This study explores the utilisation of artificial intelligence (AI) tools, specifically ChatGPT, Claude 3, Perplexity, and Gemini, in developing tailored case studies for tertiary-level Food Science and Technology education focused on bread production. An iterative prompt engineering process involving prompt generation, refinement, and rating was employed to craft effective prompts for the AI platforms. These prompts covered desirable bread qualities, ingredients, production processes, and their respective functions. The AI platforms then generated case studies involving omissions or alterations to ingredients and process steps, mimicking real-world industry scenarios. The case study development process was evaluated using Gibbs’ reflective cycle, providing structured reflection on the experiences, feelings, evaluations, analysis, conclusions, and action plans related to leveraging AI for case study creation. Despite initial frustrations with prompt precision and variation in AI responses, the methodology enabled rapid generation of comprehensive case studies and rubrics that would otherwise require substantial time and effort. The study highlights the transformative potential of synergistically leveraging AI capabilities in cultivating an enriched, dynamic learning environment. AI-driven case studies can foster critical thinking, problem-solving proficiency, and applied knowledge among students. By incorporating Gibbs’ reflective cycle, the author gained insights into the challenges, thoughts, and lessons learned throughout the process. While demonstrating promise, gaps were identified concerning knowledge retention, comparative efficacy with traditional methods, and scalability. Future research should address these gaps through longitudinal studies, controlled trials, and expanded implementation across complex food industry topics.