Chris Brasil

Global Challenge: I explored how artificial intelligence and engineering practices can be leveraged to promote global educational equity, examining the gap between technological capability and accessible learning.

Abstract:

In my paper, I look into how artificial intelligence can be engineered to advance global educational equity, a focus that was shaped by my experience in the GLASS program. I have seen how AI-driven systems transform industries through personalization, automation, and scalability, yet their benefits remain unevenly distributed especially in education. Despite the rapid growth of digital learning tools, millions of students worldwide still lack access to reliable internet, devices, and quality instruction. Through GLASS, I came to understand that this gap is not simply a technological limitation, but a systems-level challenge shaped by infrastructure, design, and access.

Drawing from research on adaptive learning and intelligent tutoring systems, I explore how AI can improve student outcomes through real-time feedback, personalized pacing, and data-driven instruction. At the same time, I address critical risks such as algorithmic bias and unequal access, which can reinforce existing disparities when systems are not designed inclusively. My academic and professional experiences in software engineering and machine learning have reinforced this perspective, showing me how technologies are often optimized for efficiency and performance rather than accessibility or equity.

I focus on AI-powered tutoring platforms as scalable solutions, particularly when designed for low-resource environments. Inspired by both my global exposure through GLASS and my hands-on technical experience, I propose the development of lightweight, offline-capable adaptive learning tools that prioritize inclusivity and cultural context. This work aligns directly with Sustainable Development Goal 4: Quality Education, which calls for equitable access to learning opportunities for all.

Ultimately, GLASS reshaped how I view engineering, not just as a technical discipline, but as a tool for global impact. It strengthened my commitment to building human-centered AI systems and continues to influence my goal of developing scalable, accessible technologies that expand educational opportunity worldwide.

Bio:

Born in New York, Chris Brasil is a Computer Science graduate at NYU’s Tandon School of Engineering. Fluent in English, Spanish, and Portuguese, Chris brings a global perspective to his work in technology and engineering. Through GLASS, Chris has focused on Quality Education as his UN Sustainable Development Goal, exploring how artificial intelligence practice can be intentionally leveraged to promote global educational equity.
At NYU, Chris has been an active member of communities like the Opportunity Programs and the Society of Hispanic Professional Engineers, promoting representation for underrepresented students in engineering. He gained hands-on industry experience as a Software Engineering Intern at Octozi, an AI-powered health tech startup. He prides himself in developing projects in countries he has visited like the UAE and Vietnam, developing Magic Mirror and Rapid Ride respectively.

As a GLASS Scholar, Chris has combined his technical background in software engineering with a commitment to social impact. His research looks into the gap between technological capability and accessible learning, arguing that closing education gaps is not solely a social challenge but a systems-level engineering problem requiring human-centered AI design and equity as a core constraint. Chris aspires to work at a major tech company early in his career before eventually co-founding a tech venture with his cousins across multiple engineering disciplines.