A Systematic Review on Prompt Engineering in Large Language Models for K-12 STEM Education

This systematic review analyzes 30 empirical studies (2021-2024) examining how prompt engineering techniques are applied to large language models in K-12 STEM education contexts, identifying common prompting strategies, model types, and evaluation methods used across teaching and learning applications.

Large language models (LLMs) have the potential to enhance K-12 STEM education by improving both teaching and learning processes. While previous studies have shown promising results, there is still a lack of comprehensive understanding regarding how LLMs are effectively applied, specifically through prompt engineering-the process of designing prompts to generate desired outputs. To address this gap, our study investigates empirical research published between 2021 and 2024 that explores the use o

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