AI is pushing higher education to rethink how students learn, how faculty assess learning and what it means to maintain academic rigor in a rapidly changing environment. Clear expectations, responsible experimentation and a focus on human judgment can help institutions move beyond reactive policies to prepare learners for an AI-enabled future. In this interview, Robert Lee discusses how institutions can build practical frameworks for responsible AI use and prepare students with the judgment and AI literacy they need for an AI-enabled workforce.
The EvoLLLution (Evo): AI has changed the conversation on teaching and learning almost overnight. What do you think higher ed leaders most need to understand about this moment?
Robert Lee (RL): Higher ed leaders need to understand that this isn’t simply a technological moment or a blip. It’s a teaching and learning opportunity. AI is already part of our world and the student experience, so the question is no longer whether students will use it. That’s already been decided. The question is: How do we want them to use it, and have we made that understanding explicit?
That’s really the gap RAISE 5 was built to close. We had faculty who were excited about AI, others who were uncertain and every gradient in between. RAISE 5 gives faculty a practical, assignment-level framework that protects academic integrity while preparing students for a world where AI is already part of their daily lives. Institutions that lead in this moment will respond thoughtfully and build cultures around responsible innovation rather than simply react to change.
Evo: How can institutions move beyond AI bans and detection tools to create a practical learning framework that is going to prepare students to use AI responsibly and effectively?
RL: moving beyond bans and detection tools is about shifting our posture from control to clarity. Bans may feel decisive and detection tools may offer reassurance, but neither teaches students how to think with AI, question its outputs or use it ethically in their academic and professional lives.
A more productive approach is creating a practical framework that makes expectations visible before the work begins. That’s what RAISE 5 does. Every assignment is given one of five AI guidance levels, from full restriction to full integration, giving faculty and students a shared language and an upfront understanding of the rules.
Students shouldn’t have to guess what’s allowed, and faculty shouldn’t have to play detective. A consistent framework can preserve faculty autonomy while teaching students the discernment, transparency and ethical responsibility they’ll need to navigate an AI-enabled world.
Evo: What does it mean to graduate as an AI-ready learner, and how should that reshape curricula, assessment and the student experience?
RL: To me, an AI-ready learner is someone who can use AI with discrimination, not dependence. It’s a graduate who understands what AI can do, where it can fail, how bias can show up in its outputs and why human expertise and judgment still matter.
AI readiness isn’t simply technical fluency. It’s the ability to ask better questions, evaluate outputs, apply disciplinary knowledge and make ethical decisions about when, how and even if AI should be used. That means we must integrate AI literacy into the curriculum rather than treat it as an outside threat. Students need opportunities to learn about AI, learn with AI and learn when not to use it.
Assessments have to evolve alongside that. Ultimately, we want graduates prepared for an AI-enabled workforce while understanding that the most important tool they bring is still their own judgment.
Evo: How can institutions ensure they’re preparing learners for this very advanced future while maintaining academic rigor and trust?
RL: AI is already reshaping entry-level roles, which raises the stakes for graduates because those first jobs were traditionally where they built foundational skills. We can’t know exactly what the job market will look like in three or five years, but employers are still asking for graduates with durable skills, including the judgment to know when and how to use AI.
That means we need to assess not just what students produce but how they’re thinking with the tool. Students might document what AI they used, what it produced, what they accepted or rejected and how their disciplinary knowledge shaped the final product.
That kind of assessment creates transparency, reinforces rigor and builds trust. If students can evaluate AI-generated material, identify bias or inaccuracies and improve the output, they’re demonstrating judgment, not simply producing an answer. Ultimately, maintaining academic rigor and trust depends on making those expectations explicit and consistent.
Evo: What will distinguish the institutions that successfully embrace AI from those that simply react to it?
RL: What will distinguish leading institutions is their ability to evolve. AI is changing so quickly that a single policy or framework can become obsolete within a semester. Successful institutions will create structures that can be tested, revisited and refined as the technology evolves.
They’ll also employ a crawl, walk, run strategy—pilot before they scale. Rather than issuing a top-down mandate, they’ll learn, from real classroom experiences, how faculty are using AI and how students are responding. Most importantly, leaders need to move with faculty rather than around them.
Successful AI adoption requires leaders who are adaptive, transparent and willing to act before every answer is settled, while still grounding decisions in institutional values. It’s less about chasing the newest tool and more about creating a culture where experimentation can happen without abandoning rigor, where expectations are clear and where everyone can responsibly evolve together.
Evo: Is there anything you’d like to add?
RL: I’d really like to give a shout-out to our faculty. Dr. Torrence Temple spearheaded this initiative and worked with our teacher education department chair, Nilsa Thorsos, to bring together Sanford College of Education faculty—Drs. Belle Booker-Zorigian, Valerie Amber and Maggie Broderick—who were curious, innovative and thinking about the future of teacher education.
What I think is elegant about their framework is RAISE 5 doesn’t dictate how AI should or shouldn’t be used. It preserves faculty autonomy while providing a clear framework for both faculty and students. An assignment might intentionally encourage full use of AI tools, while another might require students to complete the work without AI.
As we started exploring how this tool could move beyond the teacher education department and across the university, that clarity really resonated with faculty, administrators and our learning experience designers. We weren’t saying, “You must do this.” We were providing guidance that makes expectations clear. And that approach has applicability not only across higher education but in K–12 settings as well.