The LEVI Trialing Hub Evidence Matrix: Providing Progressive Measures of AI-Driven EdTech Research & Development

Abstract

The rapid growth of education technology (ed tech) tools, including AI-powered applications, has highlighted the need for robust evaluation frameworks, particularly at early development stages. Current evaluation models, such as the ESSA evidence tiers, effectively guide education evidence collection but fail to address the complexities of emerging AI-driven interventions. To support the Learning Engineering Virtual Institute (LEVI), a program targeting doubled math learning rates in middle school students, we have developed a new evidence matrix to bridge this gap. This matrix incorporates a two-dimensional approach that evaluates research methods alongside outcome variables, enabling nuanced assessments of interventions. By categorizing research into five levels—ranging from randomized controlled trials to qualitative studies and modeling efforts, this matrix ensures comprehensive evaluation. Complementary outcome measures, emphasizing math learning gains, engagement, and model performance, contextualize these findings. This framework fosters alignment between research rigor and practical application, offering valuable insights into scaling educational innovations responsibly.

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