Development and Assessment of an Adaptive Difficulty Snake Game in Python

Adaptive difficulty systems have been widely explored in game design to maintain player engagement by dynamically adjusting challenge levels based on player performance. Prior work has focused primarily on complex or commercial games, often using machine learning or rule-based systems to tailor game play experiences. However, limited research exists on implementing such systems in simple, classic games like Snake, particularly within lightweight, educational environments using accessible tools such as Python. This paper presents the design and evaluation of a Python-based Snake game featuring a real time adaptive difficulty mechanism. Our goal is to investigate how adaptive difficulty affects user engagement and performance in a familiar, minimalist gaming context. We contribute a modular framework for implementing difficulty scaling based on player input and behavior, and we assess its impact through user testing and behavioral analysis. The results aim to inform future educational and casual game designs where engagement is critical and resources are constrained.