Realizing the possibilities of the large language models: Strategies for prompt engineering in educational inquiries.
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| Title: | Realizing the possibilities of the large language models: Strategies for prompt engineering in educational inquiries. |
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| Authors: | Correia, Ana-Paula, Hickey, Sean, Xu, Fan |
| Source: | Theory Into Practice. Fall2025, Vol. 64 Issue 4, p434-447. 14p. |
| Subjects: | Artificial intelligence in education, Prompt engineering, Professional education, Freedom of teaching, Prediction models |
| Abstract: | This article examines the potential of Large Language Models (LLMs) to transform educational inquiries, emphasizing effective prompt engineering strategies. It begins by introducing LLMs and applications of generative AI tools in education. The article explores the fundamental principles of prompt engineering and LLMs, outlining strategies for educators to interact effectively with these models. It emphasizes the vital role of teacher-LLM interactions, highlighting the need for carefully crafted prompts to elicit high quality and pedagogically valuable responses. We discuss chain-of-thought prompting as a method for promoting deeper reasoning in LLM outputs. We offer other practical strategies for effective prompt engineering, providing actionable insights for teachers to enhance their professional practices. We also address ethical and practical considerations, including bias in AI responses and the importance of teacher autonomy and professional judgment. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | This article examines the potential of Large Language Models (LLMs) to transform educational inquiries, emphasizing effective prompt engineering strategies. It begins by introducing LLMs and applications of generative AI tools in education. The article explores the fundamental principles of prompt engineering and LLMs, outlining strategies for educators to interact effectively with these models. It emphasizes the vital role of teacher-LLM interactions, highlighting the need for carefully crafted prompts to elicit high quality and pedagogically valuable responses. We discuss chain-of-thought prompting as a method for promoting deeper reasoning in LLM outputs. We offer other practical strategies for effective prompt engineering, providing actionable insights for teachers to enhance their professional practices. We also address ethical and practical considerations, including bias in AI responses and the importance of teacher autonomy and professional judgment. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00405841 |
| DOI: | 10.1080/00405841.2025.2528545 |
