This paper explores how an AI model might aid pilots facing time-sensitive, multi-criteria decision-making challenges, focusing on the dynamic alternate airport selection problem. Traditional decision-making methods from the literature are ill suited in time-constrained, stressful situations. This has prompted an exploration into how incorporating AI models might provide decision-makers, pilots in this case, recommendations in such predicaments. Within the paper we explore how a Learning Classifier Systems (LCS), might be employed to tackle the problem. To train the LCS, an augmented dataset is derived from an online survey study featuring scenarios simulating alternate airport decision-making problems where state variables, reflecting aircraft conditions, and three airport options were presented to pilots. The LCS system showed promising results and appears to be a suitable model for the task.