LeagueBot: A Voice LLM Companion of Cognitive and Emotional Support for Novice Players in Competitive Games

2026-02-01

AI Summary

This study addresses the high attrition rates among novice players in highly competitive games like League of Legends, where steep learning curves and social pressures often lead to frustration and disengagement. To mitigate these challenges, this work proposes LeagueBot—the first voice-driven large language model (LLM) chatbot designed specifically for competitive gaming environments. LeagueBot delivers context-aware informational support and emotional companionship during live matches by dynamically integrating real-time game states with conversational interaction. Notably, it represents the first implementation of a voice-based LLM capable of delivering coordinated cognitive and affective interventions in competitive gameplay. Experimental results demonstrate that LeagueBot significantly reduces novices’ cognitive load, performance anxiety, and stress levels, while simultaneously enhancing their information acquisition efficiency and psychological resilience.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• competitive games
• novice players
• learning curve
• social pressure
• player engagement

Innovation

• voice-based LLM
• real-time emotional support
• cognitive assistance
• competitive gaming
• context-aware interaction