Education That Works With — Not Against — AI by Juan M. Lavista Ferres: Juan M. Lavista Ferres, Chief Data Scientist at Microsoft, on assignments that properly test students’ abilities

A little more than three years ago, OpenAI released ChatGPT, and education changed forever. For students, the ability to generate fluent, credible text on demand in seconds is an incredible new tool. For educators, it is a new kind of challenge. In the coming year, I hope the education community will make peace with AI as an educational tool and focus on developing reliable ways to evaluate student performance in the era of generative media.

In the months that followed ChatGPT’s arrival, a comforting story was widely shared: If generative AI could write essays, then we could build AI detectors to identify them. Some early studies reported near-perfect accuracy in controlled settings. The implicit promise was appealing: teachers would not need to rethink assessment. We could keep the same workflows, the same assignments, the same enforcement model.

That hope was an illusion. In a lab, these systems can perform very well. But their performance assumes that students will submit the raw model output. They won’t. The moment there is a detector, students have an incentive to evade it. And evasion is not difficult. Rewrite a paragraph. Add a few typos. Change sentence lengths. Reorder sections. Insert personal anecdotes. Translate and re-translate. Or use any of the growing set of tools that exist to rewrite AI output to look “human.”

This is the structural problem: If you can build a system that detects AI-generated text, then you can use that system to train a system that defeats it. The moment a detector is deployed, entrepreneurs will build products to break it, and students will learn to use them.

But the biggest problem is not designing effective detectors. It is maintaining trust. If educators rely on detector scores and students rely on programs designed to defeat detectors, educators are pushed into suspicion and adjudication. You end up confronting students, navigating appeals, and making high-stakes judgments without reliable evidence. You risk harming students, especially non-native English speakers, and students who have learned to follow certain academic conventions. Meanwhile, the students most committed to misuse will adapt fastest. So in practice, detection can penalize the wrong people while failing to deter the most sophisticated evasion.

Generative AI can improve learning. It can help students practice, give feedback, and deliver tutoring. It can translate material into a student’s own language and help personalize learning at scale.

But we need to be realistic. The traditional take-home essay, used as a universal proof of independent authorship, is broken. Verifying independent authorship through text alone no longer works at scale. Universities and schools should assume students will use generative AI, and they need assessment models that still work in that reality.

A few practical moves: 

• Use authentic demonstrations of understanding. In-person exams, oral defenses, live writing, presentations, and project walk-throughs make comprehension and ownership visible. 
• Teach AI literacy. Verification, citation, bias awareness, and responsible use should be part of the curriculum, not an afterthought. 
• Design for AI, not against it. For take-home assignments, assume students will use these tools. Build work that incorporates them responsibly, and assess students’ judgment, reasoning, and the ability to apply knowledge.

The genie is out of the bottle. There is no way to put it back. Our job now is to build the rules and practices that make education more effective, and more trustworthy, in the world we actually live in.

Juan M. Lavista Ferres is chief data scientist at Microsoft and a corporate vice president. He directs the Microsoft AI for Good Lab and the Microsoft AI Economy Institute.