, and evaluation of a digital learning tool that integrates support features directly into the lecture interface to assist students with ADHD. Developed using a user-centered design methodology, the prototype includes chapter-based video segmentation, AI-generated summaries and quizzes, a context-aware chatbot, and visual scaffolds such as mind maps and annotated slides. The design was informed by the input of students with ADHD and experts in Education, Accessibility, and Human-Computer Interaction (HCI). A high-fidelity prototype has been developed and evaluated to assess usability, relevance, and its potential to support focus, comprehension, and retention. The findings suggest that embedding accessible adaptive features in lecture content can reduce cognitive load and improve engagement for students with ADHD. The paper concludes with implications for inclusive e-learning design and directions for future research.

The present study aims to investigate the relationship between emotions experienced during learning and metacognition in typically developing (TD) children and those with autism spectrum disorder (ASD). This will assist us in using machine learning (ML) to develop a facial emotion recognition (FER) based intelligent tutor system (ITS) to support children’s metacognitive monitoring process in order to enhance their learning outcomes. In this paper, we first report the results of our preliminary research, which utilized an ML-based FER algorithm to detect four spontaneous epistemic emotions (i.e., neutral, confused, frustrated, and boredom) and six spontaneous basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise). Subsequently, we adapted an application (‘BrainHood’) to create the ‘Meta-BrainHood’, that embedded our proposed ML-based FER algorithm to examine the relationship between facial emotion expressions and metacognitive monitoring performance in TD children and those with ASD. Finally, we outline the future steps in our research, which adopts the outcomes of the first two steps to construct an ITS to improve children’s metacognitive monitoring performance and learning outcomes.

In this workshop, we invite researchers, practitioners and designers to reflect on ethical issues arising from Distributed Participatory Design (DPD) research with children. As participatory design research practices require rethinking and innovative adaptation in light of the COVID-19 pandemic, distributed, asynchronous and online (D)PD approaches may provide solutions to participation barriers. However, in light of this adaptation, additional ethical complexities may arise. Ongoing collaborative discussion is required to identify and address the different types of ethical issues which may arise when planning and conducting DPD projects with children. This workshop builds on previous workshops held at IDC 2021 and 2020, which provided insights into developing a protocol for a world-wide DPD project with children.

In this paper, we outline a Distributed Participatory Design (DPD) research protocol. The protocol is presented through the lens of the World’s Largest DPD Project with children, a hypothetical project in which children address the issue of climate change. The DPD research protocol is based on the protocol template recommended by WHO’s Research Ethics Review Committee (ERC) and on the data from two conference workshops with a total of 45 participants. The contribution of this paper is two-fold: 1) an initial version of a generic DPD research protocol; 2) an exemplification of how to appropriate this protocol for a specific project. We expect this protocol will be iteratively refined and will serve as inspiration for future research practices in Child-Computer Interaction (CCI).

In this extended abstract, we outline a PhD project which investigates the relationship between emotion and metacognition in children with Autism Spectrum Disorder (ASD) in order to design and develop an automatic Machine Learning based tool with real-time feedback to support metacognitive process of both Typically Developing (TD) children and children with ASD.

In this workshop, we invite researchers and practitioners as participants in co-designing the protocol for the world’s largest Distributed Participatory Design (DPD) project with children. Participatory Design – whose inclusive benefits are broadly recognised in design – can be very challenging, especially when involving children. The current COVID-19 pandemic has given rise to further barriers to PD with such groups. Recent key barriers include social distancing and government-imposed social restrictions due to the additional health risks to vulnerable children and their families. This disrupts traditional in-person PD (which involves close socio-emotional and often physical collaboration between participants and researchers). However, alongside such barriers, we have identified opportunities for new and augmented approaches to PD across distributed geographies, backgrounds, ages and abilities. We invite the CCI community to examine Distributed Participatory Design (DPD) as a solution for overcoming these new barriers, during and after COVID-19. Together, we offer new ways to think about DPD, and unpick some of its ambiguities. This workshop builds on work conducted in a similar workshop in IDC 2020, and this year will focus on the planning and design of the protocol for the world’s largest DPD project with children.

Despite its inherent challenges, participatory design (PD) has unique benefits when designing technology for children, especially children with special needs. Researchers have developed a multitude of PD approaches to accommodate specific populations. However, a lack of understanding of the appropriateness of existing approaches across contexts presents a challenge for PD researchers. This workshop will provide an opportunity for PD researchers to exchange and reflect on their experiences of designing with children with special needs. We aim to identify, synthesize and collate PD best practices across contexts and participant groups.

Our research explores the development of a novel technology-based prototype to support children and designers during brainstorming, one of the most challenging activities within Participatory Design (PD). This paper describes a proof-of-concept prototype for a tool that aims to empower children with Autism Spectrum Disorders (ASD) during PD to maximise their contributions to the design and their own benefits. Preliminary results revealed that the prototype has the potential for reducing anxiety in children with ASD, and supports unlocking their creativity.