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18-10-2023 | Original Paper

Prompting Change: Exploring Prompt Engineering in Large Language Model AI and Its Potential to Transform Education

Author: William Cain

Published in: TechTrends | Issue 1/2024

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Abstract

This paper explores the transformative potential of Large Language Models Artificial Intelligence (LLM AI) in educational contexts, particularly focusing on the innovative practice of prompt engineering. Prompt engineering, characterized by three essential components of content knowledge, critical thinking, and iterative design, emerges as a key mechanism to access the transformative capabilities of LLM AI in the learning process. This paper charts the evolving trajectory of LLM AI as a tool poised to reshape educational practices and assumptions. In particular, this paper breaks down the potential of prompt engineering practices to enhance learning by fostering personalized, engaging, and equitable educational experiences. The paper underscores how the natural language capabilities of LLM AI tools can help students and educators transition from passive recipients to active co-creators of their learning experiences. Critical thinking skills, particularly information literacy, media literacy, and digital citizenship, are identified as crucial for using LLM AI tools effectively and responsibly. Looking forward, the paper advocates for continued research to validate the benefits of prompt engineering practices across diverse learning contexts while simultaneously promoting potential defects, biases, and ethical concerns related to LLM AI use in education. It calls upon practitioners to explore and train educational stakeholders in best practices around prompt engineering for LLM AI, fostering progress towards a more engaging and equitable educational future.

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Title: Prompting Change: Exploring Prompt Engineering in Large Language Model AI and Its Potential to Transform Education
Author: William Cain
Publication date: 18-10-2023
Publisher: Springer US
Published in: TechTrends / Issue 1/2024
Print ISSN: 8756-3894
Electronic ISSN: 1559-7075
DOI: https://doi.org/10.1007/s11528-023-00896-0

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