Integrating LLMs in Gamified Systems

2025-03-14

AI Summary

This study addresses low user engagement, poor retention, and weak cross-domain adaptability in gamified systems by proposing the first LLM-driven gamification framework for mathematical modeling. Methodologically, it integrates stochastic process modeling with reinforcement learning to construct an interpretable task-dynamics evolution model and an adaptive reward mechanism, enabling personalized feedback, dynamic content generation, and behavioral intervention. Key contributions include: (1) establishing the first formal theoretical framework embedding LLMs deeply into gamification systems; (2) designing a transferable, industry-agnostic sustained motivation mechanism applicable across education, healthcare, and commerce; and (3) empirically validating—via simulation—a significant improvement in task completion rate (+32.7%) and 30-day retention (+28.4%), while rigorously identifying the causal effect of customized experiences on long-term behavioral change.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• 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
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Develops a framework for integrating LLMs into gamified systems
• Enhances user engagement through personalized feedback and adaptive learning
• Tests framework adaptability in simulated environments across multiple industries

Innovation

• Integrates LLMs into gamified systems
• Enhances user engagement via personalized feedback
• Tests framework adaptability in simulated environments