将大模型融入游戏化系统，提升用户参与度与个性化体验
Integrating LLMs in Gamified Systems
- 用大模型实现个性化反馈与动态内容生成
- 模拟环境验证框架可显著提升用户留存率
- 适合教育、医疗、商业等需持续激励的场景
本文提出一个严谨的数学框架，将大语言模型（LLMs）融入游戏化系统，重点优化任务机制、用户参与度和奖励设计。通过整合大模型，系统可实现个性化反馈、自适应学习和动态内容生成，显著提升用户参与度与系统性能。在模拟环境中测试表明，该框架具备良好适应性，具备在商业、医疗、教育等领域的实际应用潜力。研究结果证明，大模型能提供定制化体验，有效提高系统效率与用户留存。同时，论文分析了当前系统面临的挑战，强调该框架在促进持续行为改变中的重要价值。
原文摘要 · Abstract (English)
In this work, a thorough mathematical framework for incorporating Large Language Models (LLMs) into gamified systems is presented with an emphasis on improving task dynamics, user engagement, and reward systems. Personalized feedback, adaptive learning, and dynamic content creation are all made possible by integrating LLMs and are crucial for improving user engagement and system performance. A simulated environment tests the framework's adaptability and demonstrates its potential for real-world applications in various industries, including business, healthcare, and education. The findings demonstrate how LLMs can offer customized experiences that raise system effectiveness and user retention. This study also examines the difficulties this framework aims to solve, highlighting its importance in maximizing involvement and encouraging sustained behavioral change in a range of sectors.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。