Act One: Before Teaching

Overview

In our scenario, we met Gillian, who teaches Introduction to Urban Sociology. GenAI has now added a new layer of uncertainty to her teaching. Before the next semester begins, Gillian and Mindy –her consultant– sit down to look at the course design with fresh eyes. They're in Gillian’s office, surrounded by whiteboards filled with diagrams of course objectives, multi-colored Post-it notes with handwritten notes, and textbooks scattered across the desk. Gillian looks up from reviewing her draft syllabus.

Gillian: "With GenAI tools so easily accessible within our university's Google Suite, I’m deeply worried about my course. I have several writing assignments that are critical opportunities for students to deeply engage in critical thinking about the course concepts. This past summer, I noticed a surge in generic, GenAI-written essays that seem to flatten student voice during heavy workload weeks, across multiple groups of students and assignments. How do I ensure students are actually doing the cognitive lifting themselves? How do I help them see the value in doing their own thinking?"

Mindy: "Yes, this issue is tremendously challenging. I am hearing from instructors that students do not see the point in writing on their own. One place for us to start is to help work towards course structures that get away from policing the technology. Let's look at your course design and syllabus to see what we can address before the semester starts."

"What if we proactively reimagine your assignments to be 'GenAI-interrupted' by building in iterative, collaborative layers that reduce overwhelm and require authentic student engagement?"

Gillian: "I like that phrase. Interrupted. Tell me more."

Mindy: "It's about building in moments where students have to engage with the material in a way that's difficult for a chatbot to replicate."

Core Challenges

As they look more closely at the course structure, the vulnerabilities become clearer:

• Cognitive offloading under pressure: Students resorted to GenAI during heavy workload weeks when they feel overwhelmed by the sheer volume of tasks.
• Loss of authentic student voice: GenAI tools flatten unique nuances and personal perspectives, leaving underclass student writing sounding unoriginal and generic, and what is worse, it has a ripple effect on students being able to fine tune their writing which is an important learning outcome
• Scale and evaluation strain: Scaffolding more authentic writing and tracking individual progress iteratively would be difficult in a large-enrollment course with 150 students and three Graduate Student Instructors (GSIs).

What would it look like to redesign the assignments so that authentic student thinking has to show up, from the very first draft?

UDL Options

The conversation shifts from damage control to course design. If the goal is authentic student thinking, the assignments themselves need to make that thinking visible  and hard to outsource. Gillian considers two strategies:

• Iterative in-class "Quick writes"
• The reading "jigsaw" method