Introduction. The article explores the application of artificial intelligence (AI) in instructional design for shaping educational outcomes based on Bloom's and SOLO taxonomies. The study aim of the work is to develop the structure of a textual prompt and, based on it, an algorithm for generating adaptive practical cases that enable a combination of standardized assessment with personalized learning. The theoretical foundation of the research builds upon classical pedagogical taxonomies integrated with modern AI technologies. Materials and methods. The authors propose a prompt-engineering method that automates the creation of differentiated tasks tailored to students' cognitive levels and ensures their evaluation within a scoring-rating system. The method of prompt engineering enables exploring the possibilities of applying AI in the digitalization of instructional design. As a key example, the discipline “Custom Apparel Design for Enterprises of the Ivanovo Region” is examined: the authors demonstrate how AI generates tiered assignments (e.g., developing specialized clothing considering regional production requirements or designing eco-sustainable systems based on circular economy principles). Results. The study highlights that the proposed algorithm automates not only case creation but also their assessment, as evidenced by an experiment with six student submissions, where AI and instructor evaluations showed an 85% match rate.