Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring
  of Learning Objectives
Pragnya Sridhar Aidan Doyle Arav Agarwal Christopher Bogart Jaromir Savelka Majd Sakr
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.