Imagining the Past

Generative AI Prompting, Source Sets, and the TAP(E) Protocol

Abstract

This paper explores the potential of AI image generation as a creative learning tool to enhance traditional source analysis in history education. By integrating AI generated visualizations into the interpretive process, students can unlock new narratives and perspectives, furthering their understanding of historical dispositions. The proposed assignment and protocol guide students in crafting prompts that synthesize descriptive details from source materials, critically evaluating AI capabilities and limitations, and iteratively refining their outputs. This process fosters the development of essential skills such as contextual analysis, ethical reasoning, and an understanding of how data inputs shape AI-generated outcomes.

Furthermore, the assignment encourages students to interrogate the credibility and responsible production of AI images, situating these visualizations within broader discourses on innovation ethics, human experience, and desirable technological change. By engaging with shared readings and reflecting on AI's societal impacts, students contribute to the training of algorithms that produce more equitable representations of humanity's diversity while respecting educational objectives and cultural sensitivities.

The intersection of AI image generation and history education cultivates an invaluable combination of interpretive analysis, creative expression, and critical thinking about society and progress. This approach positions students as both creators and critics of AI imagination, fostering digital literacies and equipping them to become ethical, innovative AI citizens. As AI capabilities rapidly evolve, exercises like this keep humans in the loop and contribute to the ongoing exploration of whether AI-generated images can challenge colonial or neocolonial biases by incorporating diverse perspectives and imagining an inclusive past.