We’re living through a watershed moment in education. As AI tools become as common as calculators once were, educators are grappling with a fundamental question: How do we assess learning when students can generate polished essays, code, and presentations with a few keystrokes?
The answer isn’t to ban AI or pretend it doesn’t exist. Instead, forward-thinking educators are discovering that this challenge is actually an opportunity to build better, more meaningful ways to evaluate student learning.
The Product vs. Process Revolution
For decades, we’ve relied on what educators call “product-based” assessment—the final essay, the completed project, the finished presentation. But when AI can produce these artifacts in seconds, we need to shift our focus to something machines can’t replicate: the human learning process.
This means asking different questions. Instead of “What did you create?” we’re asking “How did you learn? What sources did you evaluate? How did you synthesize conflicting information? What would you do differently next time?”
This isn’t just about preventing cheating—it’s about cultivating skills that will serve students long after they graduate.
Four Practical Strategies for the AI Era
1. Reframe Academic Integrity as Digital Literacy
Rather than playing whack-a-mole with AI detection tools, successful institutions are teaching students how to use AI responsibly. The University of Michigan and Mesa Community College have developed tiered AI policies that give faculty clear guidelines for different types of assignments.
Action step: Create course-specific AI usage policies. Be explicit about when AI is allowed, prohibited, or required to be documented. This transparency helps students make ethical choices and prepares them for professional environments where AI use is expected.
2. Focus on Skills AI Can’t Master
When AI can write a decent first draft, the most valuable skills become those that require human judgment: evaluating AI-generated content for accuracy, synthesizing information from multiple sources, and applying critical thinking to complex problems.
Action step: Design assignments that require students to critique, compare, or build upon AI-generated content. For example, have students fact-check an AI-written article or improve an AI-generated business plan with real-world constraints.
3. Make the Learning Process Visible
Process-based assessment means documenting the journey, not just the destination. This includes research logs, peer feedback sessions, reflection journals, and iterative drafts that show how thinking evolves.
Action step: Implement “learning portfolios” where students document their research process, decision-making, and revisions. Use tools like collaborative documents or video reflections to capture thinking in real-time.
4. Embrace Real-Time, Collaborative Assessment
Some of the most effective AI-era assessments happen in real-time: presentations with Q&A sessions, collaborative problem-solving exercises, or live case study discussions where students must think on their feet.
Action step: Replace some traditional assignments with synchronous activities. Consider “fishbowl” discussions, peer teaching sessions, or live problem-solving challenges that reveal student understanding in ways AI can’t replicate.
Building Community in an AI World
Perhaps the most important shift is recognizing that our learning communities—the relationships between students, faculty, and peers—become more valuable, not less, in an AI-rich environment. While AI can generate content, it can’t build trust, provide emotional support, or create the kind of collaborative learning that happens when humans work together toward shared goals.
This means investing in discussion forums, study groups, peer review processes, and mentorship relationships. It means creating spaces where students can safely experiment, fail, and learn from each other.
The Path Forward
The rise of AI doesn’t signal the end of meaningful assessment—it signals the beginning of more authentic, human-centered evaluation. By focusing on process over product, collaboration over competition, and critical thinking over content creation, we can prepare students for a world where working alongside AI is the norm, not the exception.
The institutions that thrive in this new landscape won’t be those that resist change, but those that embrace it thoughtfully, with clear guidelines and a commitment to developing the uniquely human capacities that no algorithm can replicate.
The question isn’t whether AI will change education—it already has. The question is whether we’ll use this moment to build something better.
Stacy Ybarra, Ed.D., is a highly accomplished educational leader with over 15 years of experience in higher education. She specializes in teaching and learning innovation, faculty development, curriculum design, and educational technology. Dr. Ybarra currently serves as the Director of the Center for Teaching Excellence at Our Lady of the Lake University. She is also an Adjunct Faculty member at Alamo Colleges District -San Antonio College.
In her role at Our Lady of the Lake University, Dr. Ybarra provides strategic leadership for the Center for Teaching Excellence.She led the transition to the Blackboard Ultra LMS, Coursera and E-Cornell on Demand on campus. Her work focuses on building collaborative partnerships and leading large-scale initiatives that promote equitable student success.
Dr. Ybarra earned a Doctor of Education in Educational Leadership from Capella University, a Master of Arts in Counseling and Guidance, and a Bachelor of Applied Arts and Sciences in Human Relations from Texas A&M University – San Antonio. Her research interests include Educational Leadership, Online Learning, Professional Development, Artificial Intelligence in Education, and Community Colleges. She has been recognized for her contributions to education, including being named the 2024 Outstanding Former Student of Palo Alto College.
