AI in Higher Education: Leveraging What We Already Know About Good Teaching Practices

How is artificial intelligence currently being used in higher education? As of fall 2023, we are still in the early stages of figuring out what AI integration means for higher education. In some classrooms, like my own, we talk about what AI can and cannot do. We look at sample output from a large-scale language model (LLMs) and evaluate how well it handles responses. Next semester, my students will spend time working on prompted writing, reflecting on the outcomes of the LLM, and thinking about when knowledge and learning are important and when it makes sense for AI to mix writing. For many faculty, this last question is the one that causes the most friction on campus.

How the past affects the present

Faculty across disciplines are questioning how and where students will use AI. Will students translate their knowledge into learning, or will students remain motivated to spend time on concepts and deeper understanding of topics? While I think this is a valuable question, I also reflected on my own childhood. I grew up with card catalogs and encyclopedias. Having a set of encyclopedias in the house was an amazing thing! We learned what we could from the books we had access to and memorized facts on tests. This was the epitome of knowledge and learning before the internet became commonplace. Now, if my kids ask me any question, we can search on my phone anytime, anywhere. Telling a student to go to the library to get a physical copy of something is an outlier, not the norm.

Considering how information production and knowledge holding has changed in just a few decades, I think higher education should learn some lessons. How we approach our disciplines in the face of artificial intelligence will both change radically and change little. For faculty who have had the opportunity to participate in pedagogy study groups, there may be a lot of overlap between what they know about teaching and learning and the changes that AI may bring to the classroom. Here are some examples.

  • Many people have just come up with the idea of flipping the classroom to allow students to understand concepts more deeply before seeking help from AI. But the idea of the flipped classroom was introduced by Jonathan Bergman and Aaron Sams in 2006.
  • Another pedagogical model used by teachers is the concept of transformative learning, which provides students with some sort of disorienting dilemma and then guides them through a process of exploration and reflection to see if/how the student has changed in some way by being exposed to this new concept. Jack Mezirow developed this concept in the 1970s.
  • The concept of Scholarship of Teaching and Learning (SoTL) is another reflection that teachers may benefit from. In exploring SoTL programs, faculty take the time to examine their own teaching practices to see if it really works, or if it works as well as they think it does. The origins of SoTL can be traced back to Ernest l. Boyer’s 1990 book Rethinking Scholarship: Priorities for Professors.
  • I also discovered the value of prioritizing process over product, which can be attributed to Säljö in 1979, a theory that relieves students of the stress and burden of the “three exams, one test” or “three papers, one test” model that seems to be so popular in so many places. Instead, the focus is on how the student accomplishes the end result. There is value and learning in the journey, not just the destination. Many other pedagogical theories have stood the test of time, but these theories have profoundly influenced the way I approach my classroom.

Using Pedagogical Theories to Explore Teaching Artificial Intelligence

So why is this gaze backwards when we talk about the future of AI in higher education? It reminds us that what’s old is new again, and that many of us already have at least some of the tools in our pedagogical toolboxes to deal with the changes that AI will bring to our teaching methods.

As I think about the theories presented above, I think about how I have constructed and reorganized my classroom over time. At one point, I deleted reading responses altogether because they weren’t working for me. Now, they are back, but not as a product that is entirely their own. Instead, they form the basis of our classroom discussions in the upcoming sessions. Can students use AI to write their responses? Yes, easily, which is why they are not the only measure of comprehension and why we still have class discussions. Students turn in their reading reports before class and then break into small groups in class to discuss the readings. After that, we get together as a large group and discuss the readings. Reading responses are also scored as low-stakes, taking the pressure off of students to cheat. And, if you want to include some transformative learning questions in your reading responses, you can go deeper into what reading means to your students.

It’s these subtle tweaks to pre-existing assignments and the use of pre-existing instructional tools that can have the greatest impact on our teaching and student learning. As teachers, we know that knowledge matters. We understand why being a strong critical thinker is important for both work and civic engagement, and we understand the joy of transforming words on a page into a lifelong effort of deep learning. Now, we must take the time to emphasize these ideas to our students and tell them when to ask for help and when AI can be a hindrance.

We are all tired of re-equipping everything for emergency distance learning in such a short period of time. Re-equipping for AI seems unstoppable. My advice is to start small and engage your students in the process. Spend time on AI literacy activities in your classroom. Have students reflect on the usefulness and effectiveness of AI for their assignments and understanding. Help students (and ourselves) learn to see AI as a tool, not a way to work around it. With small tweaks and baby steps forward, we can help our students create authentic and valuable learning in this new AI-enhanced world. Using the tools we already have can help us transform teaching and learning into a theory-driven approach rather than a decentralized one. By focusing on what we can do rather than what we can’t control, we have the opportunity to bring about some fantastic changes to higher education that will benefit students even after they leave campus and enter the adult world outside.

AI in Higher Education: Leveraging What We Already Know About Good Teaching Practices

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