Jason Gulya is Professor of English and Applied Media at Berkeley College. He also works as a consultant and keynote speaker on AI and education, and has worked with colleges all over the world. For his work, he has been featured in Business Insider and Forbes. Recently, he published a book (with Paul Matthews) titled Artificial Intelligence, Real Literacy.
The product mindset is embedded in college culture.
Professors grade students based on the final products they create — the papers, presentations, and exams that they can complete under pressure. When I was in college, I had a professor who placed a sheet of paper over every student’s name, so that their assessment could be as objective as possible. They wanted only the final product in front of them.
What’s more, professors themselves stand in front of classrooms of students, lecturing on topics they have studied for years or even decades. Students read essays and books that took months or years to write.
Colleges are places of intellectual products. Years of hard work hide the messiness that produced that work.
The idea was that if you saw a good finished product — such as a well-written paper — you could assume that the students went through various iterations. You could assume that a student knew how to pick out quotes, analyze evidence, and synthesize because, well, the document showed that they could. The document was all the proof needed. At least, that was the idea.
Things are changing. Or maybe they already have.
Generative AI, for better or worse, has made it increasingly difficult to distinguish between a human-generated product and an AI-generated product. This, in turn, puts a question mark over the validity of finished products in the classroom. Students can generate polished final products relatively easily, even without an intellectually challenging process.
This dynamic is a significant challenge to how colleges assess student achievement and prepare students for life and work. So, how do we move forward? Where do we go from here?
Colleges could raise expectations for student work, and assume (correctly or incorrectly) that students will use Generative AI to complete them. Some students will submit fully AI-generated work, while others would blend their words with an AI’s. In this line of thought, professors would continue to focus only on the final product, trusting that the students are exercising critical thinking and analysis even if they create through a bot. Even if a student submitted AI-generated work, at least they practiced critical thinking in directing the AI.
That approach is problematic. For one, many college students actively resist Generative AI for personal ethical reasons. It’s not an even playing field if they are graded as if they use AI all the time. Another problem is that the above approach stems from a product mindset.
A better alternative is for professors to focus more on process than product. This means:
- Consistently showing the value of process
- Guiding students as they create their own processes
- Emphasizing the importance of reflection and metacognitive skills
Reorganizing the college classroom around these ideas is no simple task. For many professors, it’s difficult to figure out what a process-minded classroom would look like and what it would mean for assessment.
After all, many of us (myself included) have never set foot in a process-minded classroom. When I was an undergraduate, I was asked to submit rough drafts for feedback. But that was about it.
The first step is to recognize that this is about more than just creating some process-oriented assignments. It’s more than just saying “process over product” over and over. It’s a mindset shift, in which we need to reassert the value of messiness and of the process itself.
The messiness isn’t something to jump past. That’s when we learn.
Virtually every professor recognizes the value of process. Now, we need to take that recognition a step further. We need to design our courses and our assessments around it.
For example, in my writing courses at Berkeley College, I guide students as they develop their own Self-Empowering Writing Processes (SEWPs, which I’m pronouncing “soups”). They design their own SEWPs, implement them, and then reflect on them. Students are welcome to work Gen-AI into their writing processes, but they need to (a) disclose their use in Transparency Statements and (b) reflect on whether that use of Gen-AI empowered their voices or took them away.
Redesigning our assessments is just part of this larger shift. Professors also need to model what a process mindset is and why it matters.
Co-designing courses with students gives them a glimpse into the processes behind the courses themselves.
Letting students choose the text to be discussed and using class time to think out loud about that text shows students what it means to apply a framework to a brand new text.
Examples abound.
In essence, professors can reinforce the value of process not only by designing assessments around it, but by consistently showing the value of their own processes.
This is tough work because in many ways, colleges are future-bound. Students want to think about the end product. They want to think about the expert they will become when it’s all done, or the degree they’ll finally have, or the job they’ll qualify for with that degree.
But AI is a push to slow down. It’s a chance to think about how much we lose when we try to jump past the messy present to the final product.
And we lose a lot.