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The growing backlash against AI-saturated classrooms has created a renewed sense of exploration into analog learning methods. I’m biased. I love the idea of deeply personalized learning, using low-tech or no-tech responses. Yet, I’m also wondering what chance many of these methods have in our existing systems of overworked, underpaid, and extremely tired faculty. Analog spaces matter because they expose something our existing approach to AI often obscures—most course-level AI policies are really faculty preferences, and students need more structured opportunities to compare AI-mediated and non-AI learning before they can develop real discernment.

Karen A. Spira, Assistant Director at Wake Forest University’s Center for the Advancement of Teaching, penned a recent provocation calling for campuses to create Human Intelligence Labs as a response to AI’s intrusion into learning. It’s a good idea that deserves more than a passing read. The concept of a low-tech, no-tech, or distraction-free space is something we are actively exploring at my own institution; however, there are a lot of caveats and considerations. I’ll start with the biggest one: What does writing, or learning for that matter, look like in this weird AI era? Analog techniques may play a role in helping students develop a writing or learning process, but such a response needs to be balanced and avoid all-or-nothing approaches to dealing with AI.

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Analog Learning’s Design Challenges

An analog response to AI will not solve the challenges the technology poses to learning, but it can offer both students and faculty the space to explore distraction-free learning for some of the time. How do we create opportunities that use both offline and connected spaces?

• Prioritizing such responses risks privileging residential in-person teaching and needs to take into account online learning or large lecture courses; otherwise, there is too much of a risk of creating fractured responses instead of a coherent approach on campus.

• Students aren’t attending courses like they did pre-COVID. Any sort of analog approach that creates an offline learning environment cannot simply be all or nothing, and must take into account how students will access materials to learn if they miss several classes.

• Students will need more accommodations than simply time and a half on exams. Students require digital devices for visual, auditory, or many other types of disorders. Any classroom will need to keep that in mind during the design phase.

• Learning cannot simply be confined to the classroom. Students in writing-intensive courses and other process-driven disciplines will need access to their drafts outside of the classroom space to reflect and revise their work. Not giving them access creates an artificial writing process, one dictated by the environment, and not by student discernment or agency to use or not use AI or many other modern writing tools.

• The cost is extremely high in terms of labor and physical space. Developing a room where technology is still present, but limited, is an expensive proposition. Having faculty redesign their teaching is time-consuming, so too is trying to grade work produced in offline settings.

Those are big challenges for sure, but they can be overcome if you consider a reduced-distraction space not as a solution to dealing with AI, but as part of a broader response. I really like Spira’s framing of her approach as a lab and not as a dedicated classroom. After all, a lab is a place that exists to augment lecture or small group work done in another classroom. Students generally visit such a lab once a week, knowing that this is a distinctly different space, where different methods of learning will be employed. I had Claude Code mock up a prototype of a reduced-distraction space to help me visualize what it would look like.

Mixing “Focus Days” With “Open Days”

With such spaces, faculty are left with compelling and potentially exciting design challenges. Some may want to teach full time in low-distraction rooms as a response to AI’s ubiquitous presence, but treating a lab as a classroom doesn’t give students the opportunities needed to create responsible or intentional habits regarding AI. Faculty need to establish guidance and practice for existing skills and provide opportunities for students to explore new AI skills. Spend some days in an open classroom, where digital tools are turned on, while spending other days in a lab space free of digital distraction, makes for a compromise that balances the needs of both.

AI likely has a role to play within student learning. So, too, will analog and other traditional methods that often oppose automation. We need a bit of both, I would think. Time spent in one space should inform and complement student engagement in the other space. Here are some possibilities to consider:

• What learning outcomes would you focus on, knowing that you and your students had access once a week to a distraction-free learning space?

• What guidance about AI use will students receive in the other parts of your course that take place outside of a reduced-distraction environment?

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The Nuanced Ways Faculty Are Already Using Analog Techniques to Navigate AI

Outside of dedicated learning spaces free of AI, there are a number of ways faculty have been creating spaces where analog and digital learning can coexist. One of the great pleasures I had working on The Norton Guide to AI-Aware Teaching was taking the time to explore the ways faculty are using analog techniques to ensure students learn. I was far more fascinated by responses to process-based learning than I was by “give them bluebooks” as a response. Here are a few. The book contains many more!

Mark Marino’s Analog Sandwich

Mark Marino teaches writing at USC. He created a semester-long approach for his students that gives them the opportunity to try new AI tools but also ensures that human writing and personal agency remain core values of his class. Marino refers to this approach as an Analog sandwich. It involves three stages:

1. AI-heavy process

2. Analog, an unplugged unit

3. Students’ Choice

You can read about his approach in detail in his blog outlining The Analog Sandwich: teaching writing with & without AI and the follow-up post–The Analog Sandwich (how it went).

James Seitz’s Writing In Focus Mode

UVA English professor James Seitz has spent years moving writing into class time, with the goal of teaching writing as a way of thinking and changing students’ relationship with their process through reduced-distraction techniques. Seitz doesn’t ban technology in his classroom. Instead, he asks students to turn off their WiFi and put their word processors in focus mode. This creates a habit, one that is focused on preserving the process of writing. What I like about this approach is that it doesn’t put students in a proctored situation. Seitz isn’t hovering over students or using the environment to compel compliance—he’s inviting students to learn using fewer technological distractions. Crucially, students with disabilities will be able to have their accommodations quietly met without sticking out as the handful of students with devices in a course filled with bluebooks. That’s something I think we should all keep in mind.

You can listen to my coauthor Derek Bruff’s interview with James Seitz on the Intentional Teaching podcast.

The Rise of Analog Workbooks

The Deconstructing Calculus project, led by Amy Langville at the College of Charleston, gives students a structured, analog approach to learning in a class. Their notebooks are highly interactive and ask students to actively engage with calculus concepts by using physical objects around them, but they also exist alongside vibrant digital resources. Students watch videos, make comics, and create digital memes.

For writing-intensive courses, Lily Abadal, Assistant Professor of Instruction in the Judy Genshaft Honors College at the University of South Florida, developed a project-based workbook for her Philosophy course called Drafted, which takes students through the scaffolded steps of a major research essay. Abadal’s reasoning for this is to give students more distraction-free time to work in and outside of class.

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Technology is the Enemy (Until it Isn’t)

We can’t effectively ban AI. We also cannot possibly keep up with AI. It is within this tension that we teach. Students will likely lose some vital practice with crucial skills when they uncritically use AI, but could potentially gain new ones working with AI in ways we haven’t quite figured out yet. That’s frightening because it is weird and overwhelming. It is also deeply upsetting because it is forced on many of us. Most faculty aren’t the ones bringing AI intentionally into the classroom. The developers have already made that decision by giving away AI to teens at scales unfathomable to most. So, what are we to do? If you are teaching in person as opposed to online, have a reasonable workload, a secure position, and few research or service obligations, then it is possible to focus on thinking about how you teach, what you teach, and why you teach using a mix of secure digital and analog methods.

I’m teaching an online class and a hybrid class that meets once per week. My online students and I get to know one another through asynchronous videos that are supposed to be informal, but always feel a bit stunted until we reach the midpoint in the semester. I use video because the text-based discussion board isn’t viable any longer for that modality. I try to get students to do more than just the ‘talking head’ routine of speaking directly into the camera. I encourage them to share their screen and talk over their writing projects, walk through the decisions they made, and highlight and underline points with digital tools. I tell myself it is pedagogically sound, but the reality is my choice was made for me. I moved to video discussions because the only real alternative is hosting an AI chatbot that students will use their own AI chatbot instead of their own voices to converse with.

If I could invite my students in my hybrid sections to learn at least some of the time in a classroom with limited distractions, then I think they could ultimately start understanding that the skill to navigate AI is discernment—learning when to use it and when to back away from it. Most importantly, it asks students to develop their own values about AI usage and not simply have an instructor provide those for them. That’s ultimately what I think we should do.

Is It an AI Policy or a Preference?

The reason these analog responses matter is not simply that they give students a break from AI. They also expose the weakness of our current policy environment. If students only encounter AI as a rule that changes from course to course, they are not really being taught discernment. They are being taught to navigate faculty preference.

Policy is higher education’s love language. It affirms. It directs. It establishes shared norms and clarifies expectations. But when it comes to AI, policy completely fails. Most institutions have forgone institution-wide policies in favor of fairly generic guidelines that defer to faculty to develop their own course-specific policies. There’s a certain logic to allowing faculty the freedom to design their own response to AI and put it in conversation with their teaching and the disciplinary expectations of their field. While it is true that an engineering professor may have a drastically different view of AI in their course than a philosophy professor. What’s glossed over is that there’s no consensus within those very disciplines about how AI should be used. Departments are filled with stories about faculty who teach the same courses, refusing AI, while others actively engage with it with students. My own discipline of writing studies feels like ground zero for many of these debates.

Having every instructor decide their own AI policy acknowledges that there is no actual uniform policy, just preference. And some of those preferences are well-informed, pedagogically motivated, and well-articulated to students. Others are anything but. Both pro- and anti-AI preferences often fail to communicate the value of learning within the course and how AI can help or hinder those goals. Some lean into seeing AI only through a practical lens and craft policies based on preferences that encourage student usage, with little understanding of the ethical dimensions at play within the classroom and beyond. Others are solely concerned with those ethical challenges and cannot see a way past them, so they prefer to ban AI. I’m not sure either position is helpful for students. Perhaps worst of all, many take the message of no uniform policy from the institution as meaning that they don’t have to craft any guidance for their students.

I’m also increasingly worried that students’ preferences to use or avoid AI aren’t valued in these power dynamics. To me, that’s why having spaces where we can communicate with students that AI isn’t going to be available, like a Focus Classroom or Human Learning Lab, should ideally be juxtaposed with conversations where students have access to AI tools, with guidance. Otherwise, there will be little opportunity for students to actually develop the type of discernment we hope arises out of these AI-aware choices.

A faculty member whose preference is to use AI in course materials, assessment, feedback, and course communication may not be able to accommodate students who prefer not to use AI. This is going to become unavoidable for some students as faculty integrate AI into learning pathways and course workflows. We don’t have prior examples of students asking to opt out of aspects of the course because of the tools being used. We do, however, have many glaring examples of students refusing to engage in courses based on their political preference. AI is likely going to become part of that toxic stew of heavily politicized positions. An intentional space can help students and faculty process many of those positions.

But what then of students who prefer to use AI and then take courses that ban it? And I’m not just talking about banning AI in assessments. There are now many courses that attempt to ban technology entirely from in-person instruction as a method of curbing AI’s influence. Many students prefer to use digital tools of their choosing in their day-to-day interactions, including those with recognized disabilities that universities are legally required to accommodate. Will students even bother asking for such accommodations if their need to use technology runs counter to a faculty member’s preference? Limiting students’ options for how they access information, write, or read likely creates real barriers for certain students to learn and closes pathways that the universal design for learning frameworks prioritize.

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The Answer To AI Is Discernment

One theme , , and I continually returned to while writing The Norton Guide to AI-Aware Teaching was how important it was for faculty to design the conditions where students can learn and continue to develop the necessary skills with and without AI. Faculty need spaces to explore those distinctions, too. We are also coping with all of these forces under tremendous pressure, often with too little time, too little institutional support, and too many hyped claims made about what AI will supposedly transform and destroy. A lab of focused-based model gives us permission to experiment without pretending that every solution must become policy, every preference must become a rule, or every rule must carry the full weight of institutional coherence.

Analog is having a moment. Its sustainability will depend on whether we treat it as something more than backlash against AI creep and instead embrace it as a design challenge worthy of our attention. If analog learning becomes merely another way to police student AI usage, it will surely fail. If it becomes a privileged experience available only to students in certain campuses, courses, or modalities, it will fail in quieter but equally serious ways. But if analog learning becomes part of a broader ecology of AI-aware teaching, then it may help us recover something we badly need. Namely, those occasions where students can feel the difference between producing work and being replaced in the production of it.

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The Norton Guide to AI-Aware Teaching is now available to order as an ebook! Print copies are expected to start shipping on September 24th. Here’s how you can get a copy:

1. Our publisher Norton is pleased to offer the guide as a free ebook for all instructors currently using a Norton textbook. If that’s you, you’ll receive access from the Norton team when the ebook is available July 1st and can contact your local Norton representative with any questions.

2. If you would like to order the ebook, you can now do so through Amazon and Barnes & Noble and other retailers.

3. If you would like to pre-order the paperback version of the book, you can now do so through Norton, Amazon, Barnes & Noble, and likely other retailers. If you go through Norton, be sure to use the code AIFREESHIP at check out to get free shipping!

4. If you would like to order multiple copies for a campus reading group or some other faculty development effort, Norton has an option for you: On orders of 10 or more print copies, we offer 50% off the list price and free domestic shipping. (Such orders must be on a nonreturnable basis.) To take advantage of this offer, contact Peter Wentz at pwentz@wwnorton.com with subject line “Norton Guide to AI-Aware Teaching.”