Prompt engineering in higher education: a systematic review to help inform curricula.
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| Title: | Prompt engineering in higher education: a systematic review to help inform curricula. |
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| Authors: | Lee, Daniel1 (AUTHOR) daniel.a.lee@adelaide.edu.au, Palmer, Edward1 (AUTHOR) |
| Source: | International Journal of Educational Technology in Higher Education. 2/10/2025, Vol. 22 Issue 1, p1-22. 22p. |
| Subject Terms: | *Generative artificial intelligence, *Engineering education, *Educational objectives, *Educational outcomes, *Higher education |
| Abstract: | This paper presents a systematic review of the role of prompt engineering during interactions with Generative Artificial Intelligence (GenAI) in Higher Education (HE) to discover potential methods of improving educational outcomes. Drawing on a comprehensive search of academic databases and relevant literature, key trends, including multiple framework designs, are presented and explored to review the role, relevance, and applicability of prompt engineering to purposefully improve GenAI-generated responses in higher education contexts. Multiple experiments using a variety of prompt engineering frameworks are compared, contrasted and discussed. Analysis reveals that well-designed prompts have the potential to transform interactions with GenAI in higher education teaching and learning. Further findings show it is important to develop and teach pragmatic skills in AI interaction, including meaningful prompt engineering, which is best managed through a well-designed framework for creating and evaluating GenAI applications that are aligned with pre-determined contextual educational goals. The paper outlines some of the key concepts and frameworks that educators should be aware of when incorporating GenAI and prompt engineering into their teaching practices, and when teaching students the necessary skills for successful GenAI interaction. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature 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: | Education Research Complete |
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