How understanding large language models can inform the use of ChatGPT in physics education

TL;DR

This paper introduces the physics education community to large language models like ChatGPT, demonstrating how prompt engineering can enhance their performance on physics tasks and discussing implications for teaching and learning.

Contribution

It provides an accessible overview of LLM functioning, illustrates prompt-engineering techniques for physics problems, and explores their potential in physics education.

Findings

Prompt engineering improves ChatGPT's accuracy on physics problems.

Understanding LLMs helps optimize their use in physics teaching.

ChatGPT shows strengths and limitations in conceptual physics tasks.

Abstract

The paper aims to fulfil three main functions: (1) to serve as an introduction for the physics education community to the functioning of Large Language Models (LLMs), (2) to present a series of illustrative examples demonstrating how prompt-engineering techniques can impact LLMs performance on conceptual physics tasks and (3) to discuss potential implications of the understanding of LLMs and prompt engineering for physics teaching and learning. We first summarise existing research on the performance of a popular LLM-based chatbot (ChatGPT) on physics tasks. We then give a basic account of how LLMs work, illustrate essential features of their functioning, and discuss their strengths and limitations. Equipped with this knowledge, we discuss some challenges with generating useful output with ChatGPT-4 in the context of introductory physics, paying special attention to conceptual questions and problems. We then provide a condensed overview of relevant literature on prompt engineering and demonstrate through illustrative examples how selected prompt-engineering techniques can be employed to improve ChatGPT-4's output on conceptual introductory physics problems. Qualitatively studying these examples provides additional insights into ChatGPT's functioning and its utility in physics problem solving. Finally, we consider how insights from the paper can inform the use of LLMs in the teaching and learning of physics.