Culture Eats AI Strategy for Breakfast

The rise of generative AI (GenAI) has been swift, leaving many of us in higher education scrambling to adapt. Our universities have focused on creating new policies and encouraging faculty to redesign assignments, but it can feel like we are swimming upstream. The culture has changed, and despite our best efforts, many students are using GenAI to replace – not encourage – their thinking.¹

Our universities are providing us with policies for GenAI use, but these strategies often miss the mark by creating rigid rules in a world full of gray areas.² The challenge is not only about preventing cheating and maintaining academic integrity, but it is also about building a culture of responsible and effective AI use.

Many university and faculty GenAI policies are framed from an academic integrity perspective and focus on what students shouldn’t do to avoid consequences.² Often, AI policies allow students to use GenAI for editing but not creating. But the dividing line between “creating” and “editing” with AI is fuzzy.

Don’t believe me? Try to define the dividing line yourself. You’ll see how difficult it is to create a rule that a reasonable person could apply with confidence. The reality is a continuous spectrum, not a neatly packaged binary outcome.

To compound matters, students may be given different GenAI use policies by their individual professors each semester, making their compliance anything but straightforward.³ Ultimately, a punitive culture built on fear discourages transparency, and students become hesitant to admit when they’ve used GenAI for fear of penalty, even if they’re trying to use it responsibly. 

Meanwhile, faculty are seeing a clear shift in how students study and learn. The biggest concern I hear from other faculty is that students are using GenAI to do the work for them, short-circuiting the development of foundational skills and critical thinking necessary for building operations research knowledge. True learning requires vulnerability and the discomfort of intellectual growth. GenAI offers a tempting shortcut to bypass the very discomfort that builds competence.

Moving From Policing to Process

The culture has changed, and faculty are up against a powerful force. The phrase, “culture eats strategy for breakfast,” commonly attributed to management expert Peter Drucker, is crucial for faculty and university leaders to understand and embrace. In short, it means that while institutions can create detailed GenAI strategies that incorporate GenAI policies for students, these efforts fail when they clash with the existing culture of how students 
are using GenAI in practice. Because culture eats GenAI strategy for breakfast.

If faculty want to make the changes they seek, they need to focus less on crafting policies and more on changing culture. Here are three practical ways to start.

1. Engage with GenAI use as an ongoing classroom dialogue rather than a static policy.

Teaching students how to responsibly use GenAI should not be a one-time activity that starts and ends with an AI policy in a syllabus. For example, my syllabi are the start of a conversation, not the end of one.

GenAI statements should explain the why in addition to the what. I frame responsible GenAI use around three core principles that shift the focus from catching misconduct to training students to be future professionals:⁴

1. Be transparent about how AI was used.
2. Be responsible for the final work.
3. Be critical by using AI to enhance learning, not replace thinking.

I recommend that other faculty provide specific examples for students to help them understand the dividing line between acceptable and unacceptable AI use.

But that’s just the beginning. Rather than simply telling students to be open, responsible, and critical, we need to show them how we do that ourselves by being the “human-in-the-loop,” where we actively participate in GenAI workflows. This creates a dialogue that unfolds over the course of a semester and sets the classroom culture.

At various points in the semester, I talk openly with my students about how I honor the GenAI policy myself. To be transparent with my students, I create and share how I use GenAI in my courses, although I do so sparingly. To foster responsibility, I share how I create teaching materials and continuously improve them. Occasionally, this has involved GenAI, such as when I’ve used it to improve rubrics. But even then, I am extremely critical of the output, heavily editing, rewriting, and reorganizing the final product.

This has helped me invite students into a conversation about using GenAI in which they are comfortable asking questions and engaging in dialogue. I am delighted when students share how they have used GenAI for personalized learning by creating study guides, increasing their understanding of examples from class they didn’t understand the first time, and even creating daily practice problems to test their mastery.

2. Model how to use GenAI as a professional.

While fostering an open classroom dialogue helps manage the current semester, faculty must also prepare students for what comes next. Students are seeking guidance about how to use GenAI professionally, and, in my experience, their universities and professors are not providing it. If we want students to use AI professionally, we must show them what that looks like.

We need to model how to use GenAI by sharing our own processes, from brainstorming to drafting, and demonstrating how responsible GenAI use is a core professional competency, not an academic shortcut we want students to avoid.

I started including GenAI use statements in my course materials to model professional use and encourage their adoption in professional settings. When I co-created a new in-class exercise with GenAI, I added a GenAI statement to the bottom of the document:

Generative AI statement (April 2026): This exercise was initially drafted with the assistance of Gemini 3 Pro, but the final version was fully vetted and authorized by me. I critically edited and manually confirmed all mathematical logic and course alignment. Any errors are my responsibility alone.

It is my intention that the GenAI use statements I provide can serve as templates that can be adapted for various workplace settings.

To accelerate my own learning, I took the advice of Tom Koulopoulos, a keynote speaker at the 2024 INFORMS Analytics Conference, to find “reverse mentors” to accelerate my GenAI learning. I have benefited from a former PhD student who helped me see some of the possibilities for responsibly using GenAI in professional settings, and how to discuss it openly with digital natives while not having all the answers.

3. Rethink assessment.

If an assignment can be completed by a prompt, it may not be a valid tool for assessing learning.⁵ Take-home exams and essays are quickly becoming obsolete as a primary assessment tool. To protect the integrity of our degrees, we must rethink how we assess learning.

Many faculty are shifting toward adopting assessments that require students to demonstrate their learning in real-time, such as in-class exams and project presentations that allow faculty to ask follow-up questions to probe students’ critical thinking.

Oral exams are making a comeback and are quickly becoming a new best practice.⁶ These exams are not unlike what occurs in professional environments, in which employees work collaboratively on teams, share their work, and ask one another questions. While professionals don’t receive grades, there are real consequences if they don’t understand their work, such as missing out on professional advancement opportunities or recognition.

In my course on engineering economics, I directed student teams to record a short video essay (under five minutes) and hand in a written report. Since such reports increasingly appear to be heavily edited with GenAI, oral communication is becoming an important professional skill. In the video essays, students explained their approach, assumptions, and findings in their own words, which offered a window into each team’s project and made it easier for me to assess their learning. I have since replicated this successful experiment for other assignments.

Today’s Students Are Tomorrow’s Professionals

While the rise of GenAI presents a challenge for higher education, it also offers an opportunity. We can move beyond a reactive stance and embrace our role as partners in this journey together, where we guide students in navigating this new landscape with curiosity, openness, and humility, and our current students help us better prepare future generations of students for new professional environments. By focusing on culture rather than consequences, we can ensure our students develop the critical thinking skills they will need for a world where GenAI is a partner in their intellectual and professional lives.

Author’s Note: An earlier version of this article appeared on Punk Rock Operations Research.

GenAI statement: I wrote this article myself and used Google Gemini 3 to lightly edit it for grammar and flow. I reviewed and vetted all suggestions, and I am fully responsible for the final content. 

References

1. Legatt, A., 2025, “90% of College Students Use AI: Higher Ed Needs AI Fluency Support Now,” Forbes.
2. Huddleston, S., 2025, “How Are Instructors Talking About AI in Their Syllabi?” The Chronicle of Higher Education.
3. Teaching in Higher Ed (podcast), 2026, “Skepticism and Curiosity in the Age of AI with Marc Watkins.”
4. Albert, L., 2025, “Artificial intelligence in Systems: Integrating AI Into the Engineering Curriculum,” http://dx.doi.org/10.2139/ssrn.5240570   
5. Mollick, E., 2024, “Post-apocalyptic Education,” One Useful Thing.
6. Shirky, C., 2025, “Students Hate Them. Universities Need Them. The Only Real Solution to the A.I. Cheating Crisis.” The New York Times.
7. Chirikov, Igor. "How Instructors Regulate AI in College: Evidence from 31,000 Course Syllabi." Higher Education Working Paper Series 26.1 (2026).