Aerospace Engineering Education in the Era of Generative AI

The proliferation of large-language model (LLM) generative artificial intelligence (AI) tools like ChatGPT raises an inevitable question of how it should impact student assessments in aerospace engineering. To evaluate this, a large sample of multiple-choice questions from undergraduate aerodynamics and aeronautics courses were input into ChatGPT-4 and Gemini and the accuracy evaluated. The cognitive level of each question was coded using Bloom’s taxonomy based on consensus of the authors. It was found that that generative AI performs increasingly poorly as the cognitive level is increased. Chi-square analyses of the data show very strong association with ChatGPT and strong association with Gemini for these trends. Cursory analysis of questions where both tools gave different wrong answers are consistent with the pattern matching aspects of LLMs. Based on the authors’ observations, recommendations are offered for writing multiple choice questions that actually assess human understanding.