AI in Teaching and Learning: Productive Failure
We’ve launched a semi-regular feature to keep you informed about how artificial intelligence (AI)—especially generative AI (GenAI)—is shaping teaching and learning at Carleton and beyond. We started with our four-pronged approach to developing an AI strategy for your course. To compliment this advice, we’re unpacking four interconnected elements of sustainable teaching practices:
- Relationship-rich environments
- Scaffolded experiences
- Productive failure
- Intrinsic motivation
In previous months, we unpacked the first two layers and this month we turn our attention to the next layer: Productive failure.
Productive failure challenges the assumption that effective learning should always feel efficient. While students often prefer clear instructions and immediate success, struggling with a problem before receiving instruction can lead to deeper understanding, stronger retention and improved transfer of learning.
Today, productive failure may be more important than ever. When answers, explanations and examples are available on demand, it can be tempting to remove uncertainty from the learning process altogether. Yet learning rarely happens because information is delivered; rather, it happens when learners actively grapple with ideas, test assumptions, identify gaps in their understanding and refine their thinking.
Designing for productive failure does not mean setting students up to fail. Instead, it means creating opportunities for learners to attempt challenging tasks, make mistakes in low-stakes environments and receive timely feedback before trying again. Activities such as prediction exercises, case analyses, draft submissions and reflective revisions can all help transform mistakes into meaningful learning opportunities.
As you begin preparing for the upcoming academic year, consider where you might intentionally create space for productive struggle in your course design—not as a barrier to learning, but as a pathway to it.
Next month, we’ll turn to intrinsic motivation.
Have something to share or a question about AI in your course? We want to hear from you! Reach out to us with your ideas, challenges or success stories.