PERC 2024 Abstract Detail Page
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| Abstract Title: | Strategies to learn with Large Language Models in Physics Education |
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| Abstract Type: | Symposium Talk |
| Abstract: | Large language models (LLMs), like ChatGPT, have significantly advanced various fields, offering automated, coherent responses to complex inputs, including essay writing, programming, and zero-shot learning. These models, trained on vast datasets, promise substantial benefits in physics education. However, there are several challenges such as the controllability of the output, interpretability, and ethical concerns, including bias and potential misuse. In physics education, it is crucial for students to critically evaluate ChatGPT's output for correctness and biases, possibly using efficient prompting strategies, such as chain of thought prompting or few-shot learning, to enhance the accuracy of the output. This study explores how chatbots based on an LLM can be brought into 9th and 10th grade classrooms. Therefore, we investigated how students (N=114) from these grades tackle physics problems using ChatGPT. Using a pre-post design, we compared the problem-solving process of students taught a prompting strategy (intervention group 1) against those without such instruction (intervention group 2) and a control group learning from worked examples. The study aimed to determine if and how instructional strategies could mitigate issues when ChatGPT generates incorrect responses. We specifically designed physics problems to challenge ChatGPT, providing a control window for students to verify answers and learn from the model's outputs. The findings reveal significant challenges in using ChatGPT for physics problem-solving when immediate corrections are needed. The results are analyzed within a theoretical framework involving Generative Artificial Intelligence, highlighting the importance of strategic prompting techniques to improve students' learning outcomes with AI tools. |
| Parallel Session: | Applications, Opportunities and Challenges of Large Language Models in Physics Education |
Author/Organizer Information | |
| Primary Contact: |
Stefan Küchemann Ludwig-Maximilians-Universität München, Germany |
| Co-Author(s) and Co-Presenter(s) |
Jochen Kuhn |
