How is ai currently 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 mine, we talk about what ai can and cannot do. We looked at examples of results from large language models (LLM) and critiqued how well they did with the answers. Next semester, my students will spend time working on speed writing, reflecting on the results of the LLM, and reflecting on when knowledge and learning are important and when hybrid human-ai writing makes sense. For many professors, that last question is the one that causes the most friction on campuses.
How the past influences the present
Teachers across disciplines are wondering how and where their students will use ai. Will students be unburdened by converting knowledge into learning, or will they remain motivated to spend time wrestling with concepts and gaining a deep understanding of the topics? While I see this as a valuable question, I also reflect on my own childhood. I grew up with card catalogs and encyclopedias. It was very important to have your own set of encyclopedias at home! We learned what we could from the books we had access to and memorized facts to regurgitate on exams. This was the epitome of knowledge and learning before the Internet was widely available. Now, if my son asks me a question about anything, we can Google it on my phone from wherever we are. Telling a student to go to the library to access a physical copy of something is an outlier rather than the norm.
If we think about how information production and knowledge ownership have changed in just a few decades, I think there are lessons to be learned for higher education. The way we approach our subjects in front of ai will change radically and barely change at all. For teachers who have had the opportunity to participate in pedagogy learning groups, there can be a lot of overlap between what they already know about teaching and the changes that ai could bring to the classroom. Here are some examples:
- Many people are starting to embrace the idea of the flipped classroom to let students grapple more deeply with concepts before turning to ai for help. But the idea of flipped classroom It is attributed to Jonathan Bergman and Aaron Sams in 2006.
- Another pedagogical model that instructors use is the concept of transformative learningwhich provides students with a disorienting dilemma of some kind and then guides them through the process of exploration and reflection to see if and how the students changed in some way through exposure to this new concept. Jack Mezirow is credited with developing this concept in the 1970s..
- The concept of the Scholarship of Teaching and Learning (SoTL) is another that instructors could benefit from thinking about. in exploring SoTL projectsInstructors take the time to interrogate a teaching practice of their own and see if it really works or if it works the way they think it does. The origins of SoTL dates back to Ernest L. Boyer’s 1990 book Scholarship reconsidered: Faculty priorities.
- I also find value in prioritizing. process over productwhich can be credited to Sale in 1979. This theory takes away from students the stress and burden of the “three exams and a final” or “three papers and a final” model that seems to have taken hold in many places and instead focuses on how students arrive at the final exam. product. Value and learning can be gained in the journey and not just at the destination. Many other pedagogical theories have stood the test of time, but they deeply impact the way I approach my classrooms.
Using pedagogical theories to address teaching with ai
So why do we look back when we talk about the future of ai in higher education? It is a reminder that what was old is new again and that many of us already have at least some tools in our pedagogical toolboxes to address the changes that ai will bring to our approaches to teaching.
When I think about the theories presented above, I think about how I have structured and restructured my classroom over time. At one point, I removed reading responses entirely because they weren’t working for me. Now they are back, but not as an exclusive product. Rather, they form the basis for our class discussion at the next meeting. Can students use ai to write their answers? Yes, quite easily, which is why they are not the only measure of understanding and why we also have discussions in class. Students submit their reading responses before class and then meet in small groups in class to discuss the reading. After that, we met as a large group and discussed the reading. Reading answers also have a low-stakes point value, which takes the pressure off students to cheat. And, if you wanted to add some transformative learning questions to the reading responses, you could delve even deeper into what the readings meant to the students.
It is these small adjustments to the tasks we already have (and the use of pedagogical tools that already exist) that can make the biggest difference in our teaching and student learning. As instructors, we know that knowledge matters. We understand why being strong critical thinkers is important for both work and civic engagement, and we understand that there is joy in the struggle to turn words on a page into deep, lifelong learning. Now, we need to take the time to emphasize these ideas to our students, as well as teach them when it might be appropriate to turn to ai for help and when ai will be a bigger obstacle.
We’re all tired of having to re-equip everything for emergency remote learning on such short notice. And having to restructure again for ai can seem overwhelming. My advice is to start small and involve your students in the process. Take the time to develop ai literacy activities for your classes. Have students reflect on the usefulness and validity of ai for their tasks and understanding. Help students (and ourselves) learn to see ai as a tool rather than a way to avoid work. Through small adjustments and small steps forward, we can help create authentic and valuable learning for our students in this new ai-enhanced world. Using the tools we already have can help us shift our teaching toward theory-based ways rather than a scattershot approach. By focusing on what we can do rather than what we cannot control, we have the opportunity to bring some wonderful changes to higher education that can benefit our students even after they leave our classrooms for the outside world of adulthood. .
