Emerging technologies have become a key area of interest in sports and rehabilitation research. Technologies such as extended reality and motion capture have much to offer patients returning to sport after injury, including motivating compliance, managing anxiety, understanding progress, and increasing autonomy. To explore how emerging technologies can complement a real case of rehabilitation and return-to-sport, we used an autoethnography to document the first author’s 11-month recovery following knee surgery and her gradual return to figure skating competition. We analyze journal entries documenting progress at different stages of recovery, clinical notes from physical therapy and training, and reflections following the use of novel interventions. The results of this study particularly highlight the utility of emerging technologies for balancing physical and psychological needs during return-to-sport and for improving the patient’s education, awareness, and autonomy. Future work should explore additional combinations of novel technologies with larger cohorts to identify more effective methods for complementing standardized rehabilitation practices.

Dr. Scratch automatically scores computational thinking (CT) in Scratch. While promising, these metrics are seldom evaluated in classroom realities. To compensate, we engaged six STEM teachers and 395 middle school students in qualitative classroom research using Dr. Scratch CT4ALL to support game-based learning (GBL). Through observations, field notes, interviews, and quantitative insights, we show how teachers personalize CT rubrics for GBL and how students score with these rubrics, while also documenting their experiences with automated CT metrics. We find that Dr. Scratch’s feedback often goes unheeded, confirming apprehensions from prior work. When CT is a secondary learning goal, students focus on code issues rather than CT points, questioning the usefulness of Dr. Scratch in supporting students’ motivation to learn about CT in GBL. Based on these results, we discuss the need for human-centered design of CT metrics, offering lessons learned and perspectives to improve CT metrics for classroom use.

Since the inception of Generative Adversarial Networks (GANs) more than a decade ago, artists have been facing the rising impact of AI systems on their creative practice and livelihood. As a result, the imposition on creativity has led to adverse consequences for the creative community. In this work, we take a foundational approach to mitigating these impacts by proposing a rights-based framework to recognize creativity as a right. In Part 1, we argue for creativity as a human right, and in Part 2, we argue for creativity as a cultural right. We then combine these arguments to present a Co-creative Rights Enablement (CORE) Design Framework comprising rights, capabilities, and possibilities. This work advances creativity in the context of technical change and contributes a rights-based framework for developing co-creative AI systems that is applicable to co-creative system designers, policymakers, and design researchers.

Dan-sco is a set of soft-sensing wearable interfaces co-designed with dancers and musicians through rehearsal and performance. We developed two interactive garments that translate stretch and pressure into sound while foregrounding material qualities such as fit, friction, and resistance as integral to interaction. Rather than optimizing sensing accuracy, Dan-sco treats sensor ambiguity and mapping drift as creative resources that invite performers to listen, adapt, and co-compose with the system in real time. These frictions act as cues for negotiating timing, intensity, and phrasing during improvisation. This pictorial contributes: (1) a textile-based wearable interface that frames garments as co-performative agents; (2) an improvisational co-design methodology in which mappings emerge through rehearsal; and (3) a design perspective that positions ambiguity and material resistance as generative interactional resources in creative HCI.

FemTech, including apps for fertility, menstruation, and menopause, increasingly shapes how users manage intimate aspects of their health. Yet these apps are often built on opaque commercial models, raising ethical concerns about consent, privacy, and misuse of sensitive health data. While prior work has documented these risks, less is known about how users perceive and negotiate commercial data practices in FemTech apps. We conducted an online survey with 187 participants, combining factorial vignettes with provotypes— interface prototypes designed to provoke reflection— to examine user boundaries and discomforts around FemTech data collection and commercial use. Participants drew sharp distinctions across data types, resisting peripheral data collection and pervasive tracking. Commercial practices were often judged conditionally: tolerated only when functionally relevant. Notably, our provotypes, even under exaggerated transparency, elicited more forgiving responses to commercial practices compared to brief text descriptions in the vignettes. We discuss implications for designing transparent, accountable, and user-aligned FemTech.

Control is a critical yet underexplored concept in human-AI co-creativity and more broadly human-AI collaboration, where AI systems are expected to act as collaborative partners with creative autonomy. Existing frameworks for characterizing control remain limited and often fall short in capturing the tensions and complexities of co-creation dynamics. In this paper, we examine how experts conceptualize control and expect human-AI control dynamics by leveraging a recent framework on characterizing control as our theoretical probe. We conduct a semi-structured focus group study with nine experts in HCI, co-creativity, and AI. Our findings reveal that control is widely viewed as a dynamic, context-dependent construct that should adapt across different phases of co-creation, domains, and levels of trust in AI. Drawing on our findings, we propose a conceptualization of control along with actionable design implications for designing such AI systems. This work contributes to the literature on Human-AI collaboration, Computational Creativity, and HCI, advancing our understanding of control in co-creative human-AI partnerships.

The rise in popularity and value of esports motivates the creation of computational training tools (CTTs) for learning, assessment, and skill gain. While some tools exist commercially, much of the work in the research literature is rarely used outside of a lab, resulting in a lack of knowledge on the challenges involved in real-world integration. In this work, we develop a bespoke CTT for League of Legends—MySkills—based on prior work and deploy it at a professional training academy for three months. Based on two rounds of stakeholder interviews, we uncover insights into users’ perspectives on using CTTs in esports coaching and the challenges inherent in introducing a novel tool into an existing, real-world esports training context. From these results, we connect the domain of esports training technology to existing conversations on translational HCI, challenges in bridging research and practice, and present implications for future work.

Caregivers often experience emotional difficulties and social isolation due to their demanding caregiving duties. Conversational AI has the potential to provide emotional support, yet it lacks effective emotional-regulation support. In this study, we conducted focus groups and semi-structured interviews with mental health professionals and caregivers (n = 17) to explore the potential benefits, challenges, and concerns of users on the applications of conversational AI for caregivers’ emotional support. Our findings suggest that, while current text-based conversational AI is deemed valuable for emotional support, there is a desire to have a more empathic AI, an AI that actively listens, takes cultural, religious, and linguistic context into consideration; and makes humans feel heard. We examined the dimensions of empathic AI in mental health, from authenticity and trust to over-reliance, misuse, and even exacerbating mental health problems, and how this can potentially be addressed to improve caregivers’ well-being.

Lightweight augmented reality (AR) glasses are increasingly entering everyday use, extending interaction design beyond short, isolated sessions. However, most existing gesture vocabularies are inherited from VR headsets or early AR goggles. These systems tend to prioritize recognizer accuracy while overlooking fatigue, sustainability, and social legibility in daily contexts. To address this gap, we collaborated with physical therapists (PTs) to reimagine gesture design for everyday AR, drawing on their expertise in safe and sustainable movement. Through a review of 104 AR applications, we identified 15 common gesture intents and implemented an on-device gesture generator. Ten licensed physical therapists, with an average of 14.8 years of professional experience, then shaped these gesture intents through three iterative stages: unaided gesture performance, PT-guided gesture substitution, and stage-aware card sorting. This work contributes (1) a PT-informed gesture translation method, (2) the Everyday-AR Golden Ergonomic Canvas, and (3) a stage-aware social legibility framework that illustrates how gesture suitability shifts with social readability. Together, these contributions provide a recognizer-agnostic reference framework for designing sustainable and socially coherent gesture vocabularies for lightweight AR glasses.

Augmented reality (AR) games hold promise for rehabilitation, yet most remain confined to laboratory studies with limited clinical uptake. Recent advances in spatial computing, especially lightweight, glasses-form-factor AR, create a timely opportunity to embed rehabilitative play into clinical practice and daily contexts. To investigate this potential, we systematically reviewed 132 applications and conducted playtesting with 14 licensed physical therapists. Our analysis revealed three ways therapists re-authored AR games: co-authored play (reshaping movements, progressions, and difficulty), situated play (adapting across specialties, conditions, and contexts), and dual play (mediating both physical recovery and psychological support). We reframe therapists’ frequent phrase—“It depends”—as a generative design principle. This study contributes a clinical reasoning–based framework and design principles and guidelines for creating personalized, situated forms of play that align with therapists’ everyday workflows and inform future lab-to-clinic translation.

This full-day studio investigates how tangible and soft interfaces can serve as cognitive companions in virtual environments. Rather than optimizing usability or learning outcomes, the workshop explores how perception itself can become a material for reflection and thought. Participants will design and test multisensory interfaces—airflow, heat, softness, and scent-diffusion elements—integrated into a VR escape-room framework. These sensory interventions aim not to enhance performance but to reveal the reciprocity between body and cognition. By treating VR as an embodied instrument rather than a display, Perception VR Studio invites participants to examine how airflow, temperature, and scent modulate attention, emotion, and reflective learning. The goal is to prototype new forms of perceptual scaffolding that make cognition tangible—something one can sense, breathe, and feel.

This in-person studio explores how mixed reality (MR) and biometrics can make intangible emotional states tangible through embodied art practices. We begin with two well-established modalities, clay sculpting and free-form 2D drawing, to ground participants in somatic awareness and manual, reflective expression. Building on this baseline, we introduce an MR prototype that maps physiological signals (e.g., breath, heart rate variability, eye movement dynamics) to visual and spatial parameters (color saturation, pulsing, motion qualities), generating ”3D emotional artifacts.” The full-day program balances theory (somatic psychology, embodied cognition, expressive biosignals), hands-on making, and comparative reflection to interrogate what analog and digital modalities respectively afford for awareness, expression, and meaning-making. Participants will (1) experience and compare analog and MR-based journaling of emotion; (2) prototype and critique mappings from biosignals to visual/spatial feedback; and (3) articulate design principles for trauma-informed, hybrid workflows that amplify interoceptive literacy without overwhelming the user. The expected contributions include a shared design vocabulary for biometric expressivity, a set of generative constraints for future TEI work on emotional archiving, and actionable insights into when automated translation supports or hinders embodied connection.

Figure 1: Clusters of countries with similar cultural attitudes based on two different sets of metrics and algorithms.1a (left) uses mean values and hierarchical clustering.1b (right) uses cosine similarity and k-means clustering.

The rapid shift to remote work has exposed limitations in traditional tools for 3D data visualization, particularly in medical training. We present HoloViz Office, a portable Mixed Reality (MR) workspace that enables immersive 3D medical data visualization independent of physical location. Unlike Virtual Reality (VR) solutions that isolate users, our MR approach using HoloLens preserves situational awareness while providing intuitive interaction with complex medical datasets. We demonstrate the system through comparative brain analysis for Cerebral Small Vessel Disease (CSVD) and dynamic human anatomy exploration. Evaluation with 10 participants confirms that HoloViz Office provides location-independent, convenient, and immersive visualization capabilities, contributing to effective remote collaboration tools in medical education.

FemTech is an emerging industry offering products, software, and services to support women’s health and well-being. Within FemTech, mobile health applications (mHealth apps) are popular for managing menstruation, fertility, pregnancy, and menopause. Yet, these apps expose users to deceptive and misleading practices, which can be characterized as Dark Patterns in user experience (or dark UX). Dark UX in commercial FemTech mHealth apps is underexplored, leaving a critical gap in understanding how deceptive patterns manifest in intimate health contexts, the harms they cause, and how to address them. We crowd-source and thematically analyze user accounts of dark UX through user reviews from sixteen systematically selected FemTech mHealth apps. User-reported accounts of dark UX in FemTech mHealth apps reveal several problematic design patterns, which emphasize risks for minors and the need for more transparent design of FemTech mHealth apps. Based on our results, we outline recommendations for enhancing ethical UX design and furthering regulatory action in FemTech.

To enable personalized augmented reality (AR) systems that adapt in real time to a user’s level of expertise, it is first necessary to assess individual skill levels during task performance. In this article, we introduce a novel data-driven approach for the multidimensional assessment of expertise in AR-guided psychomotor tasks. We evaluate users’ performance against multiple indicators by employing pairwise correlation and principal component analysis (PCA) on multimodal data collected from AR devices and wearable sensors—including hand tracking, galvanic skin response (GSR), and gaze. The results show that objective measures—such as visual scanning efficiency, stress indicators, inspection accuracy, and hand dexterity—provide valuable insights into multiple indicators of expertise and skill acquisition. These findings validate the potential for AR systems to adapt dynamically to users’ expertise levels based on observable data. This approach offers a promising direction for enhancing the effectiveness of AR-based training systems in high-stakes domains such as manufacturing, aviation, and medicine, which can benefit from real-time, multimodal assessment of expertise for adaptive AR guidance and assistance.

Increasing numbers of people represent themselves in legal disputes—known as pro se litigants. Many lack the skills, experience, or knowledge to navigate legal proceedings without a lawyer, resulting in limited access to justice. Serious games may provide an effective, interactive, and engaging way of educating pro se litigants about the law and enabling their access to justice. Through participatory design with legal experts and an authoring tool, we co-designed RePresent, a serious game that helps individuals with limited access to legal support prepare for pro se litigation. A total of 965 people played RePresent, and 149 provided feedback on their player experience. Results show that RePresent was engaging and valuable for learning about the law and pro se litigation. Our work highlights avenues for co-design methodologies with co-creative authoring tools that facilitate serious game design, contributing a potentially scalable solution to enable access to justice via co-authored serious games.

Assessing speech disorders in early childhood is challenging, and Speech-Language Pathologists (SLPs) play a key role in addressing such a challenge. However, tools that support speech assessment are often not child-friendly, and SLPs struggle to keep young patients engaged. To compensate, we introduce ArticuMotion, a child-friendly app that supports the assessment of speech disorders while engaging children in gamified experiences. We use participatory design to co-create ArticuMotion with SLPs and test the resulting product in a user study with nine preschool children. ArticuMotion has promise as a gamified assessment for motor speech disorders and shows a potential avenue for designing clinical tools that are useful while being child-friendly.

Gamification and virtual reality (VR) are increasingly being explored for their potential to enhance mindful practices and well-being. We further explore the potential of gamification and VR for breath awareness and mindfulness, and contribute Stairway to Heaven, a VR artifact that combines gamification with respiratory sensor biofeedback to cultivate mindful awareness of breathing. In our mixed-method study with 21 participants, we evaluated the usability and effectiveness of our artifact in promoting breathing frequencies between 4 and 10 breaths per minute (BPM). We integrate breath-driven teleportation as a virtual locomotion technique (VLT) using respiratory biofeedback to gamify progression through a virtual wilderness. Additionally, we supplement our design with a mindfulness audio guide. The results of our user study showcase the potential of combining actionable gamification and VR, guided mindfulness, and breath-driven VLT to foster slow breathing self-regulation successfully.

This paper introduces a scoping review of 55 articles to explore GPT’s potential for games, offering researchers a comprehensive understanding of the current applications and identifying both emerging trends and unexplored areas. We identify five key applications of GPT in current game research: procedural content generation, mixed-initiative game design, mixed-initiative gameplay, playing games, and game user research. Drawing from insights in each of these application areas, we propose directions for future research in each one. This review aims to lay the groundwork by illustrating the state of the art for innovative GPT applications in games, promising to enrich game development and enhance player experiences with cutting-edge AI innovations.

Mixed-initiative co-creative storytelling games have existed for some time as a way to merge storytelling with play. However, modern mixed-initiative co-creative storytelling games predominantly prioritize story creation over gameplay mechanics, which might not resonate with all players. As such, there is untapped potential for creating mixed-initiative games with more complex mechanics in which players can engage with both co-creation and gameplay goals. To explore the potential of more prominent gameplay in mixed-initiative co-creative storytelling games, we created Snake Story, a variation of the classic Snake game featuring a human-AI co-writing element. To explore how players interact with the mixed-initiative game, we conducted a qualitative playtest with 11 participants. Analysis of both think-aloud and interview data revealed that players’ strategies and experiences were affected by their perception of Snake Story as either a collaborative tool, a traditional game, or a combination of both. Based on these findings, we present design considerations for future development in mixed-initiative co-creative gaming.

In this Work-in-Progress Research Paper, we explore students' emotions and motivations in a game-based learning (GBL) environment. GBL has been shown to be effective in improving students' cognitive and non-cognitive learning outcomes, including emotions and motivations. In this study, we focus on the Cone Penetration Testing module of GeoExplorer's virtual learning environment, which has been shown to enable civil engineering undergraduates to develop key competencies necessary to problem-solve and make critical on-site decisions. This paper examines the impacts of GeoExplorer on students' emotions and motivations by asking: (1) How, if at all, do students' self-reported emotions shift through experiencing Geo-Explorer, and how does GeoExplorer contribute to these shifts, if at all? and (2) What is the connection between students' self-reported emotions and their self-reported motivational attitudes, both before and after they engage with GeoExplorer? Responses from 60 participants about their emotions and motivations pre-and post-engagement with GeoExplorer were analyzed. Inductive methods, including open coding and the constant comparative approach, were used to analyze the following emotions-based survey item: Thinking about your experience over the past week in the course, what particular emotions and/or thoughts about the course both inside and outside the classroom stand out for you? Deductive analytical practices, including closed coding, were used to analyze the following motivations-based survey item: What factors contributed to your motivation over the past week in the course? Consistent with literature, our preliminary findings indicate an interrelationship between GBL experiences and shifts in students' reported emotions. We also determine a link between these emotional shifts and motivational changes from pre- to post-engagement with GeoExplorer. It is the interplay of these three elements - GBL experience, student emotions, and student motivations - that is at the center of this work.

This Work-in-Progress Research Paper centers on game-based learning and its impact on student motivation as a function of their self-defined demographic factors. Specifically, this work investigates the game-based learning experience of civil engineering undergraduates engaged with the Cone Penetration Testing module of GeoExplorer, a virtual learning environment that simulates in-field learning about the structural properties of soil, providing students with the opportunity to practice competencies and mindsets associated with real-world data collection and analyses. In this paper, we explore the impacts of GeoExplorer on student motivation by addressing the following research questions: (1) How, if at all, do students experience identifiable motivational shifts over the course of their engagement with GeoExplorer? (2) What is the interplay between engagement with GeoExplorer, demographic factors, including students' racial, ethnic, and gender identities, and student motivation? and (3) How can game-based learning environments best address the needs of students with different motivational attitudes? Preliminary analyses of 60 response pairs to the pre- and the post-GeoExplorer engagement surveys from 2021 were analyzed. Deductive analytical practices using ‘a priori’ qualitative coding, based on Self-Determination Theory (SDT) of motivation, were used to examine the following survey item: What factors contributed to your motivation over the past week in the course? Please give specific examples of activities, interactions, emotions, thoughts, etc. that may have contributed to your motivation. While our early findings indicate distinct differences in students' motivational outcomes between the pre- and post-survey, no clear patterns in motivational shifts are identified across the board, suggesting that the GeoExplorer experience may have impacted each student's motivation uniquely. However, patterns emerged between students' pre-survey motivation types, based on demographic factors. Additionally, unique shifts in motivational attitudes between the pre- and post-surveys were found to occur within each demographic grouping. These findings and challenges in performing this study are further discussed with a proposal for possible future directions.

Commercial pregnancy apps are becoming popular in mobile health and integral to individuals' health management ecosystems. For that, they can complement medical advice and be conveniently used for ubiquitous tracking of pregnancy. Besides their functional and medical purpose, they may elicit subjective, personal, and intimate experiences that are equally relevant to users. Yet, these qualitative aspects of experiencing pregnancy apps remain under-researched. An inquiry into those qualitative aspects may help advance the design of pregnancy apps for improved user embodiment, engagement, and experience. Here, we qualitatively inquire about experiences with six popular pregnancy apps through 4,000+ online reviews. Our findings reveal that pregnancy apps are more than mere trackers and can impact pregnancy experiences, either positively or negatively, based on their design features. Further, reviews pointed to a neglect of family, friends, and relatives in the apps' design, which users found often problematic. To counter these shortcomings, we outline avenues for improving the design of pregnancy apps beyond usability and medical outcomes and call for enhancing their user experience through more sensitive, user-centered, and inclusive design.

The COVID-19 pandemic, the rise of telehealth and virtual medicine, and the increased accessibility of commercial VR headsets have created opportunities to revitalize the physical therapy field and introduce more innovative tools to complement traditional practice. Researchers are actively exploring the intersection of VR, exergames, and gamified exercise to create new products that can improve both provider efficiency and patient care. This study builds on that momentum by delving into the perspectives of practitioners. We interviewed eight physical therapists after letting them experience a state-of-the-art VR exercise product (i.e., Supernatural VR ). Participants loved the game and were most optimistic about the mental benefits of using VR exergames alongside traditional PT, but cautioned the lack of feedback and expert guidance that could lead to misuse and further injury. Regarding the future integration of VR and PT, participants were cautiously optimistic and offered design feedback and recommendations, with warnings regarding the potential barriers to innovating clinical practice.

Studying mental models has recently received more attention, aiming to understand the cognitive aspects of human-computer interaction. However, there is not enough research on the elicitation of mental models in complex dynamic systems. We present Thought Bubbles as an approach for eliciting mental models and an avenue for understanding players’ mental model development in interactive virtual environments. We demonstrate the use of Thought Bubbles in two experimental studies involving 250 participants playing a supply chain game. In our analyses, we rely on Situation Awareness (SA) levels, including perception, comprehension, and projection, and show how experimental manipulations such as disruptions and information sharing shape players’ mental models and drive their decisions depending on their behavioral profile. Our results provide evidence for the use of thought bubbles in uncovering cognitive aspects of behavior by indicating how disruption location and availability of information affect people’s mental model development and influence their decisions.

Although the world’s oceans play a critical role in human well-being, they have not been a primary focus of the sustainable HCI (SHCI) community to date. In this paper, we present a scoping review to show how concerns with the oceans are threaded throughout the broader SHCI literature and to find new research opportunities. We identify several themes that could benefit from focused SHCI research, including marine food sources, culture and coastal communities, ocean conservation, and marine climate change impacts and adaptation strategies. Finally, we discuss opportunities for further work on marine human-natural systems research in SHCI and interdisciplinary collaboration with marine science and coastal communities.

Researchers and designers have considered how gamification can make education more effective. In this paper, the role of player-character relationships is considered. Designers can use various factors to get users attached to their character, including the feeling of responsibility. In this paper, we investigate whether feeling responsible for one’s in-game character improved the efficacy of a gamified educational experience. An educational, gamified story called Humanatees was designed to incite the protégé effect to see if participants learned more when they felt responsible for their character. The results suggest that participants in the experimental condition achieved significantly higher learning gains, providing evidence for the protégé effect. However, how this effect is established is more complex than feeling responsible. Future work should shed more light on the dynamics behind the protégé effect.

Students are often tasked in engaging with activities where they have to learn skills that are tangential to the learning outcomes of a course, such as learning a new software. The issue is that instructors may not have the time or the expertise to help students with such tangential learning. In this paper, we explore how AI-generated feedback can provide assistance. Specifically, we study this technology in the context of a constructionist curriculum where students learn about experimental research through the creation of a gamified experiment. The AI-generated feedback gives a formative assessment on the narrative design of student-designed gamified experiments, which is important to create an engaging experience. We find that students critically engaged with the feedback, but that responses varied among students. We discuss the implications for AI-generated feedback systems for tangential learning.

Many human-AI authoring tools are used in a playful way, while being primarily designed for task-achievement—not playfulness. We argue that playfulness is an important yet overlooked factor of user behaviour and experience when interacting with such tools. Motivating and rewarding playfulness as an exploratory, task-agnostic, open, and subversive attitude can support the satisfaction of more diverse user goals, and have a strong, positive effect on the user experience, the emerging human-AI interaction, and the resulting artefact. In this paper, we motivate the importance of playfulness as user experience in human-AI authoring tools, and propose concrete strategies to design for playfulness in the human user through UI design, in the AI through algorithms, or through interventions to their dialog. We conclude with an outlook of the research agenda.

This Work-in-Progress research paper focuses on the game-based learning environment of GeoExplorer, a digital learning tool that simulates Cone Penetration Testing (CPT) – a field test that civil engineers conduct to understand soil properties. This paper aims to investigate the gendered patterns of student interaction with GeoExplorer, if any, and the associated gendered patterns of content comprehension and self-efficacy related to one's ability to pursue CPT in the field, if any. As such, we ask: (1) How, if at all, does gender affect a student's belief in their ability to apply skills gained through engagement with GeoExplorer in real world environments? and (2) How, if at all, do prior gaming experience and gender impact the GeoExplorer play experience? Four open-ended semi-structured interviews regarding students' experience with gameplay, gender identity, and learning were analyzed. Preliminary analyses used narrative and grounded theory approaches. Our early findings indicate a lack of student insight on the role of their gender identity on their gameplay experience and the presence of a gendered impact of gameplay on student learning and self-efficacy in carrying out CPT in the field.

Understanding decision-making in dynamic and complex settings is a challenge yet essential for preventing, mitigating, and responding to adverse events (e.g., disasters, financial crises). Simulation games have shown promise to advance our understanding of decision-making in such settings. However, an open question remains on how we extract useful information from these games. We contribute an approach to model human-simulation interaction by leveraging existing methods to characterize: (1) system states of dynamic simulation environments (with Principal Component Analysis), (2) behavioral responses from human interaction with simulation (with Hidden Markov Models), and (3) behavioral responses across system states (with Sequence Analysis). We demonstrate this approach with our game simulating drug shortages in a supply chain context. Results from our experimental study with 135 participants show different player types (hoarders, reactors, followers), how behavior changes in different system states, and how sharing information impacts behavior. We discuss how our findings challenge existing literature.

Traditionally, game designers have driven the development process of psychotherapeutic games with psychotherapists playing a consultant role. In contrast, our study explores psychotherapists independently designing digitally gamified psychotherapy. Our workshop participants consisted of six psychotherapists who created gamified digital therapies with the design prompt of anxiety disorder. We analyze the resulting six prototypes from a game design and psychotherapy perspective. We present insights into strategies for digitally gamifying therapy and identify challenges and opportunities for the future of gamified psychotherapy, grounded in the experiences of psychotherapists designing such therapies. This study also reflects on the use of user-friendly development tools for independently curating gamified digital therapies, especially by non-technical users such as psychotherapists.

Increasingly, videogame designers harness game usability techniques to inform design decisions and reduce costs, but advanced techniques are not commonplace yet—perhaps due to failing to see their benefits or a lack of expertise and facilities. In this paper, we present a case study that demonstrates the value of using eye-tracking to help guide the design process of a narrative game by an independent game studio. The designers of this studio had defined four options of how text should be presented to players (Horizontal, Vertical, Subtitle, and Messenger), and were deadlocked on what to choose. As part of a collaboration with a university, a within-subjects eye-tracking study was conducted with 15 participants to evaluate the options. Combining the eye-tracking data with stated user preferences, designers reached a consensus that the game in question benefits from strategies derived from messenger on smart-phone interaction design—later confirmed with statistical analysis of fixation transition on elements of interest on screen. The use of eye-tracking broke the deadlock and helped inform a final design decision, as demonstrated by a decision making impact analysis that describes the design process. The paper ends with a reflection on the applicability of this academia-industry model to other contexts.

Physical and digital games are increasingly used and acknowledged for their cognitive effects, but there is a lack of understanding of how a game’s medium directly impacts cognitive functions. This exploratory case study presents a novel method of using mobile eye-tracking to compare working memory and visual attention between physical and digital versions of the board game Ticket to Ride in natural settings. For visual attention, no significant differences between the versions were measured. However, results indicate that fixation duration is significantly greater while playing the digital version versus the physical version, suggesting the counter-intuitive finding that the digital version increases working memory load. The study design, with evidence for high ecological validity, and its findings provide a foundation for future studies for assessing cognitive processing of game media. This work also provides inspiration for making game media considerations for obtaining specific cognitive experiences and increasing accessibility.

Increasingly, PCG systems are developed to help game designers create content for their games. However, game designers have limited control over the content current PCG systems generate. We investigate an interaction mechanism non-technical users can use to control generative grammars without the need for understanding the grammar’s rules. To demonstrate this control mechanism, we present a system, built using a probabilistic graph grammar, that allows designers to specify their desired generative space by defining a region on an expressive range plot. We ran a user study with game design students to assess its viability. Our findings suggest that designers have an easier time controlling the grammar using this mechanism over manually interacting with grammars rules.

Geotechnical engineers work with a complex material. Unlike steel or concrete, soil is heterogeneous, and its properties are difficult to predict. As such, judgment plays a significant role in geotechnical engineering practice and the design process is unavoidably iterative. This WIP starts with geotechnical engineering as the origin for defining engineering judgment, but the emerging theory can service all engineering decision making. The process through which aspiring engineers practice engineering judgment in their education remains a research area in its adolescence. Members of industry posit that experience is essential for competent engineering practice, but traditional engineering education practices provide few opportunities to acquire such experience. The GeoExplorer project is a mixed reality "mock internship" that allows students to get preliminary experience with field testing through a mixed-reality game-based learning environment. The CPT module of GeoExplorer was designed to both develop and measure gains in engineering judgment. The importance of defining engineering judgment with respect to engineering education standards became clear when designing this module to serve undergraduate engineering classrooms.

This Work-in-Progress Research paper focuses on digital game-based learning (DGBL), which refers to the use of a virtual environment to support students’ learning. In this exploratory study, we examine how students engage with GeoExplorer, a digital game-based learning environment that simulates Cone-Penetration Testing (CPT), an on-site test used in geotechnical engineering to investigate soil properties that students typically don’t have access to. In GeoExplorer’s CPT activity, students participate in a virtual internship in which they examine several sites with varied types of soil. This paper investigates DGBL environments by leveraging Self-Determination Theory (SDT) to ask the following research questions: (1) How do "freedom" and autonomy within GeoExplorer encourage students’ new emergent learning strategies? and (2) How do emergent learning strategies in GeoExplorer support students’ confidence as they self-guide their learning? Ten open-ended semi-structured interviews were performed with civil engineering students from three U.S.-based institutions. The data are analyzed using narrative analysis and a grounded theory approach. Our preliminary findings indicate that, while GeoExplorer is intended as a complement to in-person learning, it serves both as a complement and supplement to the online learning that helps to engage students during the pandemic. Students share that a felt sense of "freedom" within GeoExplorer encourages them to engage in different emergent learning strategies, such as repetition and trial and error. Students also describe that these emergent learning strategies promote knowledge retention and understanding, and further support their confidence in performing CPT. Our preliminary findings provide opportunities for students to practice autonomy and develop competency – two out of three basic psychological needs in SDT – in their educational processes.

Optimizing operations on critical infrastructure networks is key to reducing the impact of disruptive events. In this paper, we explore the potential of having humans and algorithms work together to address this difficult task. For this purpose, we use a gamified experiment to build and assess this potential in the context of the debris collection problem (i.e., “gamesploring”). We developed a digital game where players can request the help of the computer while facing a multi-objective problem of assigning contractors to road segments for clearing debris in a disaster area. Through a within-subjects experimental study, we assessed how players optimized under various circumstances (e.g., initial solution vs. from scratch) compared to the computer on its own. The results are both surprising as well as insightful: they suggest that human-machine collaboration is indeed beneficial but also that more work is needed on how to appropriately guide this form of collaboration.

Unsupervised physical rehabilitation traditionally has used motion tracking to determine correct exercise execution. However, motion tracking is not representative of the assessment of physical therapists, which focus on muscle engagement. In this paper, we investigate if monitoring and visualizing muscle engagement during unsupervised physical rehabilitation improves the execution accuracy of therapeutic exercises by showing users whether they target the right muscle groups. To accomplish this, we use wearable electrical impedance tomography (EIT) to monitor muscle engagement and visualize the current state on a virtual muscle-skeleton avatar. We use additional optical motion tracking to also monitor the user’s movement. We conducted a user study with 10 participants that compares exercise execution while seeing muscle + motion data vs. motion data only, and also presented the recorded data to a group of physical therapists for post-rehabilitation analysis. The results indicate that monitoring and visualizing muscle engagement can improve both the therapeutic exercise accuracy during rehabilitation, and post-rehabilitation evaluation for physical therapists.

Questionnaires are vital in games user research (GUR) to assess player experience (PX). However, having too many questions in surveys prevents wider uptake among GUR professionals because of games' rapid production cycles. To address this issue, we present the miniPXI---an eleven-item measure of the popular Player Experience Inventory (PXI)---providing single items for each of its eleven constructs. To develop the scale and examine its reliability and validity, we present three studies, conducted with 15 experts and 628 digital game players across continents. In the first survey study (n=366, 15 experts), single items were selected. In a second survey study (n=232), we explored reliability and validity of the single-item scale. Participants completed both full and single-item (SI) variants in three days. In the last study (n=30), we established the validity and sensitivity via an experimental evaluation of two games. The results are nuanced; SI reliability estimates for PXI constructs range from .51 to .83 with an average of .68, we could confirm the validity for nine constructs. We conclude that the miniPXI can be a valuable tool for PX evaluations where a longer measure is not feasible, and provide practical considerations for its use.

Queerness can help redefine interactive technologies and re-conceptualize their design beyond cisheteronormativity. Recently, games have emerged as promising avenues for exploring queerness. In this research-through-design (RtD) effort, we advance queer explorations in games by leveraging queer temporality to evoke and explore feelings of alienation and isolation in a horror game called You're Going To Be Late. We further engage with queer individuals who playtest our game and participate in focus groups to discuss how queer they felt our game was and what they regard as queer game mechanics. Participants had varying experiences while playing our game, ranging from serendipity and wonder to alienation and confusion. The focus groups described queer game mechanics as queer modes of play and often tapped into gender representation. Finally, we show how including and amplifying queer voices in discursive efforts around interactive technology design has implications for transformative and critical reflections that can broadly benefit game design and HCI research.

Understanding players' mental models are crucial for game designers who wish to successfully integrate player-AI interactions into their game. However, game designers face the difficult challenge of anticipating how players model these AI agents during gameplay and how they may change their mental models with experience. In this work, we conduct a qualitative study to examine how a pair of players develop mental models of an adversarial AI player during gameplay in the multiplayer drawing game iNNk. We conducted ten gameplay sessions in which two players (n = 20, 10 pairs) worked together to defeat an AI player. As a result of our analysis, we uncovered two dominant dimensions that describe players' mental model development (i.e., focus and style). The first dimension describes the focus of development which refers to what players pay attention to for the development of their mental model (i.e., top-down vs. bottom-up focus). The second dimension describes the differences in the style of development, which refers to how players integrate new information into their mental model (i.e., systematic vs. reactive style). In our preliminary framework, we further note how players process a change when a discrepancy occurs, which we observed occur through comparisons (i.e., compare to other systems, compare to gameplay, compare to self). We offer these results as a preliminary framework for player mental model development to help game designers anticipate how different players may model adversarial AI players during gameplay.

COVID-19 has heavily impacted our lives. To date, the ongoing pandemic continues to cause dramatic societal changes and raises shared sentiments of uncertainty for our future. As such, however, COVID-19 provides opportunities to explore futures through speculative research. Here, we gamify the story completion method (SCM) to explore futures post-COVID and ask 37 participants to play a day in the life of Sal in a post-COVID future. The game asks participants to describe what Sal sees, hears, or does throughout a day based on multiple story stems. Our analysis reveals narratives of post-COVID futures as business as usual, back to basics, or everyday chaos. Notably, these narratives raise concerns about privacy loss and increased militarization, but also envision futures post-COVID that reclaim stronger bond with nature and family. We discuss the lessons learned from gamifying the SCM and the temporal implications of performing speculative research during evolving dramatic events.

Searching for relative mobile user interface (UI) design examples can aid interface designers in gaining inspiration and comparing design alternatives. However, finding such design examples is challenging, especially as current search systems rely on only text-based queries and do not consider the UI structure and content into account. This paper introduces VINS, a visual search framework, that takes as input a UI image (wireframe, high-fidelity) and retrieves visually similar design examples. We first survey interface designers to better understand their example finding process. We then develop a large-scale UI dataset that provides an accurate specification of the interface’s view hierarchy (i.e., all the UI components and their specific location). By utilizing this dataset, we propose an object-detection based image retrieval framework that models the UI context and hierarchical structure. The framework achieves a mean Average Precision of 76.39% for the UI detection and high performance in querying similar UI designs.

Esports is a rapidly growing industry, generating interest from research disciplines including marketing, social sciences, and human-computer interaction. Despite its continued growth, there is a lack of studies surrounding the health of esports players. Previous work on the subject is limited, as research has only recently begun to explore the potential factors affecting physical and psychological wellness. Using an exploratory mixed-methods approach, a series of semi-structured interviews (n = 10) and an online survey (n = 68) were used to identify the biggest health concerns among esports players. The results demonstrate a better understanding of issues regarding physical and psychological wellness according to the players. Moving forward, we suggest the HCI community adapts mindfulness, ergonomics, and social-emotional learning as methods for supporting player’s health concerns.

Esports are rapidly growing within academia. In HCI, earlier work explored how problematic behaviors emerging from gender biases (e.g., toxicity) negatively impact female participation in esports. Here, we further explore gender biases in esports by interviewing 19 self-identified female and male professional gamers and event organizers. We inquire our interviewees about personal experiences with gender biases in esports and their perspective on how these biases impact participation, inclusivity, and career prospects. Our interviewees see gender biases in esports as a consequence of stereotypical gender roles in gaming tout-court (e.g., girls do not like violence, boys are competitive by nature). The rationale for separating male and female in esports, however, seems rooted in the need for female gamers to create role-models and grow in self-confidence. We scrutinize the considerations emerging from our interviews under a Feminist HCI lens and discuss how HCI research can help design equitable environments in esports.

The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. Through a systematic survey of neural network games (n = 38), we identified the dominant interaction metaphors and AI interaction patterns in these games. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems. Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction.

This Work-in-Progress paper focuses on the qualitative aspects of a larger mixed-methods study about GeoExplorer, a game-based learning aspect of a mixed reality educational environment where students participate in a mock internship with the goal to complete a geotechnical mission involving Cone Penetration Testing, a civil engineering field technique students traditionally get very little exposure to due to its complexity and cost. This work seeks to understand how mixed reality learning environments, specifically game-based learning, support (i) creation of individualized hands-on learning opportunities, particularly during the pandemic-driven remote learning paradigm, and (ii) students' development along various noncognitive axes - confidence and motivation. Methods of narrative analyses and grounded theory are used to identify emergent themes in interviews with 10 undergraduate civil engineering students who experienced GeoExplorer in their required geotechnical engineering courses. Our preliminary findings indicate that the GeoExplorer activity was perceived by students as a novel learning experience, particularly welcomed in a time of remote learning, that motivates them to engage more with content and creates individualized hands-on experiences. Students describe how the activity affects their perceived confidence, often gendered, regarding their ability to perform civil engineering fieldwork. Further analyses of these findings may shed light on the ways in which mixed reality learning environments support equitable learning opportunities for all students.

Is it possible to detect toxicity in games just by observing in-game behavior? If so, what are the behavioral factors that will help machine learning to discover the unknown relationship between gameplay and toxic behavior? In this initial study, we examine whether it is possible to predict toxicity in the MOBA gameFor Honor by observing in-game behavior for players that have been labeled as toxic (i.e. players that have been sanctioned by Ubisoft community managers). We test our hypothesis of detecting toxicity through gameplay with a dataset of almost 1,800 sanctioned players, and comparing these sanctioned players with unsanctioned players. Sanctioned players are defined by their toxic action type (offensive behavior vs. unfair advantage) and degree of severity (warned vs. banned). Our findings, based on supervised learning with random forests, suggest that it is not only possible to behaviorally distinguish sanctioned from unsanctioned players based on selected features of gameplay; it is also possible to predict both the sanction severity (warned vs. banned) and the sanction type (offensive behavior vs. unfair advantage). In particular, all random forest models predict toxicity, its severity, and type, with an accuracy of at least 82%, on average, on unseen players. This research shows that observing in-game behavior can support the work of community managers in moderating and possibly containing the burden of toxic behavior.

The concept of flow is used extensively in HCI, video games, and many other fields, but its prevalent definition is conceptually vague and alternative interpretations have contributed to ambiguity in the literature. To address this, we use cognitive science theory to expose inconsistencies in flow's prevalent definition, and introduce fuse, a concept related to flow but consistent with cognitive science, and defined as the "fusion of activity-related sensory stimuli and awareness''. Based on this definition, we develop a preliminary model that hypothesizes fuse's underlying cognitive processes. To illustrate the model's practical value, we derive a set of design heuristics that we exemplify in the context of video games. Together, the fuse definition, model and design heuristics form our theoretical framework, and are a product of rethinking flow from a cognitive perspective with the purpose of improving conceptual clarity and theoretical robustness in the literature.

Agent-based simulations are widely used for modeling human behavior in various contexts. However, such simulations may oversimplify human decision-making. We propose the use of Gamettes to extract rich data on human decision-making and help in improving the human behavioral aspects of models underlying agent-based simulations. We show how Gamettes are designed and provide empirical validation for using Gamettes in an experimental supply chain setting to study human decision-making. Our results show that Gamettes are successful in capturing the expected behaviors and patterns in supply chain decisions, and, thus, we find evidence for the capability of Gamettes to inform behavioral models.

Game design is increasingly used in modern education to foster Computational Thinking (CT). Yet, it is unclear how and if the game genre of student-designed games impact CT and programming. We explore how game genre impacts CT development and programming routines in Scratch games designed by 8th-grade students using a metrics-based approach (i.e., Dr. Scratch). Our findings show that designing particular games (e.g., action, storytelling) impact CT and programming development. We observe, for instance, that CT skills develop and consolidate fast, after which students can focus on aspects more specific to game design. Based on the results, we suggest that researchers and educators in constructionist learning consider the impact of game genre when designing game-based curricula for the learning of programming and CT.

Sex Robots are no longer science fiction and may soon be-come widespread. While much discussion has developed in academia on their moral and social impact, sex robots have yet to be examined from a critical design perspective and are under-explored in HCI. We use the Story Completion Method(SCM) to explore commonplace assumptions around futures with sex robots and discuss those from a critical design perspective. Thirty five participants completed a story stem of a human encountering a sex robot or vice-versa. Through thematic analysis, we show narratives of consumerist relation-ships between humans and sex robots, stories that describe sex robots as highly-efficient sex workers that (out)perform humans in routinal sex activities, and narratives that explore sex robots as empathetic and sentient beings. Our participant-created stories both reinforce and challenge established norms of sex robots and raise questions that concern responsible design and ethics in HCI. Finally, we show opportunities and limitations of using multiple-perspective story stems in SCM

Many successful games rely heavily on data analytics to understand players and inform design. Popular methodologies focus on machine learning and statistical analysis of aggregated data. While effective in extracting information regarding player action, much of the context regarding when and how those actions occurred is lost. Qualitative methods allow researchers to examine context and derive meaningful explanations about the goals and motivations behind player behavior, but are difficult to scale. In this paper, we build on previous work by combining two existing methodologies: Interactive Behavior Analytics (IBA) [2] and sequence analysis (SA), in order to create a novel, mixed methods, human-in-the-loop data analysis methodology that uses behavioral labels and visualizations to allow analysts to examine player behavior in a way that is context sensitive, scalable, and generalizable. We present the methodology along with a case study demonstrating how it can be used to analyze behavioral patterns of teamwork in the popular multiplayer game Defense of the Ancients 2 (DotA 2).

Game design is emerging in contemporary education, especially in constructionist curricula for student-centered and discovery learning. While previous work focused on assessing how complementary skills (e.g., computational thinking) develop through game design, few have attempted to assess the very games designed by students. Here, we show preliminary results on using the Triadic Game Design (TGD) model to assess the elements that may constitute a ”balanced” student-designed game in the context of constructionist learning. Our results show that TGD is viable to carry out such assessment. We conclude by discussing the limitations of our approach and propose directions for future work.

Game-based learning has long been heralded as a promise to transform education into a more engaging and experiential paradigm. Its implementation in engineering education has thus far shown that promise, yet more evidence is needed for widespread adoption. Our work focuses on issues of equity, inclusivity, and diversity, thus surfacing the question of whether game-based learning benefits each and every student. To date, these issues have not received much attention in game-based learning educational research landscape, despite its poignancy, given today's global social climate and the systematic lack of equity and diversity in engineering education. With a focus on gender and race/ethnicity we reflect on the outcomes of implementing game-based learning environment in undergraduate geotechnical engineering courses (n = 362). We find evidence that game-based learning supports positive outcomes and provides an equitable learning environment. However, we also note several challenges that are further discussed.

Computational thinking (CT) is key to digital literacy and helps develop problem-solving skills, which are fundamental in modern school. As game design shows potential for teaching CT, metrics like Dr. Scratch emerge that help scholars systematically assess the CT of student-designed games, particularly with Scratch. Compared to other CT metrics, Dr. Scratch scores the CT of Scratch projects automatically and can be used to describe CT development. However, previous research using Dr. Scratch summatively assessed CT, but did not look at CT development. We use Dr. Scratch to assess the CT development of Scratch games designed by 8th-grade students in STEM curricula. We show how CT proficiency in student-designed games develops differently in each CT dimension, where parallelism, synchronization, and logic develop proficiently, while developing abstraction seems hard. We discuss insights into game-based CT development for STEM, and suggest improvements for metric-based CT assessment.

In a wide range of social networks, people's behavior is influenced by social contagion: we do what our network does. Networks often feature particularly influential individuals, commonly called "influencers." Existing work suggests that in-game social networks in online games are similar to real-life social networks in many respects. However, we do not know whether there are in-game equivalents to influencers. We therefore applied standard social network features used to identify influencers to the online multiplayer shooter Tom Clancy's The Division. Results show that network feature-defined influencers had indeed an outsized impact on playtime and social play of players joining their in-game network.

Gamification and serious games have the capacity to increase engagement in often non-engaging contexts such as a test. In this study, we gamified a psychological test, the Thematic Apperception Test (TAT), with the player motivation concepts of achievement, exploration, and social interactions. We used the platform Study-Crafter to implement our games and ran a study to test the effectiveness of this gamified psychological assessment. All participants completed both the standard version of the TAT and our gamified versions and rated their experience in each setting through self-reports. Our results show that the gamified versions of the TAT provided a more enjoyable and motivating experience than the standard version. We conclude that gamifying psychological tests has potential for increasing motivation in psychological assessments while questions of validity remain to be addressed.

Teamwork is becoming an important element of gameplay. Many games nowadays incorporate teamwork in one form or another. As a result, many game researchers have investigated aspects of teamwork. However, we have yet to develop effective surveys to gauge how people strategize or problem solve together within different games, and how such games enable these activities. In this paper, we propose the Teamwork and Adaptation in Games (TAG) survey. We developed this survey using theories of team adaptation, a process of how teams work together and adapt their strategies to changes in environments. Through three separate studies, the TAG survey was used to gauge team adaptation. Our results show that the TAG survey is an effective instrument allowing us to gauge team adaptation in different game environments. We further found that the instrument can be effectively used to compare games.

Games have been designed to foster children's computational thinking (CT) skills, which is widely recognized as a key ability that should be mastered in this computing-driven era. Because learning coding skills is cognitively difficult and uninvolving for novices and it is recommended to learn CT concepts first, many educational games have been developed to teach CT in a non-coding environment. However, despite the breadth of such games and the demand for it, little is known about how to design such games or why such games are designed in a particular way. To address this gap, this paper presents a design framework that describes the building blocks for developing (non-coding) gameplay elements in games teaching CT. The framework focuses specifically on computational problem-solving because this is where games can specifically aid with. We illustrate the design framework through LittleWorld, a mobile game that teaches CT concepts through insects.

Interactivity and player experience are inextricably entwined with the creation of compelling narratives for interactive digital media. Narrative shapes and buttresses many such experiences, and therefore designers must construct compelling narrative arcs while carefully considering the effects of interaction on both the story and the player. As the narrative becomes more structurally complex, due to choice-based branching and other player actions, designers need to employ commensurately capable models and visualizations to keep track of that growing complexity. However, previous models of interactive narrative have failed to fully capture interactive elements with automated, operationalized visualizations. In this paper, we describe an algorithm for automated construction of a framework-driven, graph-based representation of interactive narrative. This representation more fully and transparently models structural and interactive features of the narrative than did prior approaches. We present an initial evaluation of this representation, based on modified cognitive walkthroughs performed by interactive narrative design and research experts from our research team, and we describe the takeaways for future improvement on interactive narrative modeling and analysis.

This paper presents steps towards a generalized player model built around an external personalization and design framework. The external framework, the Player, Environment, Agents, System (PEAS) framework, resulted from a broad scope review of the personalization, player modeling, and game design literature. Leveraging this framework allows us to define a mapping from existing player and personality models to a uniform representation of player preferences over game components. We present our pipeline for developing a generalized player model from these existing models, how to translate and blend them, and finally how to use the blended model for recommending and personalizing games. We follow up the presentation of our pipeline with an extended example to highlight how two existing player modeling approaches can be combined into a singular model, and how that blended model can be used.

Personalization has been explored in the context of games in many forms (e.g., dynamic difficulty adjustment, affective video games, adaptive systems, experience-driven PCG, etc.). The majority of techniques used in these fields have relied on data-driven or manual methods for identifying game components to modify for personalization. We propose a theoretical framework for identifying and categorizing low-level components of games that can be personalized. In this paper we first perform a review of game design frameworks and personalization approaches. We then systematically identify the aspects of games which have been utilized for personalization and which components have been identified in game design frameworks as building blocks of games. We synthesize the identified components into categories of game elements. We then propose PEAS, a theoretical framework for personalization through the adaptation of the Player, Environment, Agents, and System.

While scholarly interest typically focuses on what student designers learn from creating digital games, there has been little investigation into what student designers make. This paper analyzes what design patterns emerge within a set of games from student designers who were instructed to design educational games about climate change using a particular design tool called Scratch. In this preliminary investigation, 19 games are analyzed using the triadic game design framework, which explores relationships between the topic (Reality), learning goal (Meaning) and genre (Play) of serious games. Here we suggest that these decisions fell into a few prototypical patterns, and suggest further paths of inquiry into students' designs regarding considering students' experience with games and supporting the representation of abstract concepts.

Presenting levels commensurate with players' current understanding of game mechanics and level design is a significant challenge in designing games. Often game designers create levels by hand intending for the levels to increase in difficulty over the the course of the game while relying on their intuition or extensive user feedback, reiteration, and testing. Instead, this study starts from a number of procedurally generated levels originally generated by parameters expected to encourage a good difficulty progression and then presented to players during playtests. A number of AI-bots with different characteristics were then designed to assess the difficulty of each level. These findings are then compared with player data. Our findings show that bots encapsulating idealized player strategies can help us create a richer model of level difficulty that then reveals useful information about player struggles and learning across level progressions.

Computer Science (CS) education at the middle school level using educational games has seen recent growth and shown promising results. Typically these games teach the craft of programming and not the perspectives required for computational thinking, such as abstraction and algorithm design, characteristic of a CS curriculum. This research presents a game designed to teach computational thinking via the problem of minimum spanning trees to middle school students, a set of evaluation instruments, and the results of an experimental pilot study. Results show a moderate increase in minimum spanning tree performance; however, differences between gender, collaboration method, and game genre preference are apparent. Based on these results, we discuss design considerations for future CS educational games focused on computational thinking.

Given the rapid technological advances in our society and the increase in artificial and automated advisors with whom we interact on a daily basis, it is becoming increasingly necessary to understand how users interact with and why they choose to request and follow advice from these types of advisors. More specifically, it is necessary to understand errors in advice utilization. In the present study, we propose a methodological framework for studying interactions between users and automated or other artificial advisors. Specifically, we propose the use of virtual environments and the tarp technique for stimulus sampling, ensuring sufficient sampling of important extreme values and the stimulus space between those extremes. We use this proposed framework to identify the impact of several factors on when and how advice is used. Additionally, because these interactions take place in different environments, we explore the impact of where the interaction takes place on the decision to interact. We varied the cost of advice, the reliability of the advisor, and the predictability of the environment to better understand the impact of these factors on the overutilization of suboptimal advisors and underutilization of optimal advisors. We found that less predictable environments, more reliable advisors, and lower costs for advice led to overutilization, whereas more predictable environments and less reliable advisors led to underutilization. Moreover, once advice was received, users took longer to make a final decision, suggesting less confidence and trust in the advisor when the reliability of the advisor was lower, the environment was less predictable, and the advice was not consistent with the environmental cues. These results contribute to a more complete understanding of advice utilization and trust in advisors.

In this paper the role of game performance as an assessment tool is explored and an approach is presented for designing and assessing learning-centered game scores. In recent years, attention has shifted from focusing on games for learning to games for assessment. The research question this paper tries to address is how valid games are as an assessment tool, and more specifically, how valid the use of game scores are as a measure of assessment. To explore this use, we looked at the role of game performance in a game where the goals were designed based on its learning objectives. We hypothesized that because of this design the scores could be used as a measure of learning. The results of our mixed-methods study confirmed this hypothesis. However, the scores are influenced by factors such as computer skills and age. Further analysis revealed that the design of the game and the game-based training were also of influence. These insights will help in designing better predictive game scores in the future.

There is a growing need to understand how automated decision aids are implemented and relied upon by users. Past research has focused on factors associated with the user and automation technology to explain reliance. The purpose of the present study was determining how the predictability of the environment affects reliance. In this paper, we present the results from an experiment using a digital game where participants had access to a free environmental cue of varying predictive validity. Some participants also had access to automated advice at varying costs. We found that participants underutilized automated advice in more predictable environments and when advice was more costly; however, when costs were low and the environment was less predictable, participants tended to overutilize automated advice. These findings provide insights for a more complete model of automation use, and offer a framework for understanding automation biases by considering how automation use compares to a model of optimality.