Pedestrian dynamics models have provided valuable insights into pedestrian interactions, collision avoidance, and self-organized crowd behavior using mathematical, computational, AI-based, and heuristic approaches. However, existing models often fail to capture fundamental aspects of human decision-making, particularly the tendency to adopt indirect routes by sequentially selecting intermediate goals within the line of sight. In this study, we propose a novel Variable Goal Approach (VGA) that integrates human intelligence into pedestrian dynamics models by introducing multiple intermediate goals, termed variable goals, which guide pedestrians toward their final destination. These variable goals function as an adaptive guidance mechanism, enabling smoother transitions and dynamic navigation. VGA also enhances the efficiency of a model while minimizing interactions and disruptions. By strategically positioning variable goals, VGA introduces an element of stochasticity. This allows the model to simulate varied pedestrian paths under identical conditions, reflecting the diversity in human decision-making. In addition to its effectiveness in simple scenarios, VGA demonstrates strong performance in replicating high-density scenarios, such as lane formation, providing results that closely match real-world data.