Our Approach

Past mistakes show that adopting technology without rigorous evaluation can limit impact and deepen inequities. EVAL offers a practical framework that unites education, cognitive science, and implementation research to evaluate AI in education at scale. Through close collaboration with schools, districts, and industry, we test not only whether generative AI works, but for whom and under what conditions—ensuring it strengthens, rather than disrupts, effective teaching and learning.

EVAL turns vision into practice by combining deep content expertise with methodological rigor and meaningful partnerships. Our approach ensures that AI tools in education are evaluated not just for novelty, but for real impact on student learning.

Content Area Expertise

We draw on decades of research in literacy and language arts, mathematics education, special education and disabilities, early childhood education, social-emotional learning, and multilingual education. This breadth of expertise allows us to evaluate AI tools in ways that are aligned with what we already know about how students learn across domains.

Research Design and Statistical Excellence

Our team brings gold-standard research design to AI evaluation, including experimental and quasi-experimental studies, advanced statistical modeling, mixed-methods integration, and psychometric validation. We measure not only whether tools work, but also for whom they work best, and under what conditions.

Participatory Partnership Model

We believe that the best solutions are co-designed with educators, students, and families. Our partnerships span urban, suburban, and rural school districts, and we work closely with teachers, administrators, and local communities. By combining practitioner insight with research expertise, we ensure that AI tools are equitable, practical, and relevant to classrooms.

Methodological Innovation

As AI transforms learning environments, evaluation must keep pace. EVAL develops adaptive research designs, new measures for digital learning, and scalable evaluation protocols that integrate learning sciences with computational methods. Our work is designed to keep up with the speed of technological change while maintaining the highest research standards.

Research Framework

All evaluations are conducted using rigorous Institute of Education Sciences standards. We assess evidence on three tiers—strong, moderate, and promising—with a focus on whether AI tools provide additive gains beyond traditional instruction. This ensures that technology represents a genuine improvement in learning outcomes, not just a substitution for existing practice.

Federal Alignment

EVAL directly supports U.S. Department of Education priorities for advancing AI in education through:

• Research-practice partnerships bridging academic inquiry and classroom implementation
• Education-specific AI evaluation rather than adaptation of general-purpose assessments
• Responsible innovation frameworks with data privacy protections
• Equity-focused evaluation ensuring AI reduces opportunity gap