More Options, More Opportunities: How AI Can Support High-Quality Learning for All

By: Michael Ham and Beth Holland

The current administration has prioritized expanding school choice, reflecting a growing demand for flexible learning options. At the same time, advances in AI—and the emergence of “AI-powered schools”—are reshaping conversations about the future of education. As these shifts accelerate, ensuring every learning model—traditional, virtual, or hybrid—offers high-quality experiences is essential.

This is not a new challenge. The field has long debated the value of virtual and hybrid models. While these approaches have demonstrated their potential, a persistent myth suggests they are inherently lower quality due to concerns about student disconnection and instructional rigor. In reality, quality isn’t about where learning happens but how it’s designed—and the implementation of AI presents a powerful opportunity to strengthen that design.

Understanding What Drives Quality Learning

Understanding how AI can support high-quality learning for all students begins with understanding what drives quality learning. Today’s high-quality learning models are built on three key drivers:

• Pedagogy that is student-centered, mastery-based, and designed to engage students in meaningful, rigorous instruction that develops essential skills over time;
• Relationships between teachers, students, and peers that foster trust, belonging, and motivation; and, 
• Technology that is intentionally integrated to support and enable pedagogy and relationships and to facilitate collaboration, personalization, and instructional effectiveness without replacing human connection.

When intentionally designed around these quality drivers, virtual and hybrid learning can be just as rigorous, engaging, and relational as in-person learning, and schools and programs nationwide are proving this. As we’ve learned through collaboration with models from across the country, high-quality virtual and hybrid programs leverage technology to support, rather than replace, powerful pedagogy and meaningful relationships. For example:

• Friendship Online Academy, a fully virtual high school, pairs rigorous instruction with deep relationship-building through town halls, in-person events, learning coaches, and family outreach—all while offering AP courses, dual enrollment, and robust academic pathways.
• Da Vinci Connect TK-8, a hybrid homeschool model, blends personalized, project-based learning with family co-teaching and community engagement, offering flexibility without sacrificing rigor.
• Novi Virtual, a K-12 virtual program, supports students facing chronic absenteeism, high mobility, or behavioral challenges through a seamless curriculum, individualized support, and strong teacher-student relationships.
• Bismarck Public Schools Empower[Ed] Hybrid Program allows students to choose their learning modality while engaging in interest-driven projects that align with core academic standards, ensuring mastery of essential content, real-world skill-building, and strong connections with educators and peers.

These models demonstrate that when technology is integrated in the service of pedagogy and relationships, virtual and hybrid learning can provide rigorous, flexible, and connected educational experiences that meet the highest standards of quality, regardless of modality.

AI as a Tool for Enabling High-Quality Learning

Like any technology, AI doesn’t drive quality on its own—its impact depends entirely on how it is used. One of the most common early uses of AI is automating time-consuming tasks like grading, data analysis, and content generation. While these efficiencies reduce teacher workload, they don’t inherently improve learning. 

However, these practices can become powerful drivers of high-quality learning if we ask one key question: how is this saved time reinvested? It might create space for more direct teacher-student interactions, enable more individualized feedback, or give teachers the capacity to focus more on relationship-building. 

When centered on how its use impacts pedagogy and relationships, AI shifts from a tool of efficiency to a driver of high-quality learning—freeing educators to focus on what we know matters most for student success.

AI as a Driver of Quality in Virtual and Hybrid Learning

As virtual and hybrid learning models evolve and offer more choice for students and families, AI presents an opportunity to enhance quality, strengthen instructional design, and expand access to flexible, student-centered learning. While these models offer greater flexibility, they require intentional design to ensure students experience the same rigor, engagement, and connection as their in-person peers. The challenge isn’t whether virtual and hybrid models can work—it’s ensuring they work well.

​​Across the country, educators are experimenting with AI-driven models that enhance, rather than replace, instructional relationships. One example comes from ASU Preparatory Academy, a participant in The Learning Accelerator’s Accelerating Adoption Network, who is developing Digit, an online math platform that helps high school students build Algebra 1 skills through targeted tutorials and practice problems.

Embedded within Digit is Archie, an AI-powered tool that provides real-time, personalized feedback, helping students grasp mathematical concepts while freeing teachers to focus on deeper instruction and relationship-building. Rather than replacing educators, Archie supports students in the moment, ensuring they receive timely, individualized support while reinforcing the instructional strategies that drive mastery-based learning.

AI can do more than automate tasks—it can expand learning options, improve instructional quality, and create more personalized experiences. But to achieve this, AI must be used to strengthen pedagogy, relationships, and engagement—not be used as a shortcut or substitute for human connection.

Call to Action: Designing for Quality, Together

The future of learning depends on leaders taking action today. High-quality, rigorous learning isn’t optional—it’s necessary to meet the diverse needs of today’s students and families.

Getting there will require urgency and intentionality. By using AI to enhance, rather than disrupt, high-quality learning, leaders can create more flexible, responsive learning opportunities that truly meet the needs of all students. To guide this work, TLA offers a range of actionable resources, from case studies to inform model design to emerging AI integration strategies and evidence emerging from the Exponential Learning Initiative. 

Michael Ham is the leader of The Learning Accelerator’s policy initiatives, he collaborates with sector leaders nationwide to create the conditions that foster equity-driven innovation in K-12 institutions and drive collective movement toward a world where every child has access to transformative learning.

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