Integrated Development Environments increasingly implement AI-powered code completion tools (CCTs), which promise to enhance developer efficiency, accuracy, and productivity. However, interaction challenges with CCTs persist, mainly due to mismatches between developers’ mental models and the unpredictable behavior of AI-generated suggestions, which is an aspect underexplored in the literature. We conducted an elicitation study with 56 developers using co-design workshops to elicit their mental models when interacting with CCTs. Different important findings that might drive the interaction design with CCTs emerged. For example, developers expressed diverse preferences on when and how code suggestions should be triggered (proactive, manual, hybrid), where and how they are displayed (inline, sidebar, popup, chatbot), as well as the level of detail. It also emerged that developers need to be supported by customization of activation timing, display modality, suggestion granularity, and explanation content, to better fit the CCT to their preferences. To demonstrate the feasibility of these and the other guidelines that emerged during the study, we developed ATHENA, a proof-of-concept CCT that dynamically adapts to developers’ coding preferences and environments, ensuring seamless integration into diverse workflows. • Users want flexible triggers: balance control with smart automation • Inline works for short code; sidebar/chatbot for longer suggestions • Start minimal, let users expand from single lines to full files • Keep explanations short, contextual, opened by click or shortcut • Let users tune timing, style, detail level, and coding format

Abstract The rapid growth in interest in Artificial Intelligence (AI) has been a significant driver of research and business activities in recent years. This raises new critical issues, particularly concerning interaction with AI systems. This article first presents a survey that identifies the primary issues addressed in Human-Centered AI (HCAI), focusing on the interaction with AI systems. The survey outcomes permit to clarify disciplines, concepts, and terms around HCAI, solutions to design and evaluate HCAI systems, and the emerging challenges; these are all discussed with the aim of supporting researchers in identifying more pertinent approaches to create HCAI systems. Another main finding emerging from the survey is the need to create Symbiotic AI (SAI) systems. Definitions of both HCAI systems and SAI systems are provided. To illustrate and frame SAI more clearly, we focus on medical applications, discussing two case studies of SAI systems.

In an era where Artificial Intelligence (AI) permeates our lives, its impact on children raises critical considerations. This workshop aims to delve into the multifaceted realm of Human-Centered AI (HCAI) for children, exploring the transformative role of AI in fostering creative expression and inclusive learning environments. Our goal is to unite diverse research expertise and methodologies, focussing on how AI can be tailored to meet diverse learning needs, enabling personalized and engaging educational experiences which support creativity. This workshop will bring together researchers, educators, technologists, and practitioners for expert talks, interactive demonstrations, and collaborative discussions,. Our goal is to foster a multidisciplinary dialogue on developing child-centered AI solutions that enhance creative learning while being mindful of inclusivity, ethical considerations and safeguarding against potential risks.

Tumors can manifest in various forms and in different areas of the human body. Brain tumors are specifically hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them in time can lower the chances of death and facilitate the therapy process for patients. The use of Artificial Intelligence (AI) and, more specifically, deep learning, has the potential to significantly reduce costs in terms of time and resources for the discovery and identification of tumors from images obtained through imaging techniques. This research work aims to assess the performance of a multimodal model for the classification of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and in line with similar works, as the model reaches an accuracy of around 98\%. We also highlight the need for explainability and transparency to ensure human control and safety.

This article explores Human-Centered Artificial Intelligence (HCAI) in medical cytology, with a focus on enhancing the interaction with AI. It presents a Human-AI interaction paradigm that emphasizes explainability and user control of AI systems. It is an iterative negotiation process based on three interaction strategies aimed to (i) elaborate the system outcomes through iterative steps (Iterative Exploration), (ii) explain the AI system's behavior or decisions (Clarification), and (iii) allow non-expert users to trigger simple retraining of the AI model (Reconfiguration). This interaction paradigm is exploited in the redesign of an existing AI-based tool for microscopic analysis of the nasal mucosa. The resulting tool is tested with rhinocytologists. The article discusses the analysis of the results of the conducted evaluation and outlines lessons learned that are relevant for AI in medicine.

This demo presents SERENE, a Web platform for the UX semi-automatic evaluation of websites. It exploits Artificial Intelligence to predict visitors’ emotions starting from their interaction logs. The predicted emotions are shown by interactive heatmaps overlapped to the webpage to be analyzed. The concentration of negative emotions in a specific area of the webpage can help the UX experts identify UX problems.