Physical inactivity remains a critical global health issue, yet scalable strategies for sustained motivation are scarce. Conversational agents designed as simulated exercising peers (SEPs) represent a promising alternative, but their long-term impact is unclear. We report a six-month randomized controlled trial (N=280) comparing individuals exercising alone, with a human peer, or with a large language model-driven SEP. Results revealed a partnership paradox: human peers evoked stronger social presence, while AI peers provided steadier encouragement and more reliable working alliances. Humans motivated through authentic comparison and accountability, whereas AI peers fostered consistent, low-stakes support. These complementary strengths suggest that AI agents should not mimic human authenticity but augment it with reliability. Our findings advance human-agent interaction research and point to hybrid designs where human presence and AI consistency jointly sustain physical activity.

Possible side effects of using web job boards in the e-recruitment context, such as candidates dropping out from the hiring process, may emerge if these tools are not transparent about data usage, collection, and processing. In response, we developed a novel web job board designed to enhance transparency, simulating a job-matching recommender system. A qualitative study with 20 Italian participants, combining direct observation of the job board use with the Thinking Aloud protocol and interviews, examines participants’ privacy behaviours in terms of data disclosure and seclusion. Findings indicate a general willingness among participants to share personal data, except for information related to their identity. We found that both the design of the job board and the meanings ascribed by participants to data shaped their privacy behaviours. Features enhancing user understanding of data usage and control of privacy settings were positively received, underscoring the importance of design in fostering thoughtful engagement with job board technologies. We contribute to research on privacy behaviours in the context of job search and we draw suggestions from the study findings on how to design platforms that support data protection and allow safe and purposeful disclosure of personal data, sustaining job seekers throughout the recruitment process.

This article describes a tangible interface for an e-coach, co-designed in four countries to meet older adults’ needs and expectations. The aim of this device is to coach the user by giving recommendations, personalized tasks and to build empathy through vocal, visual, and physical interaction. Through our co-design process, we collected insights that helped identifying requirements for the physical design, the interaction design and the privacy and data control. In the first phase, we collected users’ needs and expectations through several workshops. Requirements were then transformed into three design concepts that were rated and commented by our target users. The final design was implemented and tested in three countries. We discussed the results and the open challenges for the design of physical e-coaches for older adults. To encourage further developments in this field, we released the research outputs of this design process in an open-source repository.

This paper presents the decision support system that has been defined and developed under the umbrella of the NESTORE project. The main goal of the proposed system is to help users in selecting coaching plans by proposing personalised recommendations based on their behaviours and preferences. Recognising such behaviours and their evolution over time is therefore a crucial element for tailoring the interaction of the system with the user. A three-layer system composed of pathways, coaching activity plans, and coaching events, constitutes the so-called coaching timeline on which the analysis is grounded. Various techniques are used to model and personalise the recommendations and feedback. Firstly, the indicators are extracted from disparate data sources, then these are modelled through a profiling system and, finally, recommendations on the pathways and coaching plans are performed through a scoring and a tagging system.

Ethics by Design concerns the methods, algorithms and tools needed to endow autonomous agents with the capability to reason about the ethical aspects of their decisions, and the methods, tools and formalisms to guarantee that an agent's behavior remains within given moral bounds. In this context some questions arise: How and to what extent can agents understand the social reality in which they operate, and the other intelligences (AI, animals and humans) with which they co-exist? What are the ethical concerns in the emerging new forms of society, and how do we ensure the human dimension is upheld in interactions and decisions by autonomous agents?. But overall, the central question is: "Can we, and should we, build ethically-aware agents?" This paper presents initial conclusions from the thematic day of the same name held at PRIMA2017, on October 2017.

This workshop aims at discussing the rich possibilities that the body offers to experience the external world and the prospects that arise for interaction designers when these often-neglected abilities are taken into account. In particular, the workshop will focus on the rediscovery of the five senses, either alone or in a multimodal combination, and of the perceptual-motor abilities of our body. The one-day workshop is divided in two steps: first, participants will have the possibility to briefly present and discuss with the other attendees their research. Workshop candidates are requested to send a position paper, including a short biography and detailing their research interests and background. Second, participants will have the possibilities to explore and rediscover their sensorimotor abilities through several exercises and games abilities using a critical design approach. Interdisciplinary groups will be challenged to design and develop new interaction experience concepts using our natural 'tools' as prototyping tools.

This paper presents the concept of a lamp that allows displaying and collecting user's emotional states. In particular, it displays the emotional information changing colors and facial expressions; in fact, the lamp is characterized by anthropomorphic form and behavior in order to make the interaction more natural and spontaneous. The user can interact with the lamp through tangible gestures typically used in social interactions by humans. Two different scenarios involving the use of the lamp as a companion and for computer-mediated communication are presented.

"Eyes on the road, hands on the wheel" is a crucial principle to be taken into account designing interactions for current in-vehicle interfaces. Gesture interaction is a promising modality that can be implemented following this principle in order to reduce driver distraction and increase safety. We present the results of a user elicitation for gestures performed on the surface of the steering wheel. We asked to 40 participants to elicit 6 gestures, for a total of 240 gestures. Based on the results of this experience, we derived a taxonomy of gestures performed on the steering wheel. The analysis of the results offers useful suggestions for the design of in-vehicle gestural interfaces based on this approach.

Social awareness streams limit the expressivity of emotions in computer mediated communication. In this demo, we present a system that allows sharing emotional states in a social group with multimodal ambient feedback in order to provide a more immersive and natural interaction experience.

In this paper, we present a novel opportunistic paradigm for in-vehicle gesture recognition. This paradigm allows using two or more subsystems in a synergistic manner: they can work in parallel but the lack of some of them does not compromise the functioning of the whole system. In order to segment and recognize micro-gestures performed by the user on the steering wheel, we combine a wearable approach based on the electromyography of the user's forearm muscles, with an environmental approach based on pressure sensors integrated directly on the steering wheel. We present and analyze several fusion methods and gesture segmentation strategies. A prototype has been developed and evaluated with data from nine subjects. The results prove that the proposed opportunistic system performs equal or better than each stand-alone subsystem while increasing the interaction possibilities.