Oops, Not Now: PEARL, a RAG-Based Support Agent for Gameplay and What Players Want from AI Help

Abstract:AI-powered gameplay support agents hold promise for game-based learning, yet grounding generative models in structured game data remains an open challenge. We present PEARL (Parallel Education Agent for Reflection and Learning), a dual-component Retrieval-Augmented Generation (RAG) system that combines semantic knowledge retrieval with structural board-state matching to deliver contextualized scaffolding in Parallel, a puzzle game for learning parallel programming. PEARL operates on two input streams (natural language queries and board topology), retrieving both conceptual explanations of gameplay moves and peer-generated board states as evidence: capabilities unavailable to a standard Large Language Model (LLM) with game state access alone. In a qualitative evaluation (N=10) comparing PEARL against an existing community-based Open Player Model (OPM) visualization system, participants preferred the visualization system on perceived usefulness and reported higher frustration with PEARL; five of ten minimized or abandoned the AI tool during play. Proactive delivery, generic responses, and trust deficits drove disengagement, while a subset of four participants found PEARL's grounded explanations complementary to visualization in specific contexts where they initiated the interaction. We position PEARL as a deployed design probe whose failure modes inform a concrete design agenda for AI gameplay support, captured as seven open problems for the community.

Author accepted manuscript. Accepted to the 2026 IEEE Conference on Games (CoG). 8 pages, 3 figures, 1 table

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2609.13718 [cs.HC]

(or arXiv:2609.13718v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.13718 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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