The use of digital learning environments has exploded during recent years, transforming learning at its core. At the same time, we know little about how this transformation affects younger students, still about to grasp basic concepts in STEM-subjects. This study explores the difference between interaction with physical or virtual representations when learning about areas of parallelograms. 94 middle-school students participated in the experiment, designed as an ordinary classroom activity. The students interacted with a physical deck of cards and a plastic frame – or with virtual representations of these objects – during two lessons. After that they took a test. The results reveal that even if there were no significant difference between the two conditions when it comes to applying the correct formula for areas of parallelograms, the students in the physical condition better grasped the concepts of height and base than the students in the virtual condition. This knowledge, in turn, correlated positively to the understanding of the area formula for parallelograms. These findings indicate that specific actions and/or interactions, facilitated by a physical material and the use of pen and paper, may be truly beneficial for acquiring knowledge in geometry and spatial reasoning.
The integration of technological mediums in architectural pedagogy has transitioned from a supplementary aid to a foundational necessity in response to the increasing complexity of design practices. This study conducts a systematic literature review to examine how these mediums interact with architectural pedagogy to enhance educational effectiveness. Analyzing a corpus of 175 peer-reviewed studies, the research identifies critical success factors and synthesizes them into an original conceptual model. The findings reveal that technological value in architectural education is generated through five interrelated dimensions: Configuration, Visualization, Construction Management, Knowledge Management, and Integrated Analysis. While immersive visualization remains the most dominant dimension, its impact is frequently limited to perceptual engagement rather than deep cognitive reflection. The study further identifies that successful integration is heavily moderated by institutional infrastructure, instructor expertise, and pedagogical scaffolding. By bridging fragmented empirical evidence into an integrative framework, this research shifts the discourse from technology-centered adoption toward a value-driven pedagogical strategy. These insights offer a transferable roadmap for curriculum designers and policy-makers to align technological affordances with high-order architectural learning outcomes, making a significant contribution to the current discourse.
In this conceptual paper, we synthesize findings from four studies at the intersection of Participatory Design (PD) and generative AI to articulate a coupled path: PD is suited for evaluating AI through lived experience; insights inform AI-supported co-creation (e.g., enacted/synthetic personas); and PD, in turn, designs and validates these tools. We show that AI-generated and -enacted personas can widen perspective but are not recommended as substitutes for people. Based on the cross-cutting lessons learned from the four studies we outline strategies and care practices for responsible use of synthetic personas, introducing a layered bidirectional model illustrating how PD values should form use of these instruments while knowledge acquired from use can in turn inform reconfigurations of PD practices and values. This strategy positions PD as both shaper and steward of generative AI in design.
This research explores the influence of using Artificial Intelligence tools and resources on User Experience practices aimed at discovering and understanding the needs of digital system users. The focus was on creativity, an essential skill, and productivity, a key factor in the job market. The research question revolved around how these professionals can ensure the presence of creativity in their projects when incorporating Artificial Intelligence tools into their workflow, and how this integration impacts their productivity. The aim was to capture a snapshot of the present moment, characterised by constant innovations, by exploring and analysing current uses of Artificial Intelligence. To address the proposed question, a survey, interviews, and a comparative experiment were conducted. The findings in the study demonstrate that Artificial Intelligence has primarily fulfilled an assistive function. Its implementation can boost productivity in operational tasks, allowing professionals to devote more time to higher-value activities. It can be useful for individuals working under high demand or in environments with low User Experience maturity, when facing tight deadlines, small teams, or creative blocks. Artificial Intelligence can foster creativity for some individuals, while for others, it may reduce immersion and lead to less profound or original insights. There is scepticism about adopting it in activities that require creative cognitive effort, immersion, complex decision-making, and empathy.
Our first-semester HCI course for CS bachelor students at TU Wien discusses different perspectives on understanding and solving problems in CS. Many students do not see the relevance of these topics in regard to their studies in contrast to the perceived “hard skill” courses such as programming or mathematics. Designing game-based learning methods, we aspire to spawn interest and motivation in our students to engage with these topics that are central to technology design and ideally informed by HCI paradigms. Tapping into our master-level course on gameful design, we tasked students to create games for the first-year HCI course. We conclude that our approach was successful, with the first-year students appreciating the games. In this paper, we describe this design/learning pipeline and our results, concentrating on opportunities to enhance this process, theorise on how to shift more focus on the learning in our game-based learning approach, and reflect on what worked well and possible obstacles in this type of design research.
Social innovation is a collaborative process that addresses complex problems through recombining resources, constructing networks, and adopting design tools. In this context, generative and conversational AI supports data analysis, knowledge synthesis and the production of operational toolkits. At the same time, the metaverse enables immersive, inclusive co-design spaces that overcome geographical and temporal barriers. The article analyses the potential of integrating artificial intelligence, the metaverse and co-design practices in social innovation. It discusses empirical experiences conducted in Milano and Palermo, developed in the OSMOSI action research project. It highlights opportunities and critical issues in the use of emerging technologies in urban regeneration and cultural innovation processes. It proposes an application protocol. This replicable protocol integrates digital tools and participatory methodologies, with implications for researchers, policy makers, and local actors.
Experience-Based Co-Design (EBCD) is a participatory approach commonly used to improve healthcare services. However, its application in guiding the design of technologies, particularly artificial intelligence (AI) systems, remains limited. This study adapts EBCD to support nurse-centered design of AI-enhanced electronic health records (EHRs) for patient handover. Drawing on interviews and co-design activities with nurses, AI experts, and UX designers in Australia, we identified workflow misalignments and co-developed AI features that address clinical challenges. Based on these findings, we propose a tailored EBCD approach for AI design, comprising five guiding elements: Scenario Co-Construction, Experience-Based Facilitation, Technical Feasibility Alignment, Design Integration, and Common Adjustment. This approach is aimed at promoting equitable participation, grounding design in real-world experience, and ensuring AI tools align with clinical workflows. Our work contributes a technology design pathway that builds on an established method and centers frontline clinicians as equal partners in shaping AI-integrated healthcare systems.
In recent years significant contributions have been made to expand the research area of data physicalization. The present study presents the results of a systematic literature review and an annotated portfolio analysis that frame the current state of the field, with particular attention to its implications for interaction design discipline. It outlines key outputs, methods, and domains that support tangible engagement with data, while also identifying critical challenges such as data mapping, interaction temporality, and contextual application. The study concludes by drafting promising research trajectories and by challenging dominant paradigms of data encoding, highlighting opportunities for designing more situated, embodied, and meaningful artefacts encompassing data physicalization.
Artificial Intelligence (AI) is increasingly embedded in civic technologies, including e-participation tools and platforms designed to support democratic deliberation. Yet, while a growing “participatory turn” in AI design (Delgado et al., 2023) promotes stakeholder engagement, participation is often enacted as a discrete format or early-stage aspiration rather than as a sustained process embedded across the lifecycle of AI systems. This paper examines how Participatory Design (PD) operates as a socio-technical mediation mechanism in the development of AI-enhanced e-participation tools and platforms. Drawing on the Horizon Europe project ORBIS and its six deliberative pilots, the study builds on Delgado et al.’s (2023) four analytical dimensions—goals, scope, participants, and methods—to elaborate an intermediate theoretical account of how PD structures negotiation between differentiated deliberative practices and AI configurations over time. The findings show that AI-enhanced deliberation becomes democratically meaningful not through technological optimisation alone, but through sustained mediation that aligns civic practices with evolving technological affordances under institutional constraints and deliberative needs. The paper contributes a structured account of PD as a longitudinal socio-technical mediation process for developing AI-enhanced civic technologies beyond isolated engagement interventions.
Community consultations in urban planning often exclude participants due to language barriers, limited feedback, and technical complexity. This study explores how generative artificial intelligence (AI) can enhance inclusivity in participatory design. The study reports an action research case in Samarinda, Indonesia, where the AI tool Midjourney was integrated into three community workshops involving residents, civil-society groups, students, and government officials. Activities combined analogue exercises with structured prompts-crafting and live image generation. The dataset included sketches, prompt fragments, and 133 AI images analysed thematically. Findings suggest that the protocol enabled broader participation across age groups, expanded the range of expressed ideas, and increased in-session legibility and responsiveness of design discussions. Midjourney contributed by accelerating visual iteration and providing shared boundary objects, while the sequenced activities and facilitation structured the feedback loop and made participant influence visible in evolving artefacts. Participants articulated priorities such as cultural identity, local materials, and flood adaptation. Despite challenges related to facilitation, translation, and potential model bias, the study indicates that generative AI can function as an “imagination engine” and boundary object within inclusive, climate-adaptive urban design processes.
Micro, Small and Medium sized enterprises (MSMEs) face considerable challenges in adopting Responsible Artificial Intelligence (RAI), including limited technical capacity, a lack of tailored tools, and insufficient access to training and certification. This study investigates the landscape of RAI assessment tools and evaluates their applicability to MSMEs. Through co-design workshops with Estonian MSMEs and analysis of 18 publicly available RAI assessment tools, the research identifies key gaps in the current assessment tools using the lens of the participating MSMEs, particularly in learning support and consideration of environmental impact. The findings highlight that few tools address the specific needs of MSMEs, including cross-sector cooperation and academic partnerships. This research advances perspectives on co-designing future RAI by demonstrating that the dominant paradigm of RAI governance remains structurally compliance-centric instead of allowing MSMEs to have a say. Limitations include geographic concentration and qualitative scope, suggesting the need for more cross-regional and empirical studies. The study calls for a shift from tool centric compliance models to dynamic, ecosystem-based strategies for sustainable adoption of RAI.
The workshop aims to explore relations between AI and PD: how AI can be used in PD as well as how PD can influence AI systems. In the workshop participants share experiences with AI in PD, presenting materials for discussion. The outcome of the workshop will be a set of principles and guidelines and a (common) paper, and we aim to establish a special interest research community for creative use of AI in PD.
The gamification analytics field is a part of the larger field of learning analytics. While learning analytics is a mature domain, with a lot of research in the last decades, the subfield of gamification analytics has been less investigated so far. Moreover, to the best of our knowledge, there are no studies that research the domain of gamification analytics applied in the context of peer assessment. Therefore, the current paper proposes a gamification analytics module dedicated for visualizing data collected from a gamified peer assessment platform. The module focuses on three distinct analytics dimensions: individual learner level, whole class level, and temporal level. Various graphic elements are featured, such as histograms, box plots, spline charts, pie charts, bar charts, and column charts, that facilitate the task of analyzing and visualizing the data.
Many eye-tracker studies exclude individuals with special needs to avoid results’ distortion, while few address its application design. The aim in this study was to identify key challenges in developing eye-tracker applications and explore techniques to address them. An integrative literature review was conducted across Scopus and Web of Science, analysing 26 records and evaluating 10 Tobii Dynavox games as grey literature. Results reveal device limitations, such as inaccuracy, Midas Touch, and eye fatigue, that must be considered in interface design. Key recommendations for implementation include: (1) Snap Clutch and MAGIC frameworks; (2) dwell times; (3) designated rest areas; (4) arrow-flanked and Messenger text-visualization; and (5) three game design paradigms. In conclusion, these methods positively contribute to eye-tracker game design and demonstrate how applications can adapt to encountered obstacles. Future works should explore the effectiveness of combining these solutions and expand the evaluation of eye-tracker games as grey literature.
Advancements in digital technologies based on Artificial Intelligence (AI) have made music a constant presence in teenagers’ lives, providing them with a means of creative expression and personality shaping. Among the most significant musical activities for promoting teenagers’ well-being is the ability to actively create and modify musical tracks, particularly in the field of electroacoustic music, which integrates both acoustic and electronic sounds. However, current AI-based digital technologies for electroacoustic music creation often lack dedicated learning paths, and they are targeted only at experts, limiting their accessibility. The ARTECOM project was conceived to address these issues, aiming to develop AI-based digital installations in strategic urban locations to encourage even non-expert teenagers, especially those facing economic and social barriers to art access, to engage in electroacoustic music creation. This paper presents the initial steps of the project, including a preliminary study on profiling the target participants and their context of use.
Training employees in organizations is essential for enhancing productivity and profitability, updating their knowledge, and better preparing them for market demands. Through digital platforms (LMS) and the e-Learning method, training occurs in web-based environments, enabling content management and accessibility across multiple devices. e-Learning typically follows a modular structure, ensuring adaptability, flexibility, and asynchronous learning. This study applies to the Design Science Research method to implement a data protection training course via an LMS, facilitating knowledge dissemination and employee self-assessment. The organization faces challenges in rapidly spreading knowledge due to its widespread locations, diverse working hours, and geographical constraints. The study evaluates training dissemination through microlearning, leveraging Moodle (LMS) and Digital Storytelling techniques. Additionally, it assesses the pedagogical and engagement aspects to ensure training is efficient, standardized, flexible, and more appealing to employees, increasing their receptivity and interest.
This study presents findings on university students’ perceptions and usage habits regarding generative artificial intelligence (GenAI), based on questionnaire responses from students of various academic levels and disciplines across multiple countries (Romania, Iraq, Italy, Argentina, and the Philippines). The results indicate a near-universal adoption of GenAI, with usage largely driven by personal initiative. Most students acquire their knowledge of GenAI applications and form their perceptions independently, relying primarily on online resources rather than expert instruction. While awareness and understanding of GenAI tend to increase with academic progression, they are also influenced by broader cultural contexts; in some cases, gender-related differences are observed. Higher levels of awareness are associated with increased concern about the challenges posed by GenAI, as well as a deeper appreciation of its potential benefits. Students primarily use GenAI to obtain immediate, practical educational advantages. However, there is relatively limited engagement with the broader societal implications—positive or negative—of its use. The primary concern among participants is the impact of GenAI on future employment opportunities. A causal network analysis of the questionnaire data identifies two key outcomes at the end of the causal chain: the level of trust in GenAI-generated results and the degree of personalization in learning processes. Use of GenAI outside academic contexts remains limited, suggesting the emergence of a gap in AI literacy and application in everyday life. To address this issue, it is essential to implement training programs for educators, ensuring that students’ acquisition of AI literacy is supported by expert guidance—ideally beginning as early as primary or secondary education.
This paper details the strategic modifications implemented in the latest phase of the Capacid@de Digital initiative. Building upon an analysis of earlier actions—presented in a previous publication—this article focuses on the iterative changes designed to enhance the initiative’s impact on digital inclusion among seniors. The initiative harnesses the skills and enthusiasm of higher education institution student volunteers, who deliver tailored digital training to an elderly population. We describe the process of refining the training methodologies to better meet the needs of seniors and volunteers. This study contributes to the growing body of literature on volunteer-led digital training and offers a replicable model for similar community-based programs aimed at reducing the digital divide.
An Intelligent Tutoring System (ITS) is a computer-based system that produces personalized tutoring through individualized, pedagogically sound, and easy-to-access educational material. Research groups explored various methods and assumptions for building efficient tutors in the cognitive field, with notable results in disciplines like physics, mathematics, and informatics. In contrast, the psychomotor domain is only lately exhibiting an intensive digitalization process. Selfit v2 is a recently-developed ITS that aims to engage people in sports and improve the general health of the mass population. In this study, we assess Selfit v2's utility and effectiveness in an experiment with forty-two users having low and medium training experience. The experiment used two adaptive strategies for tutoring - narrow and broad exploration spaces. Selfit v2 evaluation showed promising results and highlighted the usefulness of ITS in the psychomotor field. The current work can be considered the foundation of a new crossroad between AI in education and psychomotor training, opening new research directions aiming to improve the population's general health through automated systems.
The volume of published articles has grown exponentially, making research documentation considerably harder. To address this, we have developed and present in this paper a toolkit that can effectively process fulltext manuscripts starting from their PDFs, extract topics, and provide correlations between manuscripts based on their common topics. The effectiveness of the toolkit is demonstrated by applying it to the "Interaction Design and Architecture(s)" (IxD&A) journal's full-text articles published between 2013 and 2024 (N = 450). Topic modeling was performed using BERTopic and Llama as LLM to generate coherent topics. As a result, we extracted 246 topics from the entire corpus, which were automatically filtered for specificity using Llama-3.1. In-depth visualizations and analyses with a focus on a human-centered, smart learning perspective, are presented. We release our toolkit as open-source on GitHub to enable users to easily apply our method in other relevant contexts.
