AI-Assisted Adaptive Interface Design for Lowering Entry Barriers in Real-Time Strategy Games

Real-time strategy (RTS) games require players to make strategic decisions while executing numerous commands. This paper presents a qualitative synthesis of recent academic research on game accessibility, human–Artificial Intelligence (AI) assistance, interface design, and community content. Six design directions are examined against accessibility, strategic depth, long-term engagement, and practical value. A layered control model is proposed with beginner, traditional, and competitive profiles sharing the same strategic rules. Routine automation requires player approval and remains visible and reversible, while tutoring uses information available to the player. Cooperative practice, hybrid interactions, and community content are considered as supporting design contexts. A conceptual mission walkthrough examines how the model could separate repeated operations from consequential decisions. The analysis yields design requirements and testable hypotheses; no prototype or new participant experiment was conducted. Controlled player studies, including unassisted transfer tasks and return-session measures, are needed to determine whether the model lowers entry barriers while preserving strategic depth.