Human-Computer Interaction practitioners have been proposing best practices in user interface design for decades. However, generative Artificial Intelligence (GenAI) brings additional design considerations and currently lacks sufficient user guidance regarding affordances, inputs, and outputs. In this context, we developed a recommender system to promote responsible AI (RAI) practices while people prompt GenAI systems. We detail 10 interviews with IT professionals, the resulting recommender system developed, and 20 user sessions with IT professionals interacting with our prompt recommendations. Results indicate that responsible prompting recommendations have the potential to support novice prompt engineers and raise awareness about RAI in prompting-time. They also suggest that recommendations should simultaneously maximize both a prompt’s similarity to a user’s input as well as a diversity of associated social values provided. These findings contribute to RAI by offering practical ways to provide user guidance and enrich human-GenAI interaction via prompt recommendations.

Human-Computer Interaction practitioners have been proposing best practices in user interface design for decades. However, generative Artificial Intelligence (GenAI) brings additional design considerations and currently lacks sufficient user guidance regarding affordances, inputs, and outputs. In this context, we developed a recommender system to promote responsible AI (RAI) practices while people prompt GenAI systems, by recommending addition of sentences based on social values and removal of harmful sentences. We detail a lightweight recommender system designed to be used in prompting-time and compare its recommendations to the ones provided by three base large language models (LLMs) and two LLMs fine-tuned for the task, i.e., recommending inclusion of sentences based on social values and removal of harmful sentences from a given prompt. Results indicate that our approach has the best F1-score balance in terms of recommendations for additions and removal of sentences to promote responsible prompts, while a fine-tuned model obtained the best F1-score for additions, and our approach obtained the best F1-score for removals of harmful sentences. In addition, fine-tuned models improved the objectiveness of responses by reducing the verbosity of generated content in 93% when compared to the content generated by base models. Presented findings contribute to RAI by showing the limits and bias of existing LLMs in terms of recommendations on how to create more responsible prompts and how open-source technologies can fill this gap in prompting-time.

In this article, we introduce the concept of procedural content generation via knowledge transformation (PCG-KT), a new lens and framework for characterizing PCG methods and approaches in which content generation is enabled by the process of knowledge transformation: transforming knowledge derived from one domain in order to apply it in another. Our work is motivated by a substantial number of recent PCG works that focus on generating novel content via repurposing derived knowledge. Such works have involved, for example, performing transfer learning on models trained on one game's content to adapt to another game's content, as well as recombining different generative distributions to blend the content of two or more games. Such approaches arose in part due to limitations in PCG via machine learning, such as producing generative models for games lacking training data and generating content for entirely new games. In this article, we categorize such approaches under this new lens of PCG-KT by offering a definition and framework for describing such methods and surveying existing works using this framework. Finally, we conclude by highlighting open problems and directions for future research in this area.

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.

We present a new method of automatic critical mechanic discovery for video games using a combination of game description parsing and playtrace information. This method is applied to several games within the General Video Game Artificial Intelligence (GVG-AI) framework. In a user study, human-identified mechanics are compared against system-identified critical mechanics to verify alignment between humans and the system. The results of the study demonstrate that the new method is able to match humans with higher consistency than baseline. Our system is further validated by comparing MCTS agents augmented with critical mechanics and vanilla MCTS agents on 4 games from GVG-AI. Our new playtrace method shows a significant performance improvement over the baseline for all 4 tested games. The proposed method also shows either matched or improved performance over the old method, demonstrating that playtrace information is responsible for more complete critical mechanic discovery.

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.

Recommender Systems are widely and successfully applied in e-commerce. Could they be used for designƒ In this paper, we introduce Pitako1, a tool that applies the Recommender System concept to assist humans in creative tasks. More specifically, Pitako provides suggestions by taking games designed by humans as inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented as a new system within the mixed-initiative AI-based Game Design Assistant, Cicero. This paper discusses the motivation behind the implementation of Pitako as well as its technical details and presents usage examples. We believe that Pitako can influence the use of recommender systems to help humans in their daily tasks.

We present Cicero, a mixed-initiative application for prototyping two-dimensional sprite-based games across different genres such as shooters, puzzles, and action games. Cicero provides a host of features which can offer assistance in different stages of the game development process. Noteworthy features include AI agents for gameplay simulation, a game mechanics recommender system, a playtrace aggregator, heatmap-based game analysis, a sequential replay mechanism, and a query system that allows searching for particular interaction patterns. In order to evaluate the efficacy and usefulness of the different features of Cicero, we conducted a user study in which we compared how users perform in game debugging tasks with different kinds of assistance.

This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (Automatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.

This paper describes the design of SeekWhence, a retrospective analysis tool for gameplay session. SeekWhence is a new addition to the Cicero AI-assisted game design tool, which is built on top of the Video Game Description Language (VGDL) and the General Video Game Framework (GVG-AI). With SeekWhence, designers can prototype their games and record gameplay sessions simulated by agents or human players. They can go back and forth on every frame of the recorded session, analyzing it step by step and import it into their current project to edit it. This paper explains the technical details of SeekWhence and gives examples of its usage.