Erica Kleinman

Daijin Yang, Erica Kleinman, Casper Harteveld

Educational games can foster critical thinking, problem-solving, and motivation, yet instructors often find it difficult to design games that reliably achieve specific learning outcomes. Existing authoring environments reduce the need for programming expertise, but they do not eliminate the underlying challenges of educational game design, and they can leave non-expert designers reliant on opaque suggestions from AI systems. We designed a controlled natural language framework-based web tool that positions language as the primary interface for LLM-assisted educational game design. In the tool, users and an LLM assistant collaboratively develop a structured language that maps pedagogy to gameplay through four linked components. We argue that, by making pedagogical intent explicit and editable in the interface, the tool has the potential to lower design barriers for non-expert designers, preserves human agency in critical decisions, and enables alignment and reflections between pedagogy and gameplay during and after co-creation.

Daijin Yang, Erica Kleinman, Casper Harteveld

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.

Daijin Yang, Erica Kleinman, Casper Harteveld

Due to GPT's impressive generative capabilities, its applications in games are expanding rapidly. To offer researchers a comprehensive understanding of the current applications and identify both emerging trends and unexplored areas, this paper introduces an updated scoping review of 177 articles, 122 of which were published in 2024, to explore GPT's potential for games. By coding and synthesizing the papers, we identify five prominent applications of GPT in current game research: procedural content generation, mixed-initiative game design, mixed-initiative gameplay, playing games, and game user research. Drawing on insights from these application areas and emerging research, we propose future studies should focus on expanding the technical boundaries of the GPT models and exploring the complex interaction dynamics between them and users. This review aims to illustrate the state of the art in innovative GPT applications in games, offering a foundation to enrich game development and enhance player experiences through cutting-edge AI innovations.

Alayt Issak, Uttkarsh Narayan, Ramya Srinivasan et al.

Ethical theories and Generative AI (GenAI) models are dynamic concepts subject to continuous evolution. This paper investigates the visualization of ethics through a subset of GenAI models. We expand on the emerging field of Visual Ethics, using art as a form of critical inquiry and the metaphor of a kaleidoscope to invoke moral imagination. Through formative interviews with 10 ethics experts, we first establish a foundation of ethical theories. Our analysis reveals five families of ethical theories, which we then transform into images using the text-to-image (T2I) GenAI model. The resulting imagery, curated as Kaleidoscope Gallery and evaluated by the same experts, revealed eight themes that highlight how morality, society, and learned associations are central to ethical theories. We discuss implications for critically examining T2I models and present cautions and considerations. This work contributes to examining ethical theories as foundational knowledge that interrogates GenAI models as socio-technical systems.

Nathan Partlan, Erica Kleinman, Jim Howe et al.

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.

Magy Seif El-Nasr, Casper Harteveld, Paul Fombelle et al.

While work in fields of CSCW (Computer Supported Collaborative Work), Psychology and Social Sciences have progressed our understanding of team processes and their effect performance and effectiveness, current methods rely on observations or self-report, with little work directed towards studying team processes with quantifiable measures based on behavioral data. In this report we discuss work tackling this open problem with a focus on understanding individual differences and its effect on team adaptation, and further explore the effect of these factors on team performance as both an outcome and a process. We specifically discuss our contribution in terms of methods that augment survey data and behavioral data that allow us to gain more insight on team performance as well as develop a method to evaluate adaptation and performance across and within a group. To make this problem more tractable we chose to focus on specific types of environments, Alternate Reality Games (ARGs), and for several reasons. First, these types of games involve setups that are similar to a real-world setup, e.g., communication through slack or email. Second, they are more controllable than real environments allowing us to embed stimuli if needed. Lastly, they allow us to collect data needed to understand decisions and communications made through the entire duration of the experience, which makes team processes more transparent than otherwise possible. In this report we discuss the work we did so far and demonstrate the efficacy of the approach.

Sabbir Ahmad, Andy Bryant, Erica Kleinman et al.

Modeling players' behaviors in games has gained increased momentum in the past few years. This area of research has wide applications, including modeling learners and understanding player strategies, to mention a few. In this paper, we present a new methodology, called Interactive Behavior Analytics (IBA), comprised of two visualization systems, a labeling mechanism, and abstraction algorithms that use Dynamic Time Warping and clustering algorithms. The methodology is packaged in a seamless interface to facilitate knowledge discovery from game data. We demonstrate the use of this methodology with data from two multiplayer team-based games: BoomTown, a game developed by Gallup, and DotA 2. The results of this work show the effectiveness of this method in modeling, and developing human-interpretable models of team and individual behavior.

Erica Kleinman, Sabbir Ahmad, Zhaoqing Teng et al.

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) 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).

Magy Seif El-Nasr, Erica Kleinman

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 as well as work in the fields of AI and machine learning 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.