Professor wins grant to study AI in early childhood education

A faculty member at SUNY Cortland has secured a competitive grant to explore one of the most overlooked corners of the artificial intelligence revolution: the lives and learning of very young children. Deborah Silvis, an assistant professor in the Childhood/Early Childhood Education Department, will serve as co-principal investigator on a project that has been awarded a $75,000 Vision Grant from the Spencer Foundation, a national philanthropic organization that funds research intended to transform education systems. The funding supports a one-year planning phase designed to lay the groundwork for a much larger application — a four-year Transformation Research Grant proposal that, if successful, would allow the team to pursue sustainable, scalable solutions to a problem researchers say is growing more urgent by the day.

The central question driving the project is deceptively simple but increasingly difficult to answer: how do young children encounter, understand and make sense of artificial intelligence, and what does that mean for their development at a moment when AI tools are threading themselves into nearly every dimension of daily life? Silvis and her collaborators at the University at Buffalo argue that early childhood — the period when children form their most foundational understandings of how the world works — has been largely absent from the national conversation about AI in education. Most research, policy debates and classroom interventions have focused on older students, high schools and universities, leaving preschools, daycares and early elementary classrooms to navigate a fast-changing technological landscape without a map.

“As AI is increasingly integrated into our society, the project addresses the urgent need for equitable AI education in the early years, when children are forming foundational understandings of the world,” Silvis said. Her scholarship, which examines the relationship between technology, child development and social change, has long focused on how children interact with digital tools and how those interactions shape — and are shaped by — the social contexts around them. The new project extends that line of inquiry into territory she believes has been neglected: the years before children can even articulate what an algorithm is, when their encounters with AI are mediated by voice assistants, adaptive apps, smart toys and the adults in their lives.

The researchers identify two interconnected challenges that define the current landscape. The first is invisibility. AI education, to the extent it exists at all, remains largely absent from early childhood curricula, professional development programs and policy frameworks. Young children are already interacting with AI systems — asking questions of voice-activated devices, watching algorithmically curated videos, playing with toys that respond to their speech — but the systems that structure their learning rarely acknowledge this reality, let alone equip caregivers and teachers to respond to it. The second challenge is fragmentation. Early childhood education in the United States is delivered through a patchwork of settings: public pre-kindergarten programs, private preschools, childcare centers, family home-based care and informal learning environments. Each of these settings operates under different regulations, funding streams and professional cultures, which means that even when promising approaches to AI literacy emerge in one context, they rarely travel to others. Fragmentation, the researchers argue, compounds the invisibility problem, scattering responsibility for a systemic issue across dozens of disconnected institutions.

The Vision Grant is designed precisely to address this kind of complexity before a full-scale research project is launched. Over the next twelve months, Silvis and her colleagues at the University at Buffalo will build the project’s conceptual research framework while cultivating community partnerships across Western New York. This is not a phase of data collection in the conventional sense; rather, it is a deliberate period of groundwork intended to ensure that the eventual four-year research agenda is grounded in the lived realities of the people it aims to serve. The team will work with local education leaders, community members and technology partners to co-design a research plan focused on solutions that are not only effective in local settings but hold the potential to scale nationally — a criterion that is increasingly central to how the Spencer Foundation evaluates transformative education research.

The emphasis on community partnership reflects a broader shift in education research methodology, one that treats teachers, caregivers and families not as subjects of study but as co-producers of knowledge. For a topic as charged and fast-moving as AI, this approach carries particular weight. Parents of young children are often navigating AI tools with little guidance, forming their own intuitions about what is safe, appropriate or beneficial. Early childhood educators, meanwhile, face the dual pressure of preparing children for a world saturated with intelligent systems while lacking training, curricular materials or institutional support to do so. Technology companies, for their part, are marketing AI-enabled products to families and schools faster than research can evaluate them. A planning grant that brings all of these stakeholders to the same table is, in effect, an attempt to build the coordination infrastructure that the fragmented early childhood sector currently lacks.

The stakes of this work extend beyond classroom pedagogy into questions of equity, which the researchers place at the center of the project. AI literacy — the ability to understand, question and critically engage with intelligent systems — is rapidly becoming a form of foundational knowledge, much like numeracy or early literacy. When AI education arrives late in a child’s schooling, or not at all, the benefits accrue unevenly, often along lines of family income, community resources and access to well-resourced schools. Children who encounter AI only as consumers of its outputs, rather than as learners capable of understanding how it works and whose interests it serves, enter a world shaped by these technologies at a structural disadvantage. Silvis and her colleagues contend that waiting until middle or high school to introduce these ideas is too late; the understandings children build in their earliest years shape how they relate to technology for the rest of their lives.

Silvis joined SUNY Cortland in 2023 and holds a Ph.D. in learning sciences and human development from the University of Washington’s College of Education, one of the leading programs in the country for research on how people learn across settings and over the life course. The learning sciences tradition, which integrates insights from cognitive science, developmental psychology, sociology and design, is well suited to a problem like AI in early childhood, where the relevant questions span what children can comprehend, how technologies are designed, what families and teachers do in practice, and how policies and institutions shape all of the above. Her published work has appeared in a range of peer-reviewed outlets, including the International Journal of Computer-Supported Collaborative Learning, the Journal of Early Childhood Literacy, Information & Learning Sciences, the International Journal of Child-Computer Interaction, Learning, Culture and Social Interaction, and Cognition & Instruction — a portfolio that traces a consistent intellectual arc from children’s collaborative learning with technology to the social and cultural conditions that make such learning equitable.

The collaboration with the University at Buffalo also situates the project within a broader institutional ecosystem in Western New York, where university researchers, school districts and community organizations have increasingly partnered on education innovation. Building those relationships during the planning year is not incidental to the project’s goals; the researchers view the partnerships themselves as part of the eventual solution. A research agenda produced in isolation from practitioners, they argue, would replicate the very fragmentation the project seeks to remedy. Instead, the team hopes the planning process will surface the specific conditions, constraints and assets of local early childhood settings — knowledge that can then inform both the four-year research design and the practical tools it produces.

The Spencer Foundation’s Vision Grant program occupies a distinctive niche in the education research funding landscape. Rather than funding only mature, fully specified research projects, it invests in the upstream work of agenda-building: convening partners, testing conceptual framings, conducting pilot conversations and developing the theoretical scaffolding that ambitious long-term studies require. The Transformation Research Grant that Silvis’s team will ultimately pursue is the foundation’s flagship mechanism for supporting research aimed at rethinking education systems rather than incrementally improving them. By securing a Vision Grant, the team has effectively been invited to make the case that AI in early childhood education deserves that level of sustained, system-oriented attention.

Whether the field rises to meet that challenge remains an open question, but the timing of the project is notable. Artificial intelligence is already embedded in the media children consume, the devices their families use and the platforms their schools are beginning to adopt — often without any intentional educational design. Researchers in child-computer interaction and the learning sciences have documented how children anthropomorphize voice assistants, attribute knowledge and intentions to AI systems, and absorb tacit lessons about how information works from the technologies around them. What has been missing, Silvis and her colleagues argue, is a coordinated research agenda that treats these early encounters as a matter of educational equity rather than an afterthought of technological change. The next twelve months of planning in Western New York will determine whether that agenda — and the four-year study behind it — can become a model for how the nation prepares its youngest learners for an intelligent, automated world.