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.

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.

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.

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.

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.

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.