Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives
This addresses the time-consuming task of authoring learning objectives for instructors and curricular designers, though it is incremental as it applies an existing method to a new educational domain.
The study evaluated GPT-4's ability to automatically generate high-quality learning objectives for a university AI course, finding that 127 generated objectives were sensible, properly expressed, and aligned with Bloom's taxonomy levels.
We evaluated the capability of a generative pre-trained transformer (GPT-4) to automatically generate high-quality learning objectives (LOs) in the context of a practically oriented university course on Artificial Intelligence. Discussions of opportunities (e.g., content generation, explanation) and risks (e.g., cheating) of this emerging technology in education have intensified, but to date there has not been a study of the models' capabilities in supporting the course design and authoring of LOs. LOs articulate the knowledge and skills learners are intended to acquire by engaging with a course. To be effective, LOs must focus on what students are intended to achieve, focus on specific cognitive processes, and be measurable. Thus, authoring high-quality LOs is a challenging and time consuming (i.e., expensive) effort. We evaluated 127 LOs that were automatically generated based on a carefully crafted prompt (detailed guidelines on high-quality LOs authoring) submitted to GPT-4 for conceptual modules and projects of an AI Practitioner course. We analyzed the generated LOs if they follow certain best practices such as beginning with action verbs from Bloom's taxonomy in regards to the level of sophistication intended. Our analysis showed that the generated LOs are sensible, properly expressed (e.g., starting with an action verb), and that they largely operate at the appropriate level of Bloom's taxonomy, respecting the different nature of the conceptual modules (lower levels) and projects (higher levels). Our results can be leveraged by instructors and curricular designers wishing to take advantage of the state-of-the-art generative models to support their curricular and course design efforts.