Competitive games pose steep learning curves and strong social pressures, often discouraging novice players and limiting sustained engagement. To address these challenges, this study introduces LeagueBot, a large language model-based voice chatbot designed to provide both informational and emotional support during live gameplay in league of legends, one of the most competitive multiplayer online battle arena games. In a within-subjects experiment with 33 novice players, LeagueBot was found to reduce cognitive challenge, performative challenge, and perceived tension. Qualitative analysis further identified three themes: enhanced access to game information, relief from cognitive burden, and practical limitations. Participants noted that LeagueBot offered context-appropriate guidance and emotional support, helping ease the steep learning curve and psychological pressures of competitive gaming. Together, these findings underscore the potential of voice-based LLM companions to assist novice players in competitive environments and highlight their broader applicability for real-time support in other high-pressure contexts.

翻译：竞技游戏通常存在陡峭的学习曲线和强烈的社交压力，这常常使新手玩家感到气馁，并限制了他们的持续参与度。为应对这些挑战，本研究引入了LeagueBot，一个基于大语言模型的语音聊天机器人，旨在为《英雄联盟》（最具竞争力的多人在线战术竞技游戏之一）的实时游戏过程提供信息和情感支持。在一项涉及33名新手玩家的组内实验中，发现LeagueBot能够降低认知挑战、表现挑战和感知压力。定性分析进一步识别出三个主题：增强游戏信息获取、缓解认知负担以及实际局限性。参与者指出，LeagueBot提供了情境适宜的指导和情感支持，有助于缓解竞技游戏中陡峭的学习曲线和心理压力。综上所述，这些发现强调了基于语音的大语言模型伴侣在竞技环境中协助新手玩家的潜力，并凸显了它们在其他高压情境下提供实时支持的更广泛适用性。