I advise education technology and AI-enabled learning organizations on the design, evaluation, and improvement of literacy and learning tools — grounded in reading science, learning science, and rigorous, independent evidence.
Research Scientist, MIT McGovern Institute · Research Assistant Professor, Boston University · Director, EVAL Industry Collaborative
of AI tools used in K–12 education have undergone independent validation. I help organizations close that gap — building the rigorous, decision-relevant evidence that schools, families, and funders can trust.
I partner with edtech and AI-enabled learning organizations to strengthen the connection between learning science, product design, evaluation, and real student learning outcomes — clarifying what works, for whom, and under what conditions, across diverse classrooms.
I bring roughly two decades of research on reading, language, dyslexia, and the cognitive neuroscience of literacy, with deep expertise in evaluation and causal study design. I am a Research Scientist at MIT's McGovern Institute, a Research Assistant Professor at Boston University, and director of the Evidence-Based AI in Learning (EVAL) Industry Collaborative, which conducts rigorous, independent evaluation of AI-enabled learning tools. This combination lets me help partners align product design with the science of reading and turn everyday learning into credible, cumulative evidence.
I partner with education technology and AI learning companies, literacy intervention developers, tutoring platforms, assessment and curriculum teams, foundations and philanthropies, research organizations, and school and implementation partners. I also advise funders and investors conducting evidence diligence on learning products. Across these partnerships, the shared aim is the same: building effective, evidence-based learning tools that benefit students.
EVAL, the Evidence-Based AI in Learning Industry Collaborative, conducts rigorous, independent evaluation of AI-enabled learning tools and their impact on K–12 student learning outcomes. Through EVAL and related advisory work, I help organizations think systematically about how evidence is generated, interpreted, and used to improve learning technologies.
EVAL works to move the field beyond isolated proof points toward cumulative, decision-relevant evidence that can inform product improvement, implementation, and responsible adoption. My individual consulting and advisory work shares this evidence-building mission; each engagement is structured to match the question at hand and the standard of independence a given claim calls for.
Engagements are structured around the organization's goals, timeline, product stage, and evidence needs. They may take the form of targeted advisory calls, project-based consulting, independent product and research reviews, evaluation-design partnerships, evidence roadmaps, workshops and briefings, ongoing retainers, or sustained research–practice partnerships.
Consulting and advisory engagements are scoped individually around the organization's goals, timeline, product stage, and evidence needs, with structure and scale set to match the work.
To discuss consulting, advisory, or evaluation partnerships, contact me to talk through scope, timeline, and availability.
ola.ozernovpalchik@gmail.com