Lexical Knowledge Bases (KBs) are central to NLP but remain costly to maintain, limited in coverage, and slow to adapt to linguistic change. This study explores the potential of large language models (LLMs) to fix these issues through two key research questions: (RQ1) Are LLMs capable of producing correct and novel information suitable for integration into existing KBs? and (RQ2) To what extent can LLM-generated candidates augment an existing KB beyond its current coverage, and what does the gap between automatic overlap and human-validated correctness imply about that augmentation potential? To investigate these, we introduce a three-stage, model-agnostic pipeline that transforms KB entries into structured prompts to generate candidate concepts, applies human-in-the-loop validation to assess novel outputs, and analyzes overlap with existing KB content to probe coverage. Experiments employed eight open-weight LLMs ranging from 7B to 104B parameters, four diverse KBs (ConceptNet, FrameNet, Semagram, MultiAligNet), and variations in prompting strategies (zero-shot/one-shot) and output formats (JSON/comma-separated), producing 384000 prompt-response pairs. Regarding RQ1, human validation confirmed 86.7% of novel candidates as semantically accurate (95% CI: 85.9-87.4%), with per-KB rates highest for MultiAligNet at 93.1%; one-shot prompting and JSON formatting yielded superior results, while model performance showed no correlation with parameter scale. For RQ2, automatic metrics indicated low overlap between LLM outputs and existing KB entries (e.g., F1@10=0.14 for ConceptNet); when combined with the high human approval rate of non-overlapping candidates, this pattern is consistent with coverage gaps in the studied resources, though other factors (e.g., normalization mismatches, generation bias) may also contribute. Recall patterns correlated with KB size and relationship specificity. Combined with HLoop, this gap measures the augmentation surface available from a single open-weight model: most non-overlapping items are valid candidates the KB does not yet contain, and HLoop quantifies the fraction usable for augmentation, shifting part of the human effort from knowledge creation toward verification.

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.

Adaptive and personalized systems increasingly mediate everyday digital experiences, and recent advances in LLMs, NLP, and Generative AI have amplified their reach, from intelligent user interfaces and conversational agents to AR/immersive interactions and autonomous assistants. These methods now support applications in health and well-being, behavior change and persuasion, e-learning and educational games, and group modeling for collaboration and team formation, all of which require increasingly rich and dynamic user models. At the same time, modern pipelines based on data mining, knowledge graphs/linked data, semantic representations, and affective computing raise urgent questions about transparency, privacy, fairness, accountability, and user understanding, reinforced by regulatory expectations such as the GDPR right to explanation. Yet research often optimizes personalization performance without comparable attention to interpretability and human comprehension. This workshop provides a forum for theoretical, methodological, and empirical work that bridges effectiveness and explainability, with emphasis on robust human-centered evaluation and reproducible practices, including benchmarks, datasets, and shared challenges that advance trustworthy personalization in an era of increasingly autonomous AI.

Persuasive systems aim to support behavior change using interactive technologies. Among them, adaptive persuasive games, which combine game-based approaches with adaptive features, have recently emerged as promising tools for promoting positive behavioral changes. In this article, we report the findings of a systematic literature review of existing adaptive persuasive games (N = 55), following the Grounded Theory approach for Literature Review. We point out the main types of game-based approaches employed in the literature and the importance of aesthetics, playability, and variety in the game design. Then, we investigate the adaptation techniques employed and the central role of users’ individuality in the proposed designs. Moreover, we analyze the main persuasive theories on which adaptive persuasive games rely, as well as the most commonly used intervention designs. Finally, we describe the evaluation methodologies used and the outcomes of the proposed interventions in terms of behavior change, motivation and engagement. In conclusion, we discuss the issues emerging from the current literature and identify possible solutions and future research directions for adaptive persuasive game design.

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.

Video games are designed for enjoyment and can benefit players, but in competitive settings such as esports they may lead to stress, a significant concern for players and coaches in competitive play due to potential negative psychological, social, and physical impacts. Since esports players are mostly adolescents and young adults, the vulnerability of their age underscores the need to investigate their stress experience. To this aim, we conducted semi-structured interviews with 16 First-Person Shooter (FPS) players and coaches (mean age 22.2) across A- to C-tiers. We examined players’ subjective stress experiences, team dynamics, and the broader esports organizational context. Findings reveal the players’ ability to recognize their stress signals and to make sense of stressful events, along with a stronger reliance on individual coping strategies over team-based or organizational resources, which remain largely inaccessible. Finally, we propose design strategies to strengthen esports players’ resilience and coping capacity in stressful environments.

Generative Artificial Intelligence (GenAI) text-to-image models have made significant progress in emulating human-like outputs. However, understanding the inner functioning of these models remains a challenge due to their complexity and black-box nature. It has been observed that individuals naturally develop informal conceptualizations, termed “folk theories,” to explain the behaviors of algorithmic systems. The specific nature of GenAI text-to-image models, which are obscure in their working principles, yet carry out activities that are peculiar to humans, makes it interesting to investigate people’s theorization about this technology. With this aim, we conducted a qualitative interview study with 20 participants and observed how they accounted for the outputs of Stable Diffusion. The study findings show that participants developed a wide spectrum of conceptualizations, including folk theories that appear distinctive of GenAI text-to-image technology, also ascribing to the model a variety of “mental states.” Furthermore, we found that theory building follows different inductive and deductive trajectories, with participants employing diverse strategies to explain the functioning of the technology.

• We conducted a qualitative study on how people perceive LLMs’ unpredictable behaviors • We interviewed 20 participants to gather their feedback on a hallucination dialogue • We found that unpredictable behaviors change how people experience ChatGPT • We show that these behaviors evoke unsettling emotions and fear of AI Large Language Models (LLMs) have shown impressive capabilities in producing texts of quality and fluency that are similar to those created by humans. Despite their increasing use, however, the broader population's experience of many aspects of interaction with LLMs remains underexplored. This study investigates how diverse individuals perceive and account for “nonsensical hallucinations”, namely, an LLM's unpredictable and meaningless behavior provided as a response to a user's request. We asked 20 participants to interact with ChatGPT 3.5 and experience its hallucinations. Through semi-structured interviews, we found that participants with a computer science background or consistent previous use of LLMs interpret unpredictable nonsensical responses as an error, while novices perceive them as model's autonomous behaviors. Moreover, we discovered that such responses produce an abrupt modification of participants’ perceptions and understandings of the LLM's nature. From a soothing and polite entity, ChatGPT becomes either an obscure and unfamiliar “alien”, or a human-like being potentially hostile to humankind, making also emerge unsettling feelings, which may unveil an underlying fear of Artificial Intelligence. The study contributes to literature on how people react to the unfamiliarity of a technology that may be perceived as alien and yet extremely human-like, generating “uncanny effects,” as well as to research on the anthropomorphizing of technology.

Generative Artificial Intelligence (Gen-AI) has rapidly advanced in recent years, potentially producing enormous impacts on industries, societies, and individuals in the near future. In particular, Gen-AI text-to-image models allow people to easily create high-quality images possibly revolutionizing human creative practices. Despite their increasing use, however, the broader population's perceptions and understandings of Gen-AI-generated images remain understudied in the Human-Computer Interaction (HCI) community. This study investigates how individuals, including those unfamiliar with Gen-AI, perceive Gen-AI text-to-image (Stable Diffusion) outputs. Study findings reveal that participants appraise Gen-AI images based on their technical quality and fidelity in representing a subject, often experiencing them as either prototypical or strange: these experiences may raise awareness of societal biases and evoke unsettling feelings that extend to the Gen-AI itself. The study also uncovers several “relational” strategies that participants employ to cope with concerns related to Gen-AI, contributing to the understanding of reactions to uncanny technology and the (de)humanization of intelligent agents. Moreover, the study offers design suggestions on how to use the anthropomorphizing of the text-to-image model as design material, and the Gen-AI images as support for critical design sessions.

Artificial Intelligence is undoubtedly becoming pervasive in everyday life of everyone. In this setting, developing correct AI conception since childhood is not only a need to be addressed in educational curricula, but is also a children right. Accordingly, several initiatives at national and international levels aim at promoting AI and emerging technology literacy, supported also by a proliferation in the literature of learning courses covering a variety of topics, learning objectives and targeted ages. Schools are therefore pushed to introduce innovative activities for children in their curricula. In this paper, we report the results of a case study where we tested the contribution of an AI block-based course in developing computational thinking, and human and AI minds understanding in fifth and sixth grade children.

The COVID-19 pandemic led to dramatic changes in people’s lives. The Human-Computer Interaction (HCI) community widely investigated technology use during crises. However, commercial video games received minor attention. In this article, we describe how video game play impacted the life transformations engendered by the pandemic. We administered a qualitative online survey to 330 video game players who were living in Italy during the lockdown measures. We found that the COVID-19 pandemic altered the participants’ sense of time and space, reshaped both their intimate and wider social interactions, and elicited a wide spectrum of disturbing emotions. Players escaped from this unsatisfying reality into video game worlds, searching for a new normality that could compensate for the unpredictability and dangerousness of the pandemic life, as well as seeking uncertainty in the game environments to balance the flatness of the lockdown everydayness. In doing so, they “appropriated” the gaming technologies, which also led to several unexpected outcomes. Starting from these findings, we propose a model of escapism that points out four ways to escape from reality into video game worlds. Moreover, we outline some design implications that might inspire future strands of research in the field of crisis technologies.

Cheating is a major concern for any gaming environment, but effective solutions are still far from being found. Previous research has overlooked the social complexity of current video games and the role that technology may have in determining the cheating practices. In this article, we explore how cheating unfolds within Call of Duty: Warzone through a digital ethnography, analyzing the perspectives of both amateur players and video game streamers and how technology shapes the cheating phenomenon. We highlight that players’ perception of cheating depends on the “social role” of the cheater. Moreover, the technological sophistication of cheats makes them uncertain in the players’ eyes, contributing to the spreading of mutual suspicion and surveillance practices. Based on these findings, we offer a novel understanding of cheating as a sort of black box that only a few individuals are able to decipher, and several design considerations addressing the cheating practices in contemporary games.

A central problem in text-based chatbot research revolves around humanness: not only chatbots may reproduce fundamental aspects of being human, but also users may attribute humanlike features to chatbots. Humanness is paramount in human-chatbot conversations because it may profoundly impact the quality of the interaction: for example, when users expect that interactions with chatbots are similar to human conversations and these expectations are not met by the agent, they may easily become frustrated. This is particularly relevant in customer care, where efficient support is essential: here, unmet expectations about the chatbot’s humanlike capabilities can undermine customer satisfaction. In this article, we qualitatively analyzed 12,477 real-world exchanges with a task-based chatbot in the customer care domain, involving a total of 1,060 conversations. We conducted a single case study to define a preliminary theoretical model of humanness for chatbot technology. The study findings point to a novel conceptualization of humanness in customer-chatbot interaction, highlighting that it is multiple, contextual, modular, and dynamic. Moreover, the theoretical model that we propose explains that the kind of humanness attributed to a chatbot depends on the context in which people expect to interact, the objectives and needs that they aim to fulfill, and the cues that the chatbot exhibits: all these factors may change as the interaction evolves over time, and such changes may further affect the users’ ascriptions of humanness. Finally, we propose design implications of the model, like the need to create “context” and account for the plurality of humanness.

Abstract Users of online recipe websites tend to prefer unhealthy foods. Their popularity undermines the healthiness of traditional food recommender systems, as many users lack nutritional knowledge to make informed food decisions. Moreover, the presented information is often unrelated to nutrition or difficult to understand. To alleviate this, we present a methodology to generate natural language justifications that emphasize the nutritional content, health risks, or benefits of recommended recipes. Our framework takes a user and two recipes as input and produces an automatically generated natural language justification as output, based on the user’s characteristics and the recipes’ features, following a knowledge-based recommendation approach. We evaluated our methodology in two crowdsourcing studies. In Study 1 ( $$N=502$$ N = 502 ), we compared user food choices for two personalized recommendation approaches, based on either a (1) single-style justification or (2) comparative justification was shown, using a no justification baseline. The recommendations were either popularity-based or health-aware, the latter based on the health and nutritional needs of the user. We found that comparative justification styles were effective in supporting choices for our health-aware recommendations, confirming the impact of our methodology on food choices. In Study 2 ( $$N=504$$ N = 504 ), we used the same methodology to compare the effectiveness of eight different comparative justification strategies. We presented pairs of recipes twice to users: once without and once with a pairwise justification. Results indicated that justifications led to significantly healthier choices for first course meals, while strategies that compared food features and emphasized health risks, benefits, and a user’s lifestyle were most effective, catering to health-related choice motivations.

A central problem for chatbots in the customer care domain revolves around how people collaborate with the agent to achieve their own situated goals. The majority of the previous research, however, relied on experiments within artificial settings, rather than on observation of real-world interactions. Moreover, such research mostly analyzed users’ responses to communication breakdowns, rather than the wider collaboration strategies utilized during a conversation. In this paper, we qualitatively analyzed 12,477 real-world exchanges with a task-based chatbot using a Grounded Theory approach as a rigorous coding method to analyze the data. We identified two main aspects of collaboration, behavioral and conversational, and for each aspect we highlighted the different strategies that users perform to “work together” with the agent. These strategies may be utilized from the very beginning of the conversation or in response to misunderstandings in the course of ongoing interactions and may show different evolving dynamics.

The workshop aims to discuss open problems, challenges, and innovative research approaches in the area of persuasive and behavior change recommender systems, that is, recommender systems aimed at modifying people's habits and behavior. Some questions that motivate this workshop are: What kind of theory is more suitable to inform the design of behavior change recommender systems? What kind of personal data (e.g., coming from environmental sensors, wearable devices, etc.) should we use to design behavior change recommendations? How should we deliver them (i.e., what kind of communication channels and interfaces should we use)? What kind of strategies should we implement to design timely and contextualized recommendations? How can we support the user's motivation to adhere to the recommendations provided? How can we “persuade” users in the long term?

Wearable technologies are increasing both in number and variety enabling new ways for collecting personal data, as well as novel interaction modalities. Even though the Human-Computer Interaction (HCI) community has widely explored the potential applications of wearables, its theoretical contribution on this research field has been far from impressive. Most scholars and designers seem to rely on a series of dominant assumptions that look at wearables “from the outside” by focusing on their “external properties.” When these assumptions are fully embraced at design-time, however, they may cloud opportunities for designing for the “internal aspects” of our everyday experience. In this article, I propose a theory that looks at wearables “from the inside,”giving a theoretical backdrop to all those wearable designs that pay attention to the internal aspects of interaction. By adopting a postphenomenological approach, I conceptualize wearable devices as “extensions” of our intentionality and introduce the “extension relation” to explain how wearables may alter how we relate to the world. In doing so, I propose a series of design considerations that aim to trace future research lines for all those wearables that are currently designed from an “externalistic” perspective.

The outbreak of the COVID-19 has made apparent the importance of gaming technologies during crises and difficult life moments. However, studies exploring the role of commercial off-the-shelf video games during the pandemic are still limited. Moreover, research often focuses on a specific moment of the crisis. In this article, we investigate how playing commercial video games impacts on how players manage and understand the COVID-19 pandemic considered in its temporal evolution. We conducted a one-year-and-a-half longitudinal qualitative study by interviewing 16 participants who were living in Italy during the first pandemic lockdown, then involving a subgroup of them (N=10) in a follow-up interview. Study findings show that playing not only supported escapism from a difficult reality, but also helped individuals counteract the sense of detachment from the “pandemic world”, encouraging reflection and supporting sense-making. These functions of video games varied as the pandemic unfolded. Based on these findings, we present a preliminary model of the role of video game technologies during moments of crisis and suggest design considerations on how video games could support people experiencing extremely difficult life moments.

The majority of behavior change and persuasive technologies are exclusively addressed to modify a specific behavior. However, the focus on behavior may cloud the “existential aspects” of the process of change. To explore the lived and meaning-laden experience of behavior change, we interviewed 23 individuals who have used behavior change technology in their everyday life. The study findings highlight that behavior change is tied to meanings that point to existential matters, relates to a nexus of life circumstances, and unfolds over long periods of time. By contrast, the technology used by the participants appears mostly to focus on the present target behavior, ignoring its links to the participants’ life “context” and “time,” also providing scarce help for sense-making. Based on these findings, we surface a preliminary “existential model of behavior change,” identify several barriers that may prevent the modification of behavior and propose some design suggestions to overcome them.

Game-based interventions have been gradually and successfully implemented in the mental health domain given the games’ ability to positively affect a variety of mental health conditions. To this aim, scholars have recently discovered the usefulness of Commercial Off-the-Shelf (COTS) video games, due to their increasing popularity, availability, and cost effectiveness. Nevertheless, key aspects of this line of research have not emerged yet, since a comprehensive overview of how commercial video games impact on different mental disorders is still missing. In this article, we present a systematic literature review of recent research that focuses on the usage of commercial video games in mental health. We analyse 39 papers and map the relevant themes that are recurrent in the last ten years of research, offering a detailed understanding of the methodological approaches that were used, the results obtained, the main disorders addressed, and the video game genres exploited. On the basis of these findings, we highlight open issues in current work and point out a variety of research opportunities that could be tackled in future years, like the need of conducting more field and longitudinal studies, the necessity of developing the design knowledge, and the possibility of connecting research with clinical practice.

This special issue focuses on new perspectives of time within HCI research. We begin by explicating how HCI’s views of time have broadened over the years, going beyond the traditional subjective-objective dichotomy. We describe how the HCI community was originally anchored to clock time, but more recently the field has begun to incorporate the existential, social, and cultural dimensions of time. We introduce the papers in this special issue by contextualizing them according to different research areas. We conclude by identifying open issues and opportunities that should be explored in future HCI research and practice.

In this article, we investigate the case of human-machine dialogues in the specific domain of commercial customer care. We built a corpus of conversations between users and a customer-care chatbot of an Italian Telecom Company, focusing on a sample of conversations where users contact the service asking for explanations about billing issues or overcharges. We observed that users’ requests are often vague, generic or incomprehensible. In such cases, commercial dialogue systems typically ask for clarifications or further details to fully understand users’ specific requests. However, from the corpus analysis it appeared that chatbot's clarifying requests may result in ineffective interactions, with users eventually giving up the conversation or switching to a human agent for a faster query resolution. A recovery strategy is thus needed to anticipate users’ information needs, or intentions. We address this issue resorting to GEN-DS, a dialogue system based on symbolic data-to-text generation. GEN-DS analyzes the user-company contextual relational knowledge, with the aim to generate more relevant answers to unclear questions. In this article, we describe the GEN-DS architecture along with the experiments we carried out to evaluate its output. Results from an offline human evaluation show significant improvements of GEN-DS compared to the original system. These improvements concern properties such as utility, necessity, understandability, and quickness of the information communicated in the dialogue. We believe that GEN-DS techniques may find application in all the dialogue systems that need to manage vague requests and must rely on relational knowledge.

Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of recommended recipes. We designed a framework that takes a user and two food recommendations as input and produces an automatically generated natural language justification as output, which is based on the user’s characteristics and the recipes’ features. In doing so, we implemented and evaluated eight different justification strategies through two different justification styles (e.g., comparing each recipe’s food features) in an online user study (N = 503). We compared user food choices for two personalized recommendation approaches, popularity-based vs our health-aware algorithm, and evaluated the impact of presenting natural language justifications. We showed that comparative justifications styles are effective in supporting choices for our healthy-aware recommendations, confirming the impact of our methodology on food choices.

Design ethnography has been widely used in Human-Computer Interaction (HCI) to understand people’s everyday behaviors, in order to build technologies capable of meeting users’ needs. Building on top of the recent debate on ethnography within HCI, this article proposes to employ reflexivity and theoretical pluralism to ground a new way of using design ethnography in HCI, directly envisioning novel designs during the fieldwork. Inspired by design practices like biomimicry, I describe the figure of the ethno-designer, a digital design ethnographer who dives into ‘successful’ virtual environments in search for insightful design patterns, with the purpose of creating new designs in other, even distant, contexts. In this perspective, the fieldwork becomes an incessant source of inspiration for identifying effective ‘design elements’, understanding how they work and their ‘experiential effects’, and producing design implications to create novel technologies across multiple application domains.

A central process of virtual organization design relates to how newcomers are assimilated into organizational dynamics. Research on organizational assimilation has traditionally investigated 'serious' organizational contexts. Nonetheless, video games can offer insights on how such assimilation can be effectively supported. In this article, I propose to look at World of Warcraft (WoW) to understand how individuals can be successfully integrated into online organizations. Through an ethnographic research, made up of participant observation and 36 semi-structured interviews, I explore how specific game design elements support organizational assimilation into WoW raiding guilds. This shows how designers elicit extremely engaging organizational dynamics that encourage players to identify with their organizations. Based on these findings, I propose design considerations for gamifying virtual organizations that draw from the structure of WoW raiding guilds.

People with autism have idiosyncratic sensory experiences, which may impact on how they live the “spaces” of their everyday life. Starting from an investigation of their conception and experience of “secure places,” we defined a series of user requirements for designing technology that supports their everyday movements in the urban environment. On the basis of such requirements, we developed an interactive system that leverages crowdsourcing mechanisms to map places that are perceived as secure by the population with autism.

Mobile applications have a great potential in making everyday environments more accessible from the cognitive point of view, allowing neurodiverse people, such as individuals with autism, dementia, or ADHD, to gain independency and find continuous support. This workshop will discuss the main technological, methodological, theoretical and design issues that researchers and practitioners are facing when designing mobile devices and services for neurodiversity, exploring novel strategies to address them. In doing so, we want to focus on the neurodiverse people's idiosyncratic needs, also exploring ways for directly involving them in the design process.

This paper describes the preliminary results of a project aimed to support people with autism in finding city places that match their "sensorial" preferences and aversions. Through a participatory design approach, we designed an interactive map that collects sensorial data about the urban environment exploiting crowdsourcing mechanisms.

Human–computer interaction researchers are increasingly designing behaviour change technologies for a variety of purposes, from promoting healthier lifestyles to support sustainable habits. These technologies are commonly assessed in terms of their effectiveness in modifying human behaviour. Nevertheless, the multifaceted social and psychological long-term consequences of these kinds of artifacts are often forgotten. To explore their design space, we involved 48 students asking them to envision and design future behaviour change systems. Following the recent interest in design fictions, which present ‘fantasy prototypes’ in plausible near futures, we investigated how designs might help people think of the presuppositions and implications of technology. Then, we analyzed four design fictions to explore themes that are often forgotten in the current behaviour change debate. We finally discussed the empirical and methodological outcomes of our study and presented a series of design considerations and research questions that could stimulate further reflections on behaviour change technologies and the method we employed.

This paper presents a literature review of human- computer interaction works on wearable systems for sports. We selected a corpus of 57 papers and analyzed them through the grounded theory for literature review approach. We identified five themes across the papers: the different research perspectives, the type of sports and sportspeople, the roles of wearables in sports, their wearability, and the different types of feedback. These themes helped us in delineating opportunities for future research: the investigation of different form factors and types of feedback; the consideration of different sportspeople and collaborative tasks; the need of pushing the boundaries of the sports domain; the exploration of the evolution of sports; the interconnection of different devices; and the increase of methodological rigor.

Human-Computer Interaction researchers are increasingly designing technologies aimed at supporting “behavior change.” The model of change, which most of these works embrace, focuses on the idea that change occurs on the behavioral level and that it is externalistic, monistic, mechanistic, fragmented, and episodic. We think that a different take, focusing on the internal aspects of change, may integrate and extend what has been done using this behavioral model. We conducted 20 interviews exploring how individuals live, account for, and manage life changes. Then, we outlined five tentative patterns we identified across different kinds of changes reported by the interviewees, pointing out that change might be internalistic, multiple, intentional, holistic, and continuous. This led us to propose a set of design considerations for the evolution of the current behavior change technologies. Finally, we suggested some preliminary lines of future research, which aim to open the design space of technologies for change.

World of Warcraft (WoW) may be a source of inspiration to enrich the Personal Informatics systems user's experience and, at the same time, improve gamification design. Through the findings of a four-year reflexive ethnography in WoW, I outline how its game design elements support players in making sense of their own data, emphasizing how "game numbers" are turned into meanings. On the basis of the study results, I propose a series of design considerations to be used in the design of self-tracking systems, which recommend to embody data into digital entities, provide different analytical tools depending on the users' expertise through a flexible model, and foster the formation of "communities of practice" in order to support learning processes.

Thanks to the advancements in ubiquitous and wearable technologies, Personal Informatics (PI) systems can now reach a larger audience of users. However, it is not still clear whether this kind of tool can fit the needs of their daily lives. Our research aims at identifying specific barriers that may prevent the widespread adoption of PI and finding solutions to overcome them. We requested users without competence in self-tracking to use different PI instruments during their daily practices, identifying five user requirements by which to design novel PI tools. On such requirements, we developed a new system that can stimulate the use of these technologies, by enhancing the perceived benefits of collecting personal data. Then, we explored how naïve and experienced users differently explore their personal data in our system through a user trial. Results showed that the system was successful at helping individuals manage and interpret their own data, validated the usefulness of the requirements found and inspired three further design opportunities that could orient the design of future PI systems.

Despite the growing interest for gamification research within the Human–Computer interaction community, gamification techniques still suffer from a limited availability of game elements to be employed in design practices. Researchers and practitioners are anchored to the use of points, badges, and leaderboards, rarely introducing novel design solutions coming from games. In this article, I propose to look at World of Warcraft (WoW) to find insights for designing novel online communities based on gamified features. By exploring through an ethnographic work how WoW “shapes” its players’ social relations and how specific “social game elements” impact on their subjective experiences, I will propose a series of design suggestions that could be employed to “enhance” the user experience within online communities in domain as diverse as learning, fitness, and crowdsourcing.

Technological advances in wearable computing are changing the sports domain. A variety of Personal Informatics (PI) tools are starting to provide support and improve athletes’ performance in many sports. In this article, we interviewed 20 amateur and elite athletes of different disciplines, using an array of PI devices, to explore how sports, as well as athletes’ experience, are affected by such instruments. We discovered that amateur athletes present different patterns of usage compared to elite ones. Moreover, we found that elite athletes make sense of their data by exploiting the knowledge they have about their own body and sports practice. We then proposed four considerations for design that we believe should be explored in the future, to reflect on how self-tracking is changing our perspective on sports, and, by and large, on our everyday life.

Consumption practices show a strong presence of crystallised social understandings, organising rules and permanent ways of acting that prevent individuals from changing towards more sustainable habits. Over the years, human–computer interaction research tried to help people engage in sustainable lifestyles promoting the health of the Earth. However, by favouring an individualistic and rationalistic approach to design, these attempts often lacked a deep understanding of how individuals are intertwined with social dynamics and organisational structures that might determine their actions. In this article, we aim at exploring novel solutions to support people’s sustainable habits, by focusing on their everyday purchases. Using an ethnographic method grounded in the social practices approach, we analyse the value that individuals ascribe to activities and objects that seem already addressed to sustainable consumption: the packaging-free purchasing practices. Starting from the insights gathered from this research, and leveraging the opportunities opened by the 3D printing technology, we design an interactive system with the aim to break the old buying routines and support the reuse of containers.

Although Personal Informatics stresses the importance of “self”-awareness and “self”-knowledge in collecting personal data, a description of the “self,” to which all these knowledge endeavors are addressed, is missing in the current debate. In this article we first review how the different theoretical assumptions that currently inform the design of Personal Informatics tools fail to convey a convincing image of the self, which ought to be quantified by these technologies. We then move on to the outline of a theory of the self that may ground the current discourse in Personal Informatics. Building on this theoretical framework, we propose a set of design guidelines as its implications, which may drive the design of future self-tracking technologies. Finally, we outline a research agenda, organized around such guidelines, in the form of research questions to be addressed in the future.

Human–computer interaction (HCI) researchers are increasingly designing technologies for behavior change, drawing on different perspectives, theories and models. In recent years, a new approach has started to be explored within the HCI community: gamification is more and more used by researchers and designers to drive users’ behaviors toward specific directions. However, this design technique has showed several limits, especially for the choice of game elements to be employed. Drawing inspiration from the videogame world, this research wants to suggest the employment of game elements that could contribute and support behavior change processes in different settings. Looking at the Massively Multi Player Online Role-Playing Games (MMORPGs) as a source of inspiration, an ethnographic study in World of Warcraft (WoW) has been conducted. We found that WoW is capable of driving its players’ behavior by employing incentives and penalties, as well as by providing social support and favoring mentorship. By creating a persistent world, supporting the recollection of players’ past memories, leveraging narratives and designing the time of play, WoW further affects players’ habits, increasing their retention in the game world. Findings from the fieldwork grounded the development of a set of design strategies that suggest how to use such elements in the design of interactive systems addressed to change users’ behaviors: for example, they recommend to design incentive systems that exploit different scheduling, as well as to create digital mementos to trigger positive memories that may support the change. Such strategies were then compared with game elements that can be found in other MMORPGs to allow for a greater generalization, and discussed with reference to HCI literature.

This paper describes a novel methodology to evaluate a social media application in its formative phase of design. Taking advantage of the experiences developed in the Alternate Reality Games, we propose to insert game mechanics in the test setting of a formative evaluation of a prototypical social system. As a use case, we present the evaluation of WantEat, a prototypical social mobile application in the gastronomical domain. The evaluation highlighted how the gamification of a field trial can yield good results when evaluating social applications in prototypical status. From a methodological point of view, gamifying a field trial overcomes the cold start problem, caused by the absence of active communities, which can prevent the participation of users and therefore the collection of reliable data. Our experience showed that the gamification of a field evaluation is feasible and can likely increase the quantity of both browsing actions and social actions performed by users. Based on these results, we then are able to provide a set of guidelines to gamify the evaluation session of an interactive system.

This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure. The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.

Wanteat is a framework and a suite of applications which allow users to interact with and explore mixed social networks of smart objects and people in the gastronomy domain, thus promoting the cultural heritage of a territory. Wanteat interaction model is based on the concept of a "wheel" [1].

Seamless communication is spreading in all areas of everyday life thanks to the greater interoperability of the consumer electronic devices. The opportunity richness that this scenario opens must be designed with the end users who will face both new opportunities but also new elements of complexity. This requires to take carfully into account their perspective in order to really help their daily lives through this new communication and media fruition platform for the home environment based on user meaningful service scenarios. The use of personas throughout the design phase of services has allowed us to take into account not only users actual behaviour, but also their needs and desiderata in order to translate them into solutions inproving their daily lives.