Recap: Analog Approaches in the Age of AI

On March 18, I attended a CETL workshop titled “Analog Approaches in the Age of AI,” which was co-facilitated by my colleague Sarah Ligon, who is a Graduate Teaching Fellow at CETL, and Liz Norell, associate director of instructional support at CETL. I went in expecting a fairly predictable conversation about the challenges AI poses in the classroom. Instead, what I found was a much more thoughtful discussion about what learning actually looks like right now and where our next education wins might lie. The panel brought together three professors from the University of Mississippi, namely, Daniel Stout, associate professor of English, Amber Nichols-Buckley, lecturer in Writing and Rhetoric, and Jeffery Bednark, instructional assistant professor of Psychology and whose work in cognitive neuroscience focuses on learning and brain processes. Each of them approached the topic from a different angle, but together they raised a central question that stayed with me throughout the session: How do we design learning environments where students are still doing the thinking in an age where tools can do so much of it for them?

What do we mean by “analog”?

Sarah began by introducing us to the idea of analog, which she defined simply as any activity that is not mediated by a digital device. That includes things like handwritten notes, in-class discussions, oral exams, and other forms of active classroom learning. What made this framing compelling was the context behind it. Across classrooms, instructors report that students are increasingly distracted, less engaged, and more likely to rely on AI tools to complete assignments rather than working through the material themselves. Sarah mentioned that only about half of students in her class are actively taking notes in class, which while anecdotal, reflects how passive the learning experience can be in some cases. She also reflected on her students’ positive reactions toward analog approaches in her class, which they believe leads to better learning.

We have been there before

Dr. Stout offered an interesting way to think about this moment by comparing it to the early days of the internet. When the internet became widespread, there were similar fears about plagiarism and the loss of original thinking, and while those concerns were not unfounded, education eventually adapted. His point was not that AI is identical to that shift, but that we have been here before, at least in some form. The differences now are those of scale and immediacy. AI can generate complete, polished responses in seconds, which makes it much easier for students to bypass the actual process of thinking. That shifts the challenge from simply preventing misuse to designing assignments that make intellectual engagement unavoidable.

This is where Dr. Bednark’s perspective on cognitive offloading added an important layer. He explained how, when students rely too heavily on external tools to do cognitive work, they reduce the mental effort required for learning, which in turn affects understanding, memory, and the development of critical thinking skills. The concern, then, is not just that students are using AI, but that they may be skipping the very processes that lead to learning in the first place. That framing made the issue feel less like a technological problem and more like a pedagogical one.

What is actually working in classrooms?

The panelists shared a range of strategies they are already using in their classrooms. Oral exams and mock assessments, for instance, require students to explain their thinking in real time and make it difficult to rely on external AI-based tools. In-class writing serves a similar purpose by ensuring that students engage directly with the material. One particularly interesting idea was a kind of “speed dating” format for research topics, where students rotate quickly through peers to pitch and refine ideas, turning what could be an isolated task into a dynamic and interactive process. There was also discussion of handwritten assignments and sketch-based work. I was genuinely impressed by one of the sketches that Jeff Bednark’s students made! (You can view two student examples here and here.) Along with this, we discussed more experiential or game-based learning approaches, all of which shift students away from passive consumption and toward active participation.

Can we find a middle ground with AI?

At the same time, the conversation was not entirely framed as being anti-AI. Amber Nichols-Buckley described her approach as somewhere in the middle of the spectrum, neither banning AI entirely nor fully embracing it. That middle ground felt very practical. Rather than prohibiting AI outright, she suggested using it in limited, intentional ways, such as for brainstorming or outlining, while still requiring students to do the detailed work themselves. This approach proceeds from the assumption that AI is not going away, but also that its role in learning needs to be carefully defined.

How do analog approaches handle student accessibility?

An important thread that ran through the discussion was the need to keep accessibility in mind. Analog approaches, while valuable, can create challenges for students who rely on digital tools or who need more time to process information. This made it clear that any shift toward analog methods has to be flexible and inclusive, rather than rigidly enforced. The goal is not to replace one system with another, but to expand the range of ways students can engage with learning.

What is the instructor’s role now?

Underlying all of this was a broader question about whether technology is, in some ways, short-circuiting the learning process. With constant access to devices and tools, it becomes easier for students to avoid the discomfort that often comes with thinking deeply about something. Some instructors have responded by limiting device use in the classroom, while others focus on encouraging better habits rather than enforcing strict rules. There was no single answer here, but the question itself felt important: Are we making it too easy for students to avoid the kind of effort that learning actually requires?

The last 15 minutes were spent on answering questions from the audience. There was a question about software that can convert handwritten notes into digital computer text. Amber Nichols-Buckley gave tips on how to design activities that give enough time for students to come up with an outline, but not enough to write the whole paper purely using AI. 

What I ultimately took away from this workshop is that the conversation is not really about analog versus AI. Rather, it is about whether students are engaging in the cognitive processes that lead to understanding and learning. Analog approaches seem to help because they slow things down, make thinking more visible, and create situations where students have to actively participate. But the real goal is not to go backwards. It is to design learning environments where students cannot simply bypass the work of thinking, regardless of what tools are available to them. This workshop reinforced a simple but important idea: Good teaching is not about restricting tools, but about designing tasks that require genuine intellectual engagement. In the age of AI, that responsibility feels more important than ever.