Four trends at CES 2026: how artificial intelligence is retrofitting the classroom
At the consumer technology show CES 2026 in Las Vegas, experts in educational innovation rushed to one conclusion: the classroom of the future is undergoing a revolution. Ursula Saldivar, director of innovative technology at Tecnológico de Monterrey, during her visit to the show highlighted four technological shifts that are not just being discussed but are already being actively tested in real schools and universities.
These four directions — artificial intelligence, hyper-personalization of learning, project-based (experiential) learning, and hybrid educational models — became the focus of discussion at panels organized by experts from the Mexican university and representatives of the global tech industry.
How personalization works: from student errors to the future lesson
The mechanism of hyper-personalization of learning looks simple but powerful. The system tracks every student action: what answer they gave, how much time they spent on a task, where the error occurred. Based on this data, the algorithm in real time selects the next piece of material, adapting the difficulty exactly to their level and pace of learning.
A concrete example from American schools: a fifth-grade student solves a fraction problem. After three incorrect answers, instead of repeating the exercise or offering a new problem of the same difficulty, the adaptive textbook includes a short video with visualization explaining the concept clearly. Then the system offers a simpler exercise. If the student solves it, the difficulty gradually increases — but not in jumps, but smoothly. Thus, there is neither frustration from sharp failures nor boredom from too easy material.
What do pilots show? Cautious optimism
Preliminary results of pilot programs at Tecnológico de Monterrey itself record an increase in student engagement by 20–30 %. However, researchers deliberately emphasize an important limitation: this is data from the developer organization, not independent randomized controlled trials (RCTs), which are considered the gold standard of evidence in education.
Critics and education experts note a serious gap: long-term studies are practically absent. It is unknown whether the effect of personalization persists after a year of use. It is unclear how the teacher's role changes when scaling — whether the educator becomes an observer or remains a key participant in the process. And most importantly: does personalization keep students in education over the long haul, or do they get used to constant adaptation and lose motivation to overcome difficulties?
The problem technology does not solve: the digital divide
Behind the optimistic engagement growth numbers lies a darker picture. Hybrid models and AI tools require three conditions: reliable, high-speed internet, modern devices (tablets, laptops), and electricity. These resources are not available to all schools, especially in developing economies or rural regions of wealthy countries.
The result is paradoxical: technology created to individualize learning risks widening the existing gap between resource-rich schools (which can afford the full set of tools) and ordinary educational institutions (which remain with traditional methods). Instead of reducing inequality, AI-driven personalization may deepen it — if it remains a privilege of the few.
The question facing education
Behind the rapid adoption of AI in the classroom lies one fundamental concern: what will happen if schools begin to rely on algorithms far more than on live human contact between student and teacher? Technology can optimize knowledge transfer, but can it replace mentorship, spontaneous dialogue, the unexpected insight that happens in the classroom? Answers to these questions are still ahead.
