Pioneering AI-Driven Instructional Design in Small College Settings
At the same time, this wasn't an endeavor we entered into lightly. We were well aware of the concerns associated with AI use, including issues of bias and fairness, information accuracy, intellectual property, and pedagogy. There are valid philosophical critiques of AI, many of which I agree with, centering on what it means to be a human being, and the inherent value (and definition of) receiving a "quality education." And yet, as we've seen throughout modern history, this pattern of concern accompanying the advent of new technology is nothing new. In an ironic twist, it's this ingrained hypervigilance — the inherent fear of change — that defines us as human beings. It is a trait that is both a blessing and a vice; one we must accept and voluntarily make the decision to set aside, when warranted.
We knew that inaction was not the right approach; those players often find themselves playing catch-up years down the line, ultimately losing their innovative flavor. We also knew that we wanted to keep our students' best interests at the forefront. That's why we opted to take a responsible approach by piloting these tools on a small scale first, before attempting any broad-based initiatives in the future.
Tangible Applications into the Instructional Design Process
As we began our pilot, we found ourselves returning to our central theme—the importance of language and authenticity in better reaching our students. The strategies explained below, while unique to our institution, may provide inspiration to others.
The Introduction to a Module. Rarely, if ever, does an on-ground instructor begin a class session by jumping headfirst into the course material. More often than not, even a novice instructor will begin an on-ground class with pleasantries, housekeeping items, and perhaps some informal musing on the connections between the upcoming lecture and the content covered in the previous class. In the online realm, this aspect often does not come intuitively for SMEs designing the course. Not only does it require a formal understanding of curriculum design, but it also requires an understanding of generational differences in how students process tone in written text. We realized through our pilots that generative AI can effectively output these conversational, human-like lead-in phrases at scale, especially if detailed prompts are used. We specify tone, demeanor, and request a "professional, yet conversational," writing style in the prompt. We have also had success by specifying the module's learning objectives and key lesson content from the module.
The Relevance of Subject-Matter Content. We have also found AI to be highly effective at expanding upon, or clarifying, existing subject-matter content. In the ID industry, course materials often undergo multiple levels of revision, which is an iterative process that can be challenging to implement in a small college setting. However, we have found that with the responsible use of AI, we can enhance the existing subject matter in ways that maintain its integrity, while also generating additional examples, rephrasing dense verbiage, and providing related topics for exploration. Aside from creating more engaging course material, this approach reframes the broader conversation from purely designing curricula, to designing curricula that targets the demographics of a given institution. Something as simple as prompting AI to generate a metaphor for a complex scientific principle that resonates with an 18-24-year-old demographic may be just what is needed to improve a student's connection to the course.
Discussion Questions and Assignments. In the on-ground classroom, even the broadest style of discussion question — as an extreme example, the question of "What is life?" — can easily evolve into dynamic, nuanced discourse as ideas and questions get bounced around in real time. In the asynchronous online environment, those same questions often fall flat. These types of simple, linear questions also tend to be the types that students may feel tempted to answer using generative AI. As a solution to this, we're finding that AI can be proactively used to revamp discussion questions and create prompts that tap into higher order thinking. This not only drives better student-to-student communication, but also mitigates the risk of academic dishonesty.