  The popularity of generative text AI tools in answering questions has led to
concerns regarding their potential negative impact on students' academic
performance and the challenges that educators face in evaluating student
learning. To address these concerns, this paper introduces an evolutionary
approach that aims to identify the best set of Bloom's taxonomy keywords to
generate questions that these tools have low confidence in answering. The
effectiveness of this approach is evaluated through a case study that uses
questions from a Data Structures and Representation course being taught at the
University of New South Wales in Canberra, Australia. The results demonstrate
that the optimization algorithm is able to find keywords from different
cognitive levels to create questions that ChatGPT has low confidence in
answering. This study is a step forward to offer valuable insights for
educators seeking to create more effective questions that promote critical
thinking among students.
