Advances in artificial intelligence (AI) techniques have resulted in immense breakthroughs in how well we can algorithmically play videogames. Yet, this increased investment in game-playing AI (GPAI) techniques has not translated into a tangible improvement in the game-playing experience for our players. This paper is inspired by the positive impact of accessibility modes in recent games as well as previous calls in games research literature to focus on player experience. Responding to these calls, we propose utilizing GPAI techniques not to beat the player, as is traditionally done, but to support them in fully experiencing the game. We claim that utilizing GPAI agents to help players overcome barriers is a productive way of repurposing the capabilities of these agents. We further contribute a design exercise to help developers explore the space of possible GPAI-driven assistance methods. This exercise helps developers discover types of challenges and ideate methods that vary in magnitude and assistance type. We first apply this design exercise to explore the design space of possible assistance methods for the action platformer game Celeste. We then implement two of the discovered methods that target different challenge types in a Unity clone of Celeste. Through this implementation, we discover several additional research questions we must answer before GPAI-driven assistance methods can be truly effective. We believe this research direction furthers the discussion on how to utilize GPAI in service of the player experience and also contributes to the creation of more inclusive games.