This book chapter investigates the integration of generative AI, specifically ChatGPT, in delivering individualized responsive feedback for pre-service teachers. Through a comprehensive literature review and the development of an initial conceptual framework, we explored the practical application of prompt engineering in two detailed case studies. This process led to the formulation of an improved theoretical framework that incorporates student input, the creation of structured prompts, iterative self-reflection processes, and the generation of individualized responsive feedback. The study’s feedback was task-specific, personalized to individual assignments where students reflected on how their identities, worldviews, and cognitive biases shaped their learning and future teaching practices. Although the exact prompts may vary with different educational tasks, the underlying structure and prompt tuning procedures are versatile and transferable across various contexts and subject areas. This flexibility underscores the framework’s applicability to diverse educational settings, ensuring relevance, and adaptability. The findings emphasize the essential roles of students, researchers, and particularly instructors in guiding ChatGPT to produce feedback that is not only informative and relevant but also empathetic and personalized. This study contributes significantly to the field of educational technology by presenting a robust framework that combines advanced AI capabilities with critical human oversight, facilitating the delivery of high-quality, individualized feedback in higher education.