The article examines how generative artificial intelligence (GenAI) influences science education imagery, focusing on the representations of science classrooms and educators. It explores the potential of GenAI to reinforce biases and stereotypes in science education, using DALL-E 3 and ChatGPT as examples. The study applies a cultural capital lens to analyze how these images portray forms of culture—embodied, objectified, and institutionalized—and whether they align with or challenge stereotypical representations of science education. The research highlights that GenAI-generated images often depict science classrooms in various settings, from vintage to contemporary, and include stereotypical elements such as white lab coats, goggles, and beakers. While these images may align with traditional stereotypes, they also introduce diversity in gender and ancestry. The study finds that GenAI-generated images tend to favor students from higher socioeconomic backgrounds, reinforcing the notion that science is accessible only to those with economic capital. However, the inclusion of diverse gender and cultural backgrounds in the images challenges traditional stereotypes and promotes inclusivity in science education. The analysis of science educators reveals that GenAI-generated images often depict them in traditional lab attire, reinforcing stereotypes of scientists as white and male. However, the images also show a diverse range of educators, including individuals of different ethnicities and genders, challenging the narrow stereotype of who can be a scientist. The study emphasizes the importance of critically examining GenAI-generated content to ensure it does not perpetuate biases and stereotypes in science education. The research underscores the need for ongoing vigilance regarding equity, representation, bias, and transparency in GenAI artifacts. It contributes to broader discussions about the impact of GenAI in reinforcing or dismantling stereotypes associated with science education. The study also introduces a methodological innovation by showcasing how to critically examine the depictions of educational settings and figures by GenAI technologies like DALL-E 3. This approach provides a novel lens for assessing the biases and stereotypes embedded within AI systems, making a significant contribution to the discourse on ethical AI development and its application in educational contexts. The findings highlight the limitations of the current GenAI model, including its inherent biases and the need for further research to address these issues.The article examines how generative artificial intelligence (GenAI) influences science education imagery, focusing on the representations of science classrooms and educators. It explores the potential of GenAI to reinforce biases and stereotypes in science education, using DALL-E 3 and ChatGPT as examples. The study applies a cultural capital lens to analyze how these images portray forms of culture—embodied, objectified, and institutionalized—and whether they align with or challenge stereotypical representations of science education. The research highlights that GenAI-generated images often depict science classrooms in various settings, from vintage to contemporary, and include stereotypical elements such as white lab coats, goggles, and beakers. While these images may align with traditional stereotypes, they also introduce diversity in gender and ancestry. The study finds that GenAI-generated images tend to favor students from higher socioeconomic backgrounds, reinforcing the notion that science is accessible only to those with economic capital. However, the inclusion of diverse gender and cultural backgrounds in the images challenges traditional stereotypes and promotes inclusivity in science education. The analysis of science educators reveals that GenAI-generated images often depict them in traditional lab attire, reinforcing stereotypes of scientists as white and male. However, the images also show a diverse range of educators, including individuals of different ethnicities and genders, challenging the narrow stereotype of who can be a scientist. The study emphasizes the importance of critically examining GenAI-generated content to ensure it does not perpetuate biases and stereotypes in science education. The research underscores the need for ongoing vigilance regarding equity, representation, bias, and transparency in GenAI artifacts. It contributes to broader discussions about the impact of GenAI in reinforcing or dismantling stereotypes associated with science education. The study also introduces a methodological innovation by showcasing how to critically examine the depictions of educational settings and figures by GenAI technologies like DALL-E 3. This approach provides a novel lens for assessing the biases and stereotypes embedded within AI systems, making a significant contribution to the discourse on ethical AI development and its application in educational contexts. The findings highlight the limitations of the current GenAI model, including its inherent biases and the need for further research to address these issues.