“Who's Doing the Thinking Here?”: A Pedagogy-First Approach to Integrating Large Language Models in Higher Education

Large Language Models (LLMs), a subset of Generative Artificial Intelligence (GenAl), have been popularised through the widely publicised launch of tools such as ChatGPT. While capable of responding to human input in an apparently conversational manner, these models do not possess understanding in the same way that humans do and their outputs, based on probabilistic algorithms, are susceptible to presenting falsehoods as facts (hallucinations). Despite the risks, there are potential benefits for embedding LLMs in learning experiences if educators can cultivate a safe learning environment that supports students' interactions with LLMs while empowering students to develop their higher-order thinking skills (HOTS). Educators should be empowered to make well-informed decisions about the use of LLMs and help students work towards co-creation in collaboration with LLMs without compromising academic integrity or their own agency. In the context of GenAl, the apposite question has become “who's doing the thinking here?”, particularly with the prevalence of LLM outputs which are adept at masquerading as intelligent thought. Bloom's revised taxonomy is proposed as an effective lens through which educators can evaluate the use of LLMs in subject-specific learning contexts, particularly with a focus on developing students' HOTS. Some practical examples of prompts are included for educators to try as part of their own experimentation with GenAl. This paper aims to empower educators by offering an initial set of “Higher Order Prompting” principles to support the decision making of how - or indeed if - to use LLMs with students, while contributing to the ongoing discourse of how Generative AI is impacting Higher Education.