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1. "Superstition" in the Network: Deep Reinforcement Learning Plays Deceptive Games

   Bontrager, Philip; Khalifa, Ahmed; Anderson, Damien; Stephenson, Matthew; Salge, Christoph; Togelius, Julian (October 2019, Fifteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   null (Ed.)

   Deep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we test the widely used Asynchronous Actor-Critic (A2C) algorithm on four deceptive games, which are specially designed to provide challenges to game-playing agents. These games are implemented in the General Video Game AI framework, which allows us to compare the behavior of reinforcement learning-based agents with planning agents based on tree search. We find that several of these games reliably deceive deep reinforcement learners, and that the resulting behavior highlights the shortcomings of the learning algorithm. The particular ways in which agents fail differ from how planning-based agents fail, further illuminating the character of these algorithms. We propose an initial typology of deceptions which could help us better understand pitfalls and failure modes of (deep) reinforcement learning. 

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2. Fostering AI Literacy Through Strategic Play: A Competitive Pathfinding Game for Middle School

   https://doi.org/10.1109/CoG64752.2025.11114117  

   Aguiar, Claire; Carpenter, Dan; Vandenberg, Jessica; Min, Wookhee; Catete, Veronica; Mott, Bradford (August 2025, IEEE)

   Recent years have seen a growing interest in the introduction of foundational AI concepts into K-12 education. Although game-based learning is widely used across educational domains, its potential to support students in developing core AI understandings remains an active area of exploration. This paper presents a competitive two-player game designed to foster AI literacy among middle school students through a focus on search algorithms. In the game, students take turns editing a shared grid-based environment to observe how their actions affect their chosen algorithm’s output and runtime. Through strategic gameplay and algorithm comparison, students engage in hands-on reasoning about pathfinding efficiency and decision making. To evaluate the game’s effectiveness, we conducted a classroom study with 40 middle school students and analyzed their responses to pre- and post-activity surveys. Results indicate increased understanding of pathfinding and high levels of engagement. While preliminary, these findings demonstrate a promising playful pathway to AI literacy and offer a foundation for future classroom-centered AI education. 

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3. Building Capacity for K-12 AI Education: A Non-Computer Science Teacher’s Experience

   https://doi.org/10.22318/icls2025.896881  

   Boulden, Danielle; Uchidiuno, Judith; Vandenberg, Jessica; Cateté, Veronica; Min, Wookhee; Mott, Bradford (June 2025, International Society of the Learning Sciences)

   This case study explores the experiences of a non-computer science educator participating in a professional development program designed to support AI teaching in rural middle schools. Using Cultural-Historical Activity Theory and expansive learning as analytical lenses, the research examines how the educator leveraged supportive elements within her environment to overcome challenges, gradually building confidence while adopting new teaching practices. Findings underscore the need for tailored professional development, ongoing support, and progressive teacher learning for effective AI education. This study contributes to understanding how non-computer science educators can be supported in bringing AI learning experiences to their students, thereby making AI education more accessible. 

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4. Approaching “Filter Bubble” in Recommendation Systems: A Transformative AI Literacy Learning Experience

   https://doi.org/10.22318/icls2024.921873  

   Gong, Yunfan; Zhou, Xiaofei; Zhou, Yushan; Luehmann, April; Han, Yujung; Bai, Zhen (June 2024, International Society of the Learning Sciences)

   Young learners today are constantly influenced by AI recommendations, from media choices to social connections. The resulting "filter bubble" can limit their exposure to diverse perspectives, which is especially problematic when they are not aware this manipulation is happening or why. To address the need to support youth AI literacy, we developed "BeeTrap", a mobile Augmented Reality (AR) learning game designed to enlighten young learners about the mechanisms and the ethical issue of recommendation systems. Transformative Experience model was integrated into learning activities design, focusing on making AI concepts relevant to students’ daily experiences, facilitating a new understanding of their digital world, and modeling real-life applications. Our pilot study with middle schoolers in a community-based program primarily investigated how transformative structured AI learning activities affected students’ understanding of recommendation systems and their overall conceptual, emotional, and behavioral changes toward AI. 

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5. Rainbow Agents: A Collaborative Game For Computational Literacy

   https://doi.org/10.1145/3341215.3356312  

   Pellicone, Anthony; Lyons, Leilah; Kumar, Vishesh; Zhang, Eda; Berland, Matthew (October 2019, CHI:PLAY '19)

   This paper describes the design of a collaborative game, called Rainbow Agents, that has been created to promote computational literacy through play. In Rainbow Agents, players engage directly with computational concepts by programming agents to plant and maintain a shared garden space. Rainbow Agents was designed to encourage collaborative play and shared sense-making from groups who are typically underrepresented in computer science. In this paper, we discuss how that design goal informed the mechanics of the game, and how each of those mechanics affords different goal alignments towards gameplay (e.g. competitive versus collaborative). We apply this framework using a case from an early implementation, describing how player goal alignments towards the game changed within the course of a single play session. We conclude by discussing avenues of future work as we begin data collection in two heavily diverse science museum locations. 

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