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.

As new data privacy regulations like GDPR (General Data Protection Regulation, 2017) have come into effect, customers are under increased pressure to monetize media assets while abiding by the new rules. Monetizing media while respecting privacy regulations requires the ability to automatically extract granular metadata from assets like text, images, video, and audio files at internet scale. It also requires a scalable way to map media assets to industry taxonomies that facilitate discovery and monetization of content. This use case is particularly significant for the advertising industry as data privacy rules cause a shift from behavioral targeting using third-party cookies. Third-party cookies help enable personalized ads for web users, and allow advertisers to reach their intended audience.