Enhancing AI Responses in Chemistry: Integrating Text Generation, Image Creation, and Image Interpretation through Different Levels of Prompts

Abstract

Generative Artificial Intelligence technologies can potentially transform education, benefiting teachers and students. This study evaluated various GAIs, including ChatGPT 3.5, ChatGPT 4.0, Google Bard, Bing Chat, Adobe Firefly, Leonardo.AI, and DALL-E, focusing on textual and imagery content. Utilizing initial, intermediate, and advanced prompts, we aim to simulate GAI responses tailored to users with varying levels of knowledge. We aim to investigate the possibilities of integrating content from Chemistry Teaching. The systems presented responses appropriate to the scientific consensus for textual generation, but they revealed alternative chemical content conceptions. In terms of the interpretation of chemical system representations, only ChatGPT 4.0 accurately identified the content in all of the images. In terms of image production, even with more advanced prompts and subprompts, Generative Artificial Intelligence still presents difficulties in content production. The use of prompts involving the Python language promoted an improvement in the images produced. In general, we can consider content production as support for chemistry teaching, but only with more advanced prompts do the answers tend to present fewer errors. The importance of previously understanding chemistry concepts and systems’ functioning is noted.