Appealing, but misleading: a warning against a naive AI realism

Abstract

Scientists, educators, and instructional designers are facing numerous challenges due to the introduction of generative AI tools that can create appealing realistic imagery based on text prompts. Given that realism contributes to the trustworthiness of images coupled with people’s eagerness to externalize complex tasks to AI systems, the problem of a naive AI realism arises in which image creation and optimization is offloaded without considering the limitations of AI-driven technology. However, scientific visualizations and images used for educational purposes must go beyond an appealing presentation; above all, they should be accurate and factually correct. We argue that the utilization of generative AI tools for these types of visualizations requires human oversight, subject matter knowledge, and knowledge of effective design. In particular, we warn of a naive adoption of technological possibilities to “optimize” visualizations for educational purposes, such as memorability. A reductionist and naive view of AI-based optimization that fails to take into account the complex prerequisites for learning and instruction is likely to have negative consequences.