This paper presents Autonoming, a child-caregiver-AI safety system currently conceptualized as a browser-plugin prototype, which reimagines child online safety as a self-regulated learning process rather than a surveillance model. Grounded in Self-Regulated Learning (SRL), Autonoming structures safety as an integrated child-caregiver-AI journey across three phases: forethought, where families plan boundaries; performance, where children receive support during risky moments; and self-reflection, where contested prompts and periodic reviews support explanation, reflection, and future strategies. These phases are translated into four interaction components: families co-create (a) a Safety Agreement before online activity; children receive (b) real-time Scaffolding Prompts during risky moments; contested prompts become learning opportunities through (c) a Negotiation Bypass; and periodic (d) Trust-Building Reviews support reflection and future rule revision. The article contributes an SRL-based design model for explainable, contestable, and trust-building child online safety systems, offering a pathway to move from parental control toward collaborative safety learning.

This paper examines how player flexibility – a player’s willingness to engage in a breadth of options or specialize – manifests across two gaming environments: League of Legends (League) and Teamfight Tactics (TFT). We analyze the gameplay decisions of 4,830 players who have played at least 50 competitive games in both titles and explore cross-game dynamics of behavior retention and consistency. Our work introduces a novel cross-game analysis that tracks the same players’ behavior across two different environments, reducing self-selection bias. Our findings reveal that while games incentivize different behaviors (specialization in League versus flexibility in TFT) for performance-based success, players exhibit consistent behavior across platforms. This study contributes to long-standing debate about agency versus structure, showing individual agency may be more predictive of cross-platform behavior than game-imposed structure in competitive settings. These insights offer implications for game developers, designers and researchers interested in building systems to promote behavior change.

Although young adults frequently intend to cook sustainably, everyday behavioral barriers often prevent these intentions from becoming action. Grounded in Self-Determination Theory (SDT), which identifies competence, autonomy, and relatedness as core psychological needs driving motivated behavior, this paper argues that the intention-action gap in sustainable cooking reflects not a deficit in values but a failure to support these needs at the moment of choice. Survey data from 117 participants and workshop insights from 22 participants identified three core inhibitors of vegetable use: action uncertainty (a failure of competence support), low cooking self-efficacy (a failure of competence and autonomy), and decision fatigue (a failure of autonomy). We developed FlavorLoop, an AI-driven and gameful cooking assistant grounded in SDT and the serious games for health tradition. FlavorLoop provides context-aware meal suggestions with rationale, supports competence through adaptive micro-goals, and activates behavior through need-supportive gameful mechanics including progress indicators, supportive feedback, and a Social Co-Cooking Challenge enabling low-pressure parallel co-presence. A seven-day simulated field probe with twelve participants found that FlavorLoop reduced uncertainty and emotional load, enhanced perceived competence, and supported initiation of cooking actions that had previously been postponed. Participants described the Social Co-Cooking Challenge as especially motivating because it created accountability and mutual encouragement without evaluation.

Wildfire preparedness is a deeply social challenge that depends on shared understanding, coordinated action, and community-level decision-making. Serious games have emerged as promising tools for supporting preparedness learning, yet their evaluation often relies on individual outcomes or surveys, leaving interactional learning processes underexplored. This paper addresses this gap by analyzing how players from wildfire-affected communities collaboratively constructed preparedness knowledge while playing Firesafe Friends, a two-player serious game. Using a Discourse Acts approach, we analyzed dialogue from onsite and remote sessions (n = 20) and triangulated the findings with pre/post surveys and observational notes. Our analysis reveals distinct phases of collaborative learning – orientation, strategy formation, collaboration, and reflection – marked by shifts in cognitive, social, and integrated discourse acts. Budget constraints prompted negotiation, contested upgrades supported collective reasoning, and reflective dialogue helped players connect in-game decisions to real-world preparedness. We contribute (1) an empirical analysis of discourse as a mechanism for resilience learning, (2) a discourse-centered triangulated evaluation method, and (3) design implications for resilience games that scaffold negotiation and reflection to strengthen community preparedness.

As wildfires increasingly threaten communities worldwide, those at risk can benefit from community-centric approaches to designing interventions. A multi-layered approach can make members of the community powerful co-designers by incorporating their ideas alongside insights from experts and designers to successfully create and deploy an intervention. We took a community-centered Research through Design approach to develop a Serious Game to promote wildfire preparedness in two communities. Participatory Design workshops, interviews, and targeted user studies allowed us to leverage local knowledge of residents and expertise of stakeholders at distinct stages where their insight could be best utilized. Based on this work, we propose a process for designing Serious Games as interventions for complex societal challenges by utilizing community and expert involvement throughout iteration. Our findings suggest that the games we designed following this process lead to concrete learning and opportunities for discussion, and offer flexibility for integration with other community initiatives.

Previous work demonstrated that esports players often leverage insights from other players and communities to learn and improve. However, little research examined social learning in esports, over time, in granular detail. Understanding the role of others in the esports learning process has implications for the design of computational support systems that can help esports players learn and make the games more accessible. Therefore, we perform an exploration of this topic using Co-Regulated Learning as a theoretical lens. In doing so, we hope to enrich existing knowledge on social learning in esports, provide insights for the future development of computational support, and a road-map for future work. Through an interview study of an esports community consisting of 14, college-aged, male players, we uncovered 10 themes regarding how Co-Regulated learning occurs within their teams. Based on these, we discuss three main takeaways and their implications for future research and development.

Educational games have emerged as potent tools for helping students understand complex concepts and are now ubiquitous in global classrooms, amassing vast data. However, there is a notable gap in research concerning the effective visualization of this data to serve two key functions: (a) guiding students in reflecting upon their game-based learning and (b) aiding them in analyzing peer strategies. In this paper, we engage educators, students, and researchers as essential stakeholders. Taking a Design-Based Research (DBR) approach, we incorporate UX design methods to develop an innovative visualization system that helps players learn through gaining insights from their own and peers’ gameplay and strategies.

The concept of dealing damage is established and widespread in video games. With growing complexity and countless interactions in modern games, capturing how damage unfolds becomes an intricate problem - for developers just as for players. Misunderstanding how to optimize damage potentials includes risks of game imbalances, game-breaking exploits, mismatches between player skill and challenge (harming flow), and impaired perceived competence. All of these considerably impact player experience, game reception, success, and retention, yet polishing optimal strategies remains often a player community effort. To accelerate, inform and ease this process, we implemented an interactive tool capable of simulating, visualizing, planning and comparing damage strategies in video games. Following a case study within the Guild Wars 2 community, we contribute a player-driven perspective on the problem of damage optimization, as well as an artifact that resulted in empirical improvements – advancing the fields of game analytics, game evaluation methods and self-regulated learning.

Within the field of game analytics, visualization has been widely instrumentalized to provide an effective graphical interface for analysts to understand player experiences, preferences, and behaviors. However, it is often difficult to decipher strategies or intent with vast amounts of granular events that are rapidly accumulating. Most current visualization approaches focus on aggregated data, utilizing fundamental charts, heatmaps, and scatterplots to conduct statistical analyses, neglecting sequential information across player behavior. In this paper, we focus on sequential visualization approaches, proposing a set of requirements and an initial version of a visualization system developed based on a participatory, iterative co-design approach, collaborating with stakeholders of different games. This co-creative development demonstrates what features analysts need to reason about process visualization, which informs game analytics research. We further discuss a technical realization incorporating the resulting requirements, e.g., manipulation, filtering, abstraction, and segmentation, as well as the underlying algorithms used to realize them.

Self-reflection, a process consisting of evaluation, attribution, and adaptation, is recognized as key to learning in esports games. To better facilitate self-reflection, many games and third-party tools feature retrospective visualizations that provide players with details about their performance. However, previous work suggested that existing visualizations, which typically aggregate data, do not present sufficient information to players. In this work, we explore the impact of process visualizations, which present gameplay as a sequence of actions, on self-reflection and performance in a League of Legends training exercise through a mixed-methods study. Our results found that players who reflected using a process visualization significantly improved their performance and demonstrated significant differences in how they evaluated themselves compared to those who reflected using an aggregate visualization. We discuss what these results mean in relation to self-reflection and learning in esports and how they impact the future use of process visualizations in games.

Today's videogames are often intertwined with profound religious narratives, content, characters, and artifacts - but how do player communities understand the role of religion in games, and what is the impression of believers on the use, portrayal, and representation of the religion they practice? This paper dives deep into this confluence by rigorously analyzing discussions from religion and videogame subreddits that often depict the most popular communication channel for their communities. Following topic modeling, we cluster posts and comments from the three major religious groups and the most popular related videogame subreddits, bringing forth representative cultural sentiments and patterns of players and religious stakeholders. Through this, we discovered 22 distinct sub-themes, further categorized into the three overarching themes of (a) Blasphemous Elements of Videogames, (b) Religion as a Design Space for Videogames, and (c) Videogames for Religious Education and Community Forming.

Adaptation, or ability and willingness to consider an alternative approach, is a critical component of learning through reflection, especially in educational games, where there are often multiple avenues to success. As a domain, educational games have shown increased interest in using retrospective visualizations to promote and support reflection. Such visualizations, which can facilitate comparison with peer data, may also have an impact on adaptation in educational games. This has, however, not been empirically examined within the domain. In this work, we examine how comparison with other players’ data influenced adaptation, a part of reflection, in the context of a game that teaches parallel programming. Our results indicate that comparison with peers does significantly impact willingness to try a different approach, but suggest that there may also be other ways. We discuss what these results mean for future use of retrospective visualizations in educational games and present opportunities for future work.

Video game analytics are widely adopted within academia and industrial development – yet, the player’s perspective is rarely considered as a driving factor, even though vast communities are interested in self-regulated learning, cross-comparisons and empirical trends or insights. In this work, we propose to empower the role of the player as the central impulse for analytics, collect requirements and data from the community of one of the most popular MMORPGs (Guild Wars 2), establish a tool over a 18-month period of participatory development and discuss the co-created features. With analytics for in-game logs from (n = 175, 099) unique players and atomic game actions of over 2 million hours played, we contribute a large-scale, long-term iterative implementation and evaluation of a player-driven instrument to quantify popularity, viability and hierarchical inspection between classes or individual performances. This tool found frequent usage among the actual game community, delivering game data science to the very player.

Identifying explainable player strategies and decision patterns that give insights into player behavior is one of the most difficult tasks for game analytics, yet yields great informative potential for various purposes. Industrial stakeholders can capture player experience and infer issues or feedback on design, content, and game balancing - while players themselves might want to leverage this technique to contrast their style of play to other players, fostering self-regulated learning. On top of that, in the case of educational games, the identification of learning strategies (as well as the discovery of popular erroneous strategies) could even elevate their potential to successfully communicate educational concepts. To advance this field, we investigate how the spatial map of a game contributes to identifying player strategies and emphasize the importance of the appropriate level of abstraction to capture strategical decisions. Using visualizations and expert domain knowledge about spatial abstraction, we illustrate how the partitioning into affordance zones can reveal patterns and strategies in gameplay. To showcase the generalizability of our methodology, we investigate two case studies for the distinct genres of educational games (Parallel) and MMORPGs (Guild Wars 2). The two case studies unveil insightful strategies between different player sets of interest – only possible by the apt level of spatial abstraction.

In this demo paper, we present Parallel OPM. Informed by research on player needs of AI in educational games [1], it is a new visualization system that uses play community data from other players to help players compare and reflect on their gameplay with their peers in the game Parallel [2]. In this demo paper/session: participants will (i) have the opportunity to play a level in the game Parallel [2] (ii) Use the guided reflection system to analyze and reflect on their gameplay compared to their peers.

Within the field of games, visualization of player log data is becoming an important method for its utility in providing an intuitive and informative way to understand players' experience, which is thus often used by game analytics personnel and game user researchers. Moreover, even players themselves show increased interest in using analytics to quantify and self-improve their performances. Among other types of visualizations, node-edge graphs have proven to be capable of revealing the process of individual and aggregated players, allowing analysts to discover play patterns that can inform game design. However, visualization of player traces often has several disadvantages. First, displaying players' process data does not trivially scale, as tendentially high variance often leads to complex and abstruse graphs. Second, when aggregating all players, individual variations are often overlooked. For example, data from minorities (e.g., casual players or players who played the game very differently than others) are often treated as outliers or noise. In this paper, we present an iterative segmentation approach that allows analysts to interact with the visualization and group players into different subcategories through meta-data or behavioral patterns. Using this approach, analysts can bypass complicated visualizations while protecting significant unique information.

Emotion regulation and coping strategies are key to resilience, problem solving and eventually well-being in everyday life, but investigating or influencing these without adjustable and ecologically valid environments still poses a major challenge. With respect to video games, stressful events can frequently appear and, depending on the individual’s emotion regulation, lead to frustrating experiences, dissatisfied players or even churn – or to a sense of accomplishment, mastery or positive tension. In the greater endeavor of establishing games for studying, controlling and reinforcing resilience, we developed an alternate reality game that unifies the advantages of a fully customizable game environment with the close connection to real-life interactions. To estimate how emotion regulation strategies could be initiated as well as investigated, this work first induces different kinds of established stressors (time pressure, social encounters and being unchangeably stuck) into ordinary gameplay, quantifies the players’ physiological and psychological responses and addresses the participants’ strategies on dealing with these situations. In this paper we present results of the study showing that the induced stressors were effective in constituting stressful situations comparable to real-life experiences; and each of these conditions resulted in different types of physiological and perceived responses and behaviors, e.g. continuous stress in social encounters versus instantaneous stress when notified about time pressure; or frustrating experiences when being forcibly stuck versus stress that participants underwent but were able to work against. With this platform to study how people cope with stress, instituted within a multi-modal mixed-methods evaluation, we contribute to games beyond entertainment towards educating resilience.

As games get more complicated, Artificially Intelligent (AI) agents need to be better developed to understand and replicate complex, goal-oriented, reactive behaviors. Many existing behavior language approaches do not often make the AI’s behavior transparent and readable to the human, making it difficult to debug and update the behavioral model. In this workshop paper, we present a prototype visualization system that uses a tree-based abstraction to present complex agent behavior to a human user, allowing them to understand and edit the agent’s behavioral logic in a visual manner.

Anthropology, defined as the scientific study of human development and societies, can play a critical role in the design of (video) game mechanics and aesthetics. Especially religion and culture are seen as fundamental components in building communities, societies, and human evolution. In this paper, we present (1) a case study using UX methods such as ethnography, grounded theory, participatory design, and user interviews to develop and evaluate design spaces generated from cultural spaces, (2) this design space in the form of paradigms and syntagms and (3) how this design space can assist in the construction of novel and culturally sensitive characters.

The game design community has a long history of adapting different forms of AI techniques to produce new playable experiences. However, current AI-based game design literature focuses primarily on designers’ intent and expression. This paper argues that engaging with players’ perspectives on AI during development is an essential but often overlooked piece in existing AI-based game design processes. By integrating this perspective, game designers can better outline how players may experience AI in the context of games and tailor design decisions to the intended experience. This paper offers three case studies that incorporate the player perspective into the design process and discusses design implications.

Esports play can cultivate real world skills. However, the path to mastery is not easy, and difficulty progressing can result in discontinuation. In the absence of a human coach, computational tools may provide much-needed guidance. However, the specific improvement activities that players engage in and the exact challenges they face are not well defined in the context of computational support. As such, most tools can only support players based on a high level understanding of their practices. We present the results of an interview study (n=17) that identified four improvement activities: practicing, leveraging the knowledge of others, tracking performance, and reflecting on gameplay and setting goals for the future, and four challenges: coordinating and collaborating with teammates, knowing what to do next, tracking game state, and tracking skill and improvement. We discuss six implications for future design and development based on these results.

Learners’ backgrounds, skills, and knowledge vary as they attempt to learn a new subject. To address this variation and allow learners to progress at their own speed, many researchers are suggesting adaptive learning as a solution. Adaptive content has been successful in learning environments such as intelligent tutoring systems, but it has not been thoroughly researched within video games, especially in terms of adaptive procedural levels. In this paper, we analyze the effects of procedural levels that are generated and inserted at run-time in between pre-designed levels in the educational programming game May’s Journey. Our study with 94 Amazon Mechanical Turkers shows that players encountered fewer code-related errors in the adaptive version, however, their engagement levels were similar, if not slightly higher in the non-adaptive version.

To assist game developers in crafting game NPCs, we present EvolvingBehavior, a novel tool for genetic programming to evolve behavior trees in Unreal®Engine 4. In an initial evaluation, we compare evolved behavior to hand-crafted trees designed by our researchers, and to randomly-grown trees, in a 3D survival game. We find that EvolvingBehavior is capable of producing behavior approaching the designer’s goals in this context. Finally, we discuss implications and future avenues of exploration for co-creative game AI design tools, as well as challenges and difficulties in behavior tree evolution.

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.

Computational support for learning in the domain of esports has seen a great deal of attention in recent years as an effective means of helping players learn and reap the benefits of play. However, previous work has not examined the tools from a learning theory perspective to assess if learning is prompted and supported in the right place and time. As a first step towards addressing this gap, this paper presents the results of two studies: a review of existing computational tools, and an online survey of esports' players' learning needs supplemented with qualitative interviews. Using Zimmerman's Cyclical Phase Model of Self-Regulated Learning as a lens, we identify patterns in the types of support offered by existing tools and players' support interests during different learning phases. We identify 11 opportunities for future research and development to better support self-regulated learning in esports.

The papers in this special section focus on user experiences of artificial intelligence in games.

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.

Player agency is central to interactive narrative and games. While previous work focuses on analyzing player perception of agency through various lenses and phenomena, like meaningful choice and expectations, it is largely theoretical. Few user studies within games explore how players reason about and judge their own agency within interactive narratives. We present an interview study where participants rated their agency experiences within narrative-focused games and described their reasoning. The analysis suggests that agency perception depends on multiple factors beyond meaningful choice, such as social investment and genre-conventions. Participants described varying preferences and value judgements for different factors, indicating that individual differences have a deep impact on agency perception in narrative-focused gameplay. We discuss the implications of these cognitive variables on design, how they can be leveraged with other factors, and how our findings can help future work enhance and measure player agency, within interactive narrative and beyond.

In 2020, the rapid spread of COVID-19 forced many people to self-isolate, resulting in struggles with mental health [60], and an increase in gaming [3]. In this paper, we seek to examine how individuals used digital games during the quarantine. We conducted a two-tier qualitative study where we used a thematic analysis of tweets to derive questions for an online survey that we distributed. Results of thematic analysis of survey responses identified 15 themes. Some themes confirm previous works’ findings, particularly how games are used to increase social connection or distract oneself from unpleasant situations. We also found new themes unique to the quarantine, such as interactions with non-player characters used as a surrogate for real-world interaction and using in-game routines as a substitute to real-world routines lost due to the pandemic. This work discusses the use of games during the pandemic and can be seeds for future studies.

Physical activity (PA) is crucial for reducing the risk of obesity, an epidemic that disproportionately burdens families of low-socioeconomic status (SES). While fitness tracking tools can increase PA awareness, more work is needed to examine (1) how such tools can help people benefit from their social environment, and (2) how reflections can help enhance PA attitudes. We investigated how fitness tracking tools for families can support social modeling and self-modeling (through reflection), two critical processes in Social Cognitive Theory. We developed StoryMap, a novel fitness tracking app for families aimed at supporting both modes of modeling. Then, we conducted a five-week qualitative study evaluating StoryMap with 16 low-SES families. Our findings contribute an understanding of how social and self-modeling can be implemented in fitness tracking tools and how both modes of modeling can enhance key PA attitudes: self-efficacy and outcome expectations. Finally, we propose design recommendations for social personal informatics tools.

Game AI designers must manage complex interactions between the AI character, the game world, and the player, while achieving their design visions. Computational co-creativity tools can aid them, but first, AI and HCI researchers must gather requirements and determine design heuristics to build effective co-creative tools. In this work, we present a participatory design study that categorizes and analyzes game AI designers’ workflows, goals, and expectations for such tools. We evince deep connections between game AI design and the design of co-creative tools, and present implications for future co-creativity tool research and development.

To reduce frustration while performing no-risk tasks (e.g. in training and games) for BCI users, we propose increasing their perceived level of control through fabricated input - system-generated positive task outcomes. Two surrogate BCI studies injected fabricated input creating additional positive task outcomes to a 50% baseline. Users' perceived control increased significantly compared to the 50% baseline. In turn, frustration levels decreased. Fabricated input worked equally well in a game story context that provided an emotional stake in the protagonist's success and a simpler task lacking such incentives. People's number of input attempts during the tasks determined perceived control more than our controlled ratios of positive to negative task outcomes. Delays between users' input attempts and subsequent fabricated inputs further moderated their perceived control.

Timelines are commonly represented on a horizontal line, which is not necessarily the most effective way to visualize temporal event sequences. However, few experiments have evaluated how timeline shape influences task performance. We present the design and results of a controlled experiment run on Amazon Mechanical Turk (n=192) in which we evaluate how timeline shape affects task completion time, correctness, and user preference. We tested 12 combinations of 4 shapes --- horizontal line, vertical line, circle, and spiral — and 3 data types — recurrent, non-recurrent, and mixed event sequences. We found good evidence that timeline shape meaningfully affects user task completion time but not correctness and that users have a strong shape preference. Building on our results, we present design guidelines for creating effective timeline visualizations based on user task and data types. A free copy of this paper, the evaluation stimuli and data, and code are available https://osf.io/qr5yu/

Interactive narratives are frequently designed for learning and training applications, such as social training. In these contexts, designers may be inexperienced in storytelling and interaction design, and it may be difficult to quickly build an effective experience, even for experienced designers. Designers often approach this problem through iterative design. To augment and reduce iteration, we argue for the utility of employing models to reason about, evaluate, and improve designs. While there has been much previous work on interactive narrative models, none of them capture aspects of the interaction design necessary for testing and evaluation. In this paper we propose a new computational model called Progression Maps, which abstracts interaction design elements of the narrative's structure and visualizes its interaction properties. We report on the model, its implementation, and two studies evaluating its use. Our results demonstrate Progression Maps' effectiveness in communicating the underlying design through an easily understandable visualization.

Physical activity (PA) is critical for reducing the risk of obesity, a prevalent health concern that burdens low-socioeconomic status (SES) households. While self-tracking apps can increase PA, encouraging app engagement remains a challenge, thus limiting the app's efficacy. To understand how to better support caregiver's motivation to use family health apps, we designed and evaluated Storywell?a mobile app for promoting family PA. Guided by Self-Determination Theory, Storywell provides social rewards (e.g., storybooks with interactive reflective questions) aimed at supporting relatedness and motivation. Our 3-month qualitative study with 18 families revealed satisfying moments that can affect caregiver's motivation. We contribute new knowledge on designing satisfying moments that heighten the motivation to use health apps, especially for low-SES families who face many barriers to using such systems.

In recent years, the games industry has made a major move towards data-driven development, using data analytics and player modeling to inform design decisions. Data-driven techniques are beneficial as they allow for the study of player behavior at scale, making them very applicable to modern digital game development. However, with this move towards data driven decision-making comes a number of ethical concerns. Previous work in player modeling [45] as well as work in the fields of AI and machine learning [9, 53] have demonstrated several ways in which algorithmic decision-making can be flawed due to data or algorithmic bias or lack of data from specific groups. Further, black box algorithms create a trust problem due to lack of interpretability and transparency of the results or models developed based on the data, requiring blind faith in the results. In this position paper, we discuss several factors affecting the use of game data in the development cycle. In addition to issues raised by previous work, we also raise issues with algorithms marginalizing certain player groups and flaws in the resulting models due to their inability to reason about situational factors affecting players’ decisions. Further, we outline some work that seeks to address these problems and identify some open problems concerning ethics and game data science.

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).

Crowdsourcing games involving image labeling tasks are commonly digital, played online, and have rules set by designers. In this work we explore the potential of tabletop image labeling games, incorporating physical elements, in-person community-based gameplay, and support for customizable rules. We developed an augmented reality game toolkit called Tile-o-Scope AR and conducted two studies. The first study demonstrates how the toolkit can facilitate in-person discussions through collaborative image labeling, and the toolkit’s potential adaptability to other games and applications. The second study, using three different activities designed for the toolkit, demonstrates the toolkit’s flexibility for creating customized experiences for audiences of different backgrounds.

Reflection is a critical aspect of the learning process. However, educational games tend to focus on supporting learning concepts rather than supporting reflection. While reflection occurs in educational games, the educational game design and research community can benefit from more knowledge of how to facilitate player reflection through game design. In this paper, we examine educational programming games and analyze how reflection is currently supported. We find that current approaches prioritize accuracy over the individual learning process and often only support reflection post-gameplay. Our analysis identifies common reflective features, and we develop a set of open areas for future work. We discuss these promising directions towards engaging the community in developing more mechanics for reflection in educational games.

Debugging is an integral part of programming and is difficult to master among novice programmers. We examine how middle-school learners approach debugging within a puzzle game, May's Journey, using a Playback Tool that provides a replay of student gameplay to support teacher practice and research. Teachers and researchers can use our labeling system and Playback Tool to observe students' debugging process and identify those who are struggling to learn how to debug their code.

be especially problematic for beginners. While there has been some work trying to understand the difficulties that beginners face with debugging, investigating common mistakes or specific error types they struggle with, there is little work that focuses on in-depth analysis of how novice programmers approach debugging, and how it changes over time. In this paper, we present MAADS (Mixed-Methods Approach for the Analysis of Debugging Sequences), a scalable and generalizable approach that combines quantitative and qualitative methods by using a state/action representation and visualization to gain knowledge about the debugging process through a step by step analysis. To demonstrate the utility of MAADS, we analyzed the debugging processes of middle school students who developed code within May's Journey, a game designed to teach basic programming principles. The approach showed great utility in identifying differences in students' debugging techniques and learning paths.

Wearable activity trackers can encourage physical activity (PA)-a behavior critical for preventing obesity and reducing the risks of chronic diseases. However, prior work has rarely explored how these tools can leverage family support or help people think about strategies for being active-wo factors necessary for achieving regular PA. In this 2-month qualitative study, we investigated PA tracking practices amongst 14 families living in low-income neighborhoods, where obesity is prevalent. We characterize how social discussions of PA data rarely extended beyond the early stages of experiential learning, thus limiting the utility of PA trackers. Caregivers and children rarely analyzed their experiences to derive insights about the meaning of their PA data for their wellbeing. Those who engaged in these higher-order learning processes were often influenced by parenting beliefs shaped by personal health experiences. We contribute recommendations for how technology can more effectively support family experiential learning using PA tracking data.

When studying the impact of interactive narratives, especially in high impact, sensitive domains such as education and health, it is important that the narrative design as a whole is reported in sufficient detail to allow for analysis and replication. However, the design of interactive narrative applications is often insufficiently documented and reported, despite being a central component and, often, a focus of study. This is due, in part, to a lack of a common lexicon that allows designers and practitioners to describe and compare their designs. This paper examines interaction design in interactive narrative games, specifically structure and progression mechanics, from the perspective of establishing common ground between designer and player. Using the interaction model for interactive narratives to guide our analysis, we found patterns of structure and narrative progression mechanics that offer insight into commercial design practices, which can inform and guide interaction design choices for narrative games.

Games research is a growing interdisciplinary field. As with many interdisciplinary fields, early career faculty often confront many questions concerning the tenure process and evaluation of their interdisciplinary work. While there have been several publications on guidelines or best practices for evaluating interdisciplinary research, addressing Tenure and Promotion policies and processes for faculty as well as units and chairs, none specifically discusses tenure and promotion practices and guidelines for games research. Wardrip---Fruin and Mateas, in a report dating back to 2014 [17], outlined some challenges and recommendations for supporting work within the area of computational media. This report was the first to unpack some issues faced by computational media researchers with some recommendations to the community, funding agencies and institutions, to support this growing and evolving research area. However, this report did not target the tenure and promotion process, specifically. Now that the community is maturing with many faculty already undergoing or have undergone tenure and promotion, more publications and discussions are needed to support the diverse nature of this research area and its junior faculty. This workshop is then a first step to address this issue within the community. This article contributes to this discussion by outlining previous publications' recommendations and guidelines for inter-disciplinary research that are also applicable to the games research area. Additionally, the article also outlines specific unique aspects for games research with some take-aways of challenges and action items that we can take as a community to address these challenges.

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.

There are several educational games and tools that teach programming. However, very few offer a deep understanding of Computer Science concepts such as Abstraction, Modularity, Semantics, and Debugging. We present May's Journey, an educational game that supports learning of basic programming concepts, where players solve puzzles and interact with the environment by typing in a custom programming language. The game design seamlessly integrates learning goals, core mechanics, and narrative elements. We discuss how we integrate the CS concepts mentioned above using game mechanic metaphors.

Many studies have already shown that games can be a useful tool to make boring or difficult tasks more engaging. However, with serious game design being a relatively nascent field, such experiences can still be hard to learn and not very motivating. In this paper, we explore the use of learning and motivation frameworks to improve player experience in the well-known citizen science game Foldit. Using Cognitive Load Theory (CLT) and Self Determination Theory (SDT), we developed six interface and mechanical changes to the tutorial levels in Foldit designed to increase engagement and retention. We tested these features with new players of Foldit and collected both behavioral data, using game metrics, and prior experience data, using self-report measures. This study offers three major contributions: (1) we document the process of operationalizing CLT and SDT as new game features, a unique methodology not used in game design previously; (2) the user interface, specifically the level selection screen, significantly impacts how players progress through the game; and (3) a player's expertise, whether from prior domain knowledge or prior gaming experience, increases their engagement. We discuss both implications of these findings as well as how these implementations can generalize to other designs.

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.

Low-socioeconomic status (SES) families face increased barriers to physical activity (PA)-a behavior critical for reducing and preventing chronic disease. Research has explored how wearable PA trackers can encourage increased activity, and how the adoption of such trackers is driven by people's emotions and social needs. However, more work is needed to understand how PA trackers are perceived and adopted by low-SES families, where PA may be deprioritized due to economic stresses, limited resources, and perceived crime. Accordingly, we conducted a two-month, in-depth qualitative study, exploring low-SES caregivers' perspectives on PA tracking and promotion. Our findings show how PA tracking was impacted by caregivers' attitudes toward safety, which were influenced by how they perceived social connections within their neighborhoods; and cognitive-emotional processes. We conclude that PA tracking tools for low-SES families should help caregivers and children to experience and celebrate progress.

Integrating narrative elements into a game is a key element in designing an immersive experience. Narrative has been hypothesized to improve engagement, motivation, and learning within educational environments. While empirical results have been produced to show that narrative enhances engagement and motivation, its effects on learning were shown to either be insignificant or negative. We, therefore, aim to address the question of how to integrate narrative in a game to improve learning. We address this through the design of May's Journey, an educational game that teaches basic programming concepts where a story is integrated. The game design seamlessly integrates learning goals, core mechanic and narrative elements. In this paper, we discuss the game design as well as a study we conducted to compare two game versions, one with rich narrative and the other with light narrative. Results demonstrate that participants who interacted with the rich narrative version had fewer programming errors and increased engagement within the game. We present our contributions in the form of educational design principles for narrative integration supported by our study and results.

A growing number of new games utilize rewind as a narrative mechanic and there is an increasing need for a reliable way to understand and evaluate storytelling in such games. This paper builds on previous work by conducting narrative interaction focused analysis on rewind mechanics in commercial video games, and focuses on rewind as a form of narrative progression. The analysis resulted in updated definitions of rewind mechanic types originally defined by Kleinman et al. [13]. An updated framework is proposed where rewind is described in terms of control, scope, initiation, presentation, story structure, and structure traversal. Common design patterns associated with rewind, such as narrative justification and feedback, are also discussed. This expanded framework can be used to better understand rewind mechanics, and evaluate storytelling and user experience in the video games that utilize them.

The development of agents that can engage in human social interaction has become critical in an increasingly wide range of applications. The focus of this work is modeling agents for social skills training where learners can interact with the autonomous characters in social scenarios and thereby acquire skills that can be applied in the real world. A key goal in the design of these systems is to allow users to explore various actions and tactics while still having the agents generate consistent and diverse responses. Providing the ability to explore different tactics raises a significant content challenge for the design of agents. To tackle the creative content creation problem, this paper introduces a humans-in-the-loop iterative process to automatically generate rich, varied content from a small amount of vignettes provided by online crowd workers. Specifically, this process uses the crowd to iteratively refine and improve an ensemble of generative models. The results show that the iterative, ensemble based approach generates more coherent and novel interactions than alternative non-ensemble, non-iterative approaches. The results presented in this paper can potentially provide the basis for flexible agent-based training systems.

Multiplayer Online Battle Arena (MOBA) games have received increasing popularity recently. In a match of such games, players compete in two teams of five, each controlling an in-game avatar, known as heroes, selected from a roster of more than 100. The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero. Heroes are designed with different strengths and weaknesses to promote team cooperation in a game. Intuitively, heroes in a strong team should complement each other's strengths and suppress those of opponents. Hero drafting is therefore a challenging problem due to the complex hero-to-hero relationships to consider. In this paper, we propose a novel hero recommendation system that suggests heroes to add to an existing team while maximizing the team's prospect for victory. To that end, we model the drafting between two teams as a combinatorial game and use Monte Carlo Tree Search (MCTS) for estimating the values of hero combinations. Our empirical evaluation shows that hero teams drafted by our recommendation algorithm have a significantly higher win rate against teams constructed by other baseline and state-of-the-art strategies.

Matchmaking connects multiple players to participate in online player-versus-player games. Current matchmaking systems depend on a single core strategy: create fair games at all times. These systems pair similarly skilled players on the assumption that a fair game is best player experience. We will demonstrate, however, that this intuitive assumption sometimes fails and that matchmaking based on fairness is not optimal for engagement.

Games are an important part of popular culture and have successfully engaged audiences from different age groups [8]. This has caused many educators to start thinking of different ways to infuse game-based techniques (playing or construction) to enhance education. Over the past five years, there has been several research efforts on curriculum development and the evaluation of game construction and playing methods to promote STEM (Science Technology, Engineering and Mathematics) concepts to middle and high school students. In this paper, we report on a new study following the lead of previous efforts. In particular, we were granted a block within Frank Hurt High School by the Surrey school district for which to develop a game construction-based curriculum as a basis for computer science education within the school, replacing a traditional computer science class. From February till June 2011 students were taught elementary programming using GameMaker, a digital game development tool. In addition to collecting students' overall impressions, we also evaluated students' technical competency (using tests) and self-efficacy (using surveys) at the start and end of the semester. Our findings show that the curriculum had a positive, statistically significant effect on concept comprehension, which suggests that the curriculum was effective for understanding computer science and game design concepts. In this paper we present the course structure, methods, and evaluation as a contribution.

In this paper we describe the Reading Glove, a wearable RFID reader for interacting with a tangible narrative. Based on interviews with study participants, we present a set of observed themes for understanding how the wearable and tangible aspects of the Reading Glove influence the user experience. We connect our observational themes to theoretical notions from interactive narrative and tangible interaction to create a set of design considerations such as enacting a role, ownership and permission, multiplicity of interpretations and boundary objects.

Cooperative design has been an integral part of many games. With the success of games like Left4Dead, many game designers and producers are currently exploring the addition of cooperative patterns within their games. Unfortunately, very little research investigated cooperative patterns or methods to evaluate them. In this paper, we present a set of cooperative patterns identified based on analysis of fourteen cooperative games. Additionally, we propose Cooperative Performance Metrics (CPM). To evaluate the use of these CPMs, we ran a study with a total of 60 participants, grouped in 2-3 participants per session. Participants were asked to play four cooperative games (Rock Band 2, Lego Star Wars, Kameo, and Little Big Planet). Videos of the play sessions were annotated using the CPMs, which were then mapped to cooperative patterns that caused them. Results, validated through inter-rater agreement, identify several effective cooperative patterns and lessons for future cooperative game designs.

This paper investigates the differences between 3D level designs in 21 popular games. We have developed a framework to analyze 3D level designs based on patterns extracted from level designers and game play sessions. We then use these patterns to analyze several game play sessions. Results of this analysis reveal methods by which designers push and pull players through levels. We discuss an analysis of these patterns in terms of three level affordance configurations, combat, environmental resistance, and mixed goals in 21 different games using one walkthrough play session per game. By looking at the variety of games, we can further explore the level similarities and differences between games.

Successful lighting in video games is more than a physically accurate illumination model. Aesthetics and function are of equal or greater importance. Lighting designers may deviate from physical accuracy to help a player identify an important object or to more powerfully evoke a desired emotion. Under the assumption that fulfilling the pipeline needs of interactive lighting design requires more than solving the computer rendering equation, we set out to develop a System for Automated Interactive Lighting (SAIL). The goal for SAIL was to develop an adaptive system that maintains lighting design goals (aesthetic and functional) in the context of unpredictable, interactive experiences. This paper presents SAIL and the results of a qualitative evaluation of SAIL's contributions. We describe the algorithms of SAIL, where it succeeds, and where it fails. We conclude with a plan for future work.

Recent enhancements in real-time graphics have facilitated the design of high fidelity game environments with complex 3-D worlds inhabited by animated characters. Under such settings, it is hard, especially for the untrained eyes, to attend to an object of interest. Neuroscience research as well as film and theatre practice identified several visual properties, such as contrast, orientation, and color that play a major role in channeling attention. In this paper, we discuss an adaptive lighting design system called adaptive lighting for visual attention (ALVA) that dynamically adjusts the lighting color and brightness to enhance visual attention within game environments using features identified by neuroscience, psychophysics, and visual design literature. We also discuss some preliminary results showing the utility of ALVA in directing player's attention to important elements in a fast paced 3-D game, and thus enhancing the game experience especially for nongamers who are not visually trained to spot objects or characters in such complex 3-D worlds.

The game design process is largely driven by practice. While some work has been done in academia targeting the definition of a theoretical foundation for the process of game design, these two communities rarely came together to discuss their perspective theories or processes. As a result, both communities work in isolation. The game industry is often involved in game-specific game design methodologies and academics are concerned with theoretical foundations. The goal of this workshop is to start a dialogue between the two communities and generate general themes and underlying theories. These theories will serve to aid game designers in constructing games, and help tool designers build tools that allow designers to focus on critical issue.

Visual attention has long been an important topic in psychology and cognitive science. Recently, results from visual attention research (Haber et al. 2001; Myszkowski et al. 2001) are being adopted by computer graphics research. Due to speed limitations, there has been a movement to use a perception-based rendering approach where the rendering process itself takes into account where the user is most likely looking (Haber et al. 2001). Examples include trying to achieve real-time global illumination by concentrating the global illumination calculation only in parts of the scene that are salient (Myszkowski 2002). Video games have achieved a high degree of popularity because of such advances in computer graphics. These techniques are also important because they have enabled game environments to be used in applications such as health therapy and training.

DigitalBeing is an ambient intelligent system that aims to use stage lighting and lighting in projected imagery within a dance performance to portray dancer's arousal state. The dance space will be augmented with pressure sensors to track dancers' movements; dancers will also wear physiological sensors. Sensor data will be passed to a three layered architecture. Layer 1 is composed of a system that analyzes sensor data. Layer 2 is composed of two intelligent lighting systems that use the analyzed sensor information to adapt onstage and virtual lighting to show dancer's arousal level. Layer 3 translates lighting changes to appropriate lighting board commands as well as rendering commands to render the projected imagery.

Lighting design is an essential element of visual storytelling. In cinematic theory, lighting design plays an important role in illumination, directing viewer's gaze to important locations, and portraying visual tension. However, lighting design is a complex process, and is especially problematic for interactive entertainment systems, since important design parameters such as spatial configuration, dramatic focus, and dramatic intensity, cannot be determined in advance. Indeed, they often change dramatically during interaction. Additionally, manually adjusting colors, positions, and angles of each light in a scene is a time consuming and tedious process. In this paper, we describe a system developed based on cinematic and theatrical lighting design theory to automatically adjust positions, colors, and angles of each light in real-time to accommodate the continually evolving dramatic situation, while maintaining the desired style, and ensuring visual continuity.

The ability to express emotions is important for creating believable interactive characters. To simulate emotional expressions in an interactive environment, an intelligent agent needs both an adaptive model for generating believable responses, and a visualization model for mapping emotions into facial expressions. Recent advances in intelligent agents and in facial modeling have produced effective algorithms for these tasks independently. We describe a method for integrating these algorithms to create an interactive simulation of an agent that produces appropriate facial expressions in a dynamic environment. Our approach to combining a model of emotions with a facial model represents a first step towards developing the technology of a truly believable interactive agent which has a wide range of applications from designing intelligent training systems to video games and animation tools.