AI in Early Education: How AI Could Widen Gaps in Early Learning Quality

In parts one and two of this series, we explored how overreliance on AI-generated lesson planning can compromise developmental appropriateness and devalue the expertise of early childhood educators. In this final post, we turn our focus to one of the most pressing concerns in early education: access. Specifically, we ask whether artificial intelligence helps create more access to high-quality early learning or risks widening the gap for children and families in under-resourced communities.

Widening the Quality Gap

AI-generated curriculum is often marketed as an efficient, scalable solution—particularly appealing for programs with limited budgets or staffing shortages. In my time as a state administrator, I often heard from school leaders in under-resourced areas that “something is better than nothing,” for everything from curriculum and assessment to professional development and coaching. Here’s what persistently bugged me when I would hear this refrain: this means that programs with more financial resources are better positioned to supplement AI tools with meaningful, teacher-led instruction, while underfunded programs may come to rely on AI as a cost-saving measure.

The result? A deepening divide. In affluent programs, AI may serve as a minor convenience; in under-resourced settings, it risks becoming the primary driver of instruction—delivering standardized, generic content that cannot adapt to children’s real-time needs or unique classroom dynamics. This creates two different worlds of quality for early education: one where children benefit from thoughtful, human-centered instruction, and another where learning is filtered through impersonal algorithms.

Ignoring Cultural and Community Context

High-quality early childhood education is not one-size-fits-all. It is deeply responsive to the cultural, linguistic, and community contexts of the children it serves. It draws on local knowledge, family traditions, home languages, and lived experiences to create learning that feels relevant and affirming.

AI, by contrast, is limited by the data it was trained on—and that data may not reflect the rich variety of our communities. It may fail to include culturally relevant materials or to recognize differences in communication styles, traditions, or values. Teachers, especially those embedded in the communities they serve, bring this cultural competency to their work every day. AI cannot replace that.

Exacerbating Educational Imbalances

The risk is clear: if AI-generated curriculum is viewed as a budget-friendly solution for under-resourced programs, the children most likely to experience limited access to enriching opportunities will be the same children most likely to receive impersonal, lower-quality instruction. Meanwhile, children in better-resourced programs continue to benefit from rich, individualized, teacher-driven learning environments. Rather than closing opportunity gaps, we risk reinforcing them, delivering the fewest resources to the children who need the most.

A Call to Center Educators in the Age of AI

As we close this series, we invite educators, policymakers, and families to reflect on what truly drives quality in early childhood education: human connection, professional expertise, and responsive teaching. While AI and technology can play a supportive role in streamlining tasks or supplementing instruction, they should never replace the deep knowledge, intuition, and relationships that educators bring into the classroom each day. Rather than turning to AI as a quick fix for systemic challenges, we must commit to investing in the workforce through meaningful professional development, coaching, and tools that empower educators to meet the diverse needs of children. Access, quality, and innovation are not at odds—they are fully possible when we choose to center teachers and empower them with the right kind of technology in our vision for early learning.