Creating high quality questions and answers for educational purposes continues to be a challenge for educators and publishers. Past attempts to address this through automatic generation have shown limited abilities to generate questions targeting high cognitive levels, control question complexity and difficulty, or create adequate question-answer pairs. We take first steps toward addressing these limitations by introducing a new approach, named Angel, informed by recent developments in Large Language Models and Generative AI. Relying on advanced prompting techniques, automatic curation, and the incorporation of educational theory into prompts, Angel focuses on generating question answer pairs of varied difficulty while targeting higher cognitive levels. Questions and answers are automatically generated based on a textbook extract, with Bloom Taxonomy serving as a guide to the creation of questions addressing a diverse set of learning objectives. Our experiments compare Angel to several baselines and demonstrate the potential of informed generative models to create high-quality question answer pairs that cover a diverse range of cognitive skills.