The integration of generative AI into academic environments presents both new capabilities and complex challenges for university teaching. Rather than prescribing a single pedagogical approach, this resource is designed to provide an objective overview of how these tools function and offer practical frameworks for adapting your assignment design, assessments, and teaching strategies to an evolving digital landscape.
Instead of reacting solely to the rapid pace of technological change, a more productive approach begins with what is currently known about AI in higher education. Grounding our strategies in the documented capabilities, inherent limitations, and observed impacts of these tools allows us to move beyond speculation and focus on evidence-based applications that genuinely support student learning.
Here are some shared points of consideration:
- Balancing Integrity and Innovation: We all share valid concerns about academic dishonesty and "lazy learning." At the same time, we have an exciting opportunity to explore together how these tools might actually enhance our pedagogy and build more vibrant classrooms.
- Cultivating Prompt Literacy: Because AI models predict text rather than "know" facts, errors and confabulations are simply part of the process. This gives us a great opening to partner with our students in learning how to craft thoughtful prompts and critically evaluate the results.
- Exploring Dynamic Simulations: Many of our colleagues are finding that AI is most useful when we move beyond simple Q&A. We can experiment with asking the AI to adopt specific personas—like a Socratic tutor, a peer reviewer, or a debate opponent—to spark richer student interaction.
- Focusing on the Iterative Process: Rather than viewing AI as a shortcut to a final draft, we can explore how its diverse and multimodal capabilities (text, coding, visual design) can help students brainstorm, refine prototypes, and polish their ideas more creatively.
- Bridging Hidden Barriers: AI can act as a helpful equalizer for our students. By providing immediate, individualized support for secondary skills—such as a quick translation or basic coding help—we can free up our students to focus their cognitive energy on the primary learning objectives of our courses.
- Streamlining Our Own Workflows: Beyond how students use it, there is a lot of potential for AI to support us behind the scenes. We can share ways to use it to draft rubrics, organize course materials, or summarize real-time classroom feedback, giving us more time to focus on teaching.
Frequently Asked Questions
How can I use generative AI to help me teach in the classroom, day to day?
Activity leader: Creating interactive classes is easy to advocate for, but hard to do. Some of our faculty have used GenAI to act like a peer student to stimulate critical thinking, perform real-time analysis of student responses to make them feel heard, or even help make games that align with class content,no coding needed!
Personal tutor: You (or your TA) can’t be everywhere at once, but GenAI might be able to. Feeding it your syllabus, lectures, and an in-depth prompt can help make a personal tutorbot, generate unlimited practice problems, or even remind students of course-specific information.
Custom reviewer: LLMs can be used to provide initial personalized feedback to your students, so you can focus on the big picture. Some faculty used them to quickly summarize student responses before office hours, or even point out areas in student responses that need improvement.
Skill leveler: Classes often have hidden prerequisites: familiarity with coding, texts barred in foreign languages, or even art skills. GenAI tools can be leveraged creatively to help students overcome such barriers, like teaching a business school class about data analysis without requiring every MBA student to learn code, or analyzing trends of thousands of photographs without having to do so manually.