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Articles

Vol. 4 No. 1 (2025): Generative AI and education

Higher order prompting: Applying Bloom’s revised taxonomy to the use of large language models in higher education

DOI:
https://doi.org/10.21428/8c225f6e.0915c17e
Submitted
13 August 2026
Published
15-08-2026

Abstract

Large Language Models (LLMs) and Generative AI (GenAI) tools have been popularised through the widely publicised launch of tools such as ChatGPT by OpenAI and Copilot by Microsoft. While being able to generate seemingly humanlike language and respond to human input in an apparently conversational manner, these tools are not able to understand problems in the same way that humans do, and their outputs are based on probabilistic algorithms and are susceptible to hallucinations, even with extensive training. As with any tool, LLMs can be used poorly and even unethically, but there are benefits to be gained through comprehension of how LLMs work and how prompt engineering can drastically improve the outputs from LLMs. This paper builds on existing research in the field of prompt engineering while engaging with the ongoing discourse of how AI is impacting higher education and wider society. Utilising Bloom’s revised taxonomy of learning as a foundation, a theoretical model is proposed for the purpose of helping educators analyse and evaluate what types of LLM usage may be appropriate within subject-specific learning experiences. Practical examples of prompts are provided as a starting point for academics and students to use and extend as part of their own experimentation with GenAI tools. The intent of this pedagogy-first approach to the use of emerging AI technologies in education will empower students to develop their higher order thinking skills (HOTS) and help them work towards co-creation in collaboration with LLMs without compromising academic integrity or their own agency.

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