From Sage on the Stage to Steward of the Struggle

For many years, experts in teaching and learning have leaned into the idea that a higher education faculty member’s job has—or should have—moved from the “sage on the stage” to the “guide on the side.” This shift represented a change from lecture-based teaching, where the faculty member stands in front of a class and provides content, to active teaching, where the faculty member facilitates student engagement, often in a flipped-classroom environment where students engage directly with course materials and the faculty member facilitates their learning experience. In the “guide on the side” experience, the faculty member might physically move around a classroom as students work on problems, answering questions or stepping in when students get stuck or need redirection.

In both cases, past students generally had two choices when it came to completing their work: doing it themselves, or having another human do it for them. While some specific tasks could be completed by word processors or calculators, if a faculty member received an assignment from a student, it generally reflected that student’s effort (or blatant cheating).

Today, thanks to artificial intelligence and tools like ChatGPT, students have endless options when it comes to what they complete on their own and what type of assistance they receive from AI. That means our work as faculty has changed. Our most important role is no longer as a guide who answers questions and facilitates assignment completion, nor is it as a provider of content. The computers in our pockets can do these things quite handily. Instead, today’s faculty must guide students to the useful cognitive struggle that is at the essence of their class, creating experiences and designing assignments that further cognitive agency and durable learning.

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From Sage on the Stage to Steward of the Struggle

What does it mean to be the steward of the struggle? It means identifying for students where the productive and important cognitive friction resides in the class and what tasks they can safely offload to technology. It means identifying for students what they should be able to do on their own, independent of technology, where it is permissible to use technology as an aid or scaffold and what they can delegate altogether.

It means identifying what work must remain solely human and what can be aided by technology.

Some may want to argue it must all remain human. Students should be able to do everything without AI. This position misunderstands the notions of cognitive offloading and cognitive load theory. Cognitive offloading is not an enemy of learning. Cognitive load theory recognizes that humans have limited working memory, so effective course design includes offloading unimportant tasks while focusing working memory and effort on essential learning outcomes. If my learning outcomes are related to creating and solving algebraic problems, I may permit the use of calculators for basic calculations because offloading that routine task allows the student to focus on the more complex cognitive challenge. Or if I am teaching students to develop persuasive arguments, I may allow the use of word processors to check and fix spelling, punctuation and grammar.

The challenge for today’s faculty is to draw those lines and effectively communicate them to students:

• Yes, you can use AI to help you find the elements of a tort, but you must be able to recall the basic concept of the tort without AI assistance so you can recognize it in a business scenario.
• Yes, you can use AI to draft lovely prose, but you cannot use it to develop a persuasive argument structure for a debate.
• Yes, AI can tell you the formula for straight-line depreciation, but you must recognize the need for that type of accounting without being prompted by AI.

These lines will likely be different when a student is in fourth grade (no calculators allowed) and in graduate school (use all the calculators you want). They will also reflect the discipline at issue (no spelling help from technology in a language class; use all you want in a physics class).

To be clear, these are hypotheticals, not meant to apply to your class or your discipline. That’s the point. Only you know where the line is for you and what you need your students to be able to accomplish.

Teaching this way will require us to entirely rethink our learning outcomes (“What can a student do as a result of this class?”). Today, students can do anything, with or without our classes … with the help of AI. That means the question today is: What can a student do without any assistance from AI, and what can they do with assistance? What must remain in their brain, so they can carry it around to a meeting or a worksite, and what can they develop in more detail later, with assistance from AI? What do we want to protect as a core unassisted human skill and what are we comfortable seeing offloaded to or aided by technology?

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Being the steward of the struggle also means understanding that learning requires friction. Learning theory and psychology have long identified pathways to durable learning, and they require desirable difficulty, generating things rather than reviewing them and techniques like retrieval practice and interleaving. As faculty, these are tools we must now employ, and we must do so deliberately and transparently.

In my ethics class, I ask students to generate summaries of cases on their own, unassisted by technology, and then work with AI as a Socratic dialogue partner to test their understanding of the core dilemma. I ask them to identify ethical pitfalls and develop strategies to structure more ethical workplaces without the use of AI and then permit the use of AI to refine, test and further develop specific plans. While they do generate polished memorandums, the final work product is a relatively small portion of their grade. I allow the use of AI to polish their writing and organization because I have broken out the hard work in scaffolded steps throughout the term and seen evidence of the unassisted and assisted AI work in various other forms.

Faculty may be frustrated by students’ overreliance on AI, but let’s be clear: Without a road map, students don’t know what aspects of the intellectual struggle they should retain. I have seen students waste hours rereading notes that were drafted by AI; they did not know that it was the generation of the notes that would have resulted in durable learning. I have seen students use AI to outline an argument, sometimes with the knowledge of their professor, without realizing that they would be less able to create their own arguments in the future. And I have seen students spend hours memorizing formulas or four-part tests that they would immediately forget two months later, rather than focusing on durable understanding of an underlying concept that we would have wanted them to retain.

As stewards of the struggle, we have a deep responsibility to identify for our students key cognitive processes and the classification of the knowledge and skills we are including in our classes. We need to tell our students where the cognitive struggle should reside and where it can be delegated. We have a new job, and it’s time we started doing it.

Inara Scott is a professor of business law and senior adviser for strategy and innovation at Oregon State University’s College of Business.

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