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

arXiv: https://arxiv.org/abs/2410.11123 · HTML: https://mlsanity.com/paper/2410.11123/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 80 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://mlsanity.com/paper/2305.14536/) (2023)
- [EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education](https://mlsanity.com/paper/2308.02773/) (2023)
- [FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability](https://mlsanity.com/paper/2402.18667/) (2024)
- [The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues](https://mlsanity.com/paper/2205.07540/) (2022)
- [Applying Large Language Models and Chain-of-Thought for Automatic Scoring](https://mlsanity.com/paper/2312.03748/) (2023)
- [Automatic Generation of Socratic Subquestions for Teaching Math Word Problems](https://mlsanity.com/paper/2211.12835/) (2022)
- [Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and the Learning Sciences](https://mlsanity.com/paper/2405.04645/) (2024)
- [Evaluating ChatGPT's Decimal Skills and Feedback Generation in a Digital Learning Game](https://mlsanity.com/paper/2306.16639/) (2023)
- [Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level](https://mlsanity.com/paper/2309.08182/) (2023)
- [Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding](https://mlsanity.com/paper/2401.12983/) (2024)
- [Evaluating ChatGPT and GPT-4 for Visual Programming](https://mlsanity.com/paper/2308.02522/) (2023)
- [Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education](https://mlsanity.com/paper/2310.12059/) (2023)

## Built on by (most cited first, 8 of 8)

- [MDK12-Bench: A Multi-Discipline Benchmark for Evaluating Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2504.05782/) (2025)
- [Does the Prompt-based Large Language Model Recognize Students' Demographics and Introduce Bias in Essay Scoring?](https://mlsanity.com/paper/2504.21330/) (2025)
- [VTutor: An Animated Pedagogical Agent SDK that Provide Real Time Multi-Model Feedback](https://mlsanity.com/paper/2505.06676/) (2025)
- [Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on a Math Textbook](https://mlsanity.com/paper/2509.16780/) (2025)
- [Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design](https://mlsanity.com/paper/2508.16659/) (2025)
- [Chat-Based Support Alone May Not Be Enough: Comparing Conversational and Embedded LLM Feedback for Mathematical Proof Learning](https://mlsanity.com/paper/2602.18807/) (2026)
- [Temporal Stability and Few-Shot Prompting in Math Task Assessment](https://mlsanity.com/paper/2605.30151/) (2026)
- [Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution](https://mlsanity.com/paper/2608.01366/) (2026)

## Abstract (arXiv)

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 of LLMs combined with prompt engineering in K-12 STEM education. Following the PRISMA protocol, we screened 2,654 papers and selected 30 studies for analysis. Our review identifies the prompting strategies employed, the types of LLMs used, methods of evaluating effectiveness, and limitations in prior work. Results indicate that while simple and zero-shot prompting are commonly used, more advanced techniques like few-shot and chain-of-thought prompting have demonstrated positive outcomes for various educational tasks. GPT-series models are predominantly used, but smaller and fine-tuned models (e.g., Blender 7B) paired with effective prompt engineering outperform prompting larger models (e.g., GPT-3) in specific contexts. Evaluation methods vary significantly, with limited empirical validation in real-world settings.