In conjunction with the 30th International ACM Conference on Intelligent User Interfaces (ACM IUI 2025)
Cagliari, Italy
March 24-27, 2025 https://iui.acm.org/2025/
IUI 2025 is pleased to announce the following seven workshops to be held in conjunction with the conference. The goal of the workshops is to provide a venue for presenting research on focused topics of interest and an informal forum to discuss research questions and challenges. Workshops with few submissions may be cancelled, shortened, merged with other workshops, or otherwise restructured. The organizers of accepted workshops are responsible for producing a call for participation and publicizing it, such as distributing the call to relevant newsgroups and electronic mailing lists, and especially to potential audiences from outside the IUI conference community. Workshop organizers will maintain their own website with information about the workshop and the IUI 2025 website will refer to this website. The workshop organizers will coordinate the paper solicitation, collection, and review process. A workshop summary will be included in the ACM Digital Library for IUI 2025, and we will separately publish a joint workshop proceedings for accepted workshop submissions (through CEUR or similar)
Generative Artificial Intelligence (GAI) models capable of complex tasks are revolutionizing areas previously considered to define humanity, such as creativity. This workshop investigates the design, implementation, and evaluation of intent-based co-creative experiences that boost human creativity in work, play, and education across text, images, audio, code, and video. Key questions focus on how creativity support can guide generative AI development and how to leverage generative models for positive user experiences. By uniting researchers and practitioners from Human-Computer Interaction (HCI) and AI, the workshop seeks to explore opportunities and challenges in developing meaningful and safe interactions with generative systems.
The explosion of Large Language Models, particularly auto-regressive as agents serving as chatbots, generative search and work automating tools, has also brought with it inherent limitations. We posit that efforts to alleviate these LLM challenges - hallucinations, unpredictability outputs, lack of transparency and difficulties in customization cannot be solved through algorithmic improvements alone, but requires elevated mixed-initiative interface design that is at the heart of the IUI community. We aim to bridge the gap between agent-driven automation and direct manipulation by exploring mixed-initiative interaction models that blend the strengths of both paradigms to empower end-users that seek to harness LLMs.
As AI transforms health and care, integrating Intelligent User Interfaces (IUI) in wellness applications offers substantial opportunities and challenges. This workshop brings together experts from HCI, AI, healthcare, and related fields to explore how IUIs can enhance long-term engagement, personalization, and trust in health systems. Emphasis is on interdisciplinary approaches to create systems that are advanced, responsive to user needs, mindful of context, ethics, and privacy. Through presentations, discussions, and collaborative sessions, participants will address key challenges and propose solutions to drive health IUI innovation.
The SOCIALIZE workshop aims to explore interactive techniques that promote the social and cultural integration of diverse users. We specifically intend to attract research that addresses the interaction challenges faced by different groups, with a focus on disadvantaged and at-risk categories (such as refugees and migrants) as well as vulnerable groups (including children, elderly, autistic, and disabled people). We are also interested in advanced human-robot interaction methods designed to develop social robots - autonomous robots that meaningfully engage with people by exhibiting socially affective behaviors and adhering to the necessary skills and protocols for successful collaboration.
The 'Strengthening Engineering Psychology for Human-AI Interactions' (STEP-HAI) workshop brings together researchers and practitioners to strengthen the theoretical foundations of Human-AI Interactions (HAI) rooted in engineering and cognitive psychology. As we increasingly interact with intelligent systems, a deep understanding of human cognition is essential for designing effective, efficient and trustworthy AI. Despite a growing body of empirical AI research, theoretical frameworks require further validation and refinement. This workshop invites submissions on foundational theory, experimental work on user interfaces, and insights into strengthening psychological principles in HAI, including information processing, AI-assisted decision making, and user perceptions for human-centred AI design.
Our full-day multidisciplinary workshop brings together researchers and practitioners from the IUI community in academia and industry to understand the challenges of designing and evaluating proactive agents in a human-centric manner. We will reflect on existing evaluation methods, identify challenges in designing proactive systems, and discuss potential solutions, best practices, and human-centric guidelines to bridge these gaps. Ultimately, our goal is to map out key focus areas and research challenges, fostering strong interdisciplinary relationships within and across fields related to Artificial Intelligence (AI) and Human-Computer Interaction (HCI).
This workshop explores the frontier of human-AI interaction through adaptive explanation interfaces. As AI becomes increasingly embedded in daily decision-making, it focuses on developing intelligent interfaces that dynamically adjust their explanations to meet diverse user needs and contexts. Drawing from the FAIR project's human-centric principles, AXAI brings together researchers, designers, and practitioners to address challenges in personalized XAI interfaces, novel interaction modalities, and ethical considerations. Special emphasis is placed on emerging technologies like LLMs and interactive machine learning approaches that enhance AI system explainability while maintaining meaningful human engagement.