educational game designers

Abstract: Does persistent memory accumulated from previous attempts help an LLM agent solve new problems, and does memory design affect that benefit? We study programmatic Chinese-character drawing across five agent conditions attempting the same 668 items. Accumulated memory produces modest aggregate gains despite substantial growth in the memory stores: free-form notes improve first-attempt success by 4.6 percentage points, while a design combining free-form memory with reusable code improves it by 2.8 points. The two designs do not differ significantly overall, but the code bank shows a suggestive advantage on later, harder items. A separate post-hoc comparison finds a much larger improvement when the code-bank agent receives privileged, target-specific structural supervision. The agent can therefore exploit highly informative structural guidance, although the privileged per-item information does not establish what could have been learned from experience alone. These results suggest that memory design may affect where accumulated experience becomes useful, even when its average benefit is modest.

Abstract: Children are increasingly interacting with AI agents in learning and problem-solving contexts, where AI suggestions may appear fluent, helpful, and socially engaging. However, children may not always be able to judge whether such suggestions are accurate, incomplete, or misleading. This paper presents a game-based human-AI collaboration probe designed to examine how children verify AI suggestions during task-based interaction. The system embeds AI suggestions within a biological knowledge reasoning game and compares three Agent interaction styles: partner-like, tool-like, and dual-state Agent. Instead of treating trust as a static post-task attitude, this study focuses on children's situated verification behaviors, including acceptance, evidence checking, questioning, revision, and continued AI use. A preliminary evaluation with children aged 8-10 combined interaction logs, post-task ratings, and semi-structured interviews. The findings suggest that Agent style shaped children's engagement and response pathways: partner-like interaction tended to support direct questioning, while tool-like interaction was more associated with evidence seeking. Children generally viewed AI as useful but fallible, and their questioning behaviors varied in reasoning depth. The study contributes design insights for supporting children's critical, reflective, and evidence-based use of AI in collaborative tasks.

Abstract: As part of learning to play complex games, human players develop develop abstractions for concepts and strategies of gameplay consistent with game rules to improve their performance. These concepts are applied to explain other players' actions, and to inform their own actions in-game. Understanding other players' strategies is a crucial part of such improvement, but requires time and effort. In this paper, we propose a strategy similar to human cognition for training RL agents to synthesize learned strategies and policies as executable procedures based on sequences of gameplay actions. We present methods to automatically learn such programs to play chess and to solve tasks in a grid-based environment. We show that the learned strategies produce effective actions, and can be learned from gameplay data.