Can You Help ChatGPT Get an “A” in Organic Chemistry? Teaching Effective Prompting of Large Language Models for Reaction Prediction

Abstract

As generative artificial intelligence (AI) tools such as large language models (LLMs) become widespread, they are increasingly finding applications in chemical sciences. Although LLMs have achieved impressive performance in many chemistry tasks, optimal performance requires proper use, including appropriate prompting techniques. Chemistry students are not generally taught strategies for effective LLM usage, especially for nonwriting tasks. Here we report an activity that introduces organic chemistry students to the use of LLMs such as ChatGPT for predicting the outcome of chemical reactions, specifically the types of alkene addition reactions taught in introductory organic chemistry courses. This activity exposes students to molecular representations, digitization of chemical reactions, train-test splitting practices for evaluating performance, and generalizable LLM prompting strategies, namely, the Five “S” prompt-writing approach and in-context learning. We tested this activity with chemistry students in the USA and in Austria and evaluated the activity through anonymous pre- and postlab surveys. Survey data revealed that students felt that they achieved their learning goals and that the activity was enjoyable. As chemistry students will inevitably interact with LLMs in their future careers, it is important to teach best practices for the effective and critical use of these tools in the context of chemistry.

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