ChatGPT Leads the Pack: AI Language Models Elevating Chemistry Education

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Conclusions In our study evaluating the proficiency of four AI language models—ChatGPT, Bing Chat, Bard, and Claude—in elucidating chemistry concepts, ChatGPT stood out, decisively surpassing Bing Chat's performance. Bard and Claude trailed closely, with all three showcasing a more in- depth, precise, and nuanced understanding, underscoring ChatGPT's adeptness at contextual comprehension. Our findings highlight the robustness of GenAIbots like ChatGPT, Bing Chat, Bard, and Claude as agents-to-think-with in Chemistry education. These AI models seem suitable to offer interactivity, customisation, and multidisciplinary knowledge and promote critical thinking, problem-solving, creativity, and collaboration. They facilitate dynamic conversations and tailored content in Chemistry and potentially other domains. Engaging with GenAIbots might enhance critical thinking, problem-solving, and creativity, fostering collaborative discussions and projects in group settings and deepening understanding of the topics. By integrating GenAIbots into Chemistry education, we're aligning with the principles of Papert's Constructionism. This theory posits that learning is most effective when students actively construct knowledge by creating tangible representations in meaningful contexts. This approach encourages students to engage deeply, fostering their problem-solving, critical thinking, and creative skills. This paper is under CC BY-SA 4.0 DEED license. available on arxiv