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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.
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]
Copyright of Theory Into Practice is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Realizing the possibilities of the large language models: Strategies for prompt engineering in educational inquiries.
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  Data: <searchLink fieldCode="AR" term="%22Correia%2C+Ana-Paula%22">Correia, Ana-Paula</searchLink><br /><searchLink fieldCode="AR" term="%22Hickey%2C+Sean%22">Hickey, Sean</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Fan%22">Xu, Fan</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence+in+education%22">Artificial intelligence in education</searchLink><br /><searchLink fieldCode="DE" term="%22Prompt+engineering%22">Prompt engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Professional+education%22">Professional education</searchLink><br /><searchLink fieldCode="DE" term="%22Freedom+of+teaching%22">Freedom of teaching</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
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  Data: 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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  Data: <i>Copyright of Theory Into Practice is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00405841.2025.2528545
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        Type: general
      – SubjectFull: Prompt engineering
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      – SubjectFull: Professional education
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      – SubjectFull: Freedom of teaching
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      – SubjectFull: Prediction models
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              Text: Fall2025
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