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Maryland is the only state in the country where the national center for AI research security (NSA at Fort Meade), the world's largest biomedical research facility (NIH in Bethesda), and one of the world's most prolific AI research universities (Johns Hopkins University) all sit within 40 miles of each other. That concentration shapes Maryland's education AI landscape in a way that cannot be replicated from the outside: the state has a documented, measurable demand signal from federal agencies for AI-competent graduates, and its K-12 and higher education systems are close enough to those institutions to feel that demand directly. The Maryland State Department of Education (MSDE) under Superintendent Mohammed Choudhury has been one of the more proactive state education agencies on AI policy, issuing the Blueprint for Maryland's Future technology guidance in 2023 that explicitly addressed AI literacy as a cross-curricular competency. Montgomery County Public Schools — MCPS, the 16th largest district in the country with 165,000 students — is the state's bellwether for AI education adoption, partly because its student demographics (42% white, 32% Hispanic, 14% Asian) and household income distribution mirror the national average more closely than most large suburban districts. Baltimore City Schools, with 78,000 students and a 70% poverty rate, represents the opposite end of Maryland's educational spectrum and has been the beneficiary and sometimes the subject of Maryland's equity-focused AI oversight framework. The University of Maryland in College Park, Johns Hopkins, and UMBC (University of Maryland, Baltimore County) form a research triangle that generates AI education research at a volume that directly influences what MSDE considers evidence-based.
Updated June 2026
Montgomery County Public Schools functions as Maryland's K-12 AI education reference point in the same way MCPS functions as a national reference point for suburban district educational governance — whatever it does gets watched. MCPS's 2024 AI in Education Task Force produced a 60-page framework that has been cited by six other Maryland county school systems in their own AI policy development. The framework's distinctive feature is its distinction between 'AI as infrastructure' (tools that automate administrative tasks, grade-level data reporting, and IEP documentation — approved broadly) and 'AI as pedagogy' (tools that directly influence student learning pathways — approved only with demonstrated equity impact data). MCPS is a linguistically complex district — 165 languages spoken, 31,000 ELL students — and its ELL department's AI tool evaluation criteria have been among the most rigorous in the country. In 2024, MCPS piloted Ellevation's AI-assisted ELL progress monitoring tool in 22 schools, generating one of the largest real-world datasets on AI-assisted ELL progress monitoring in a multilingual suburban district context. The results were mixed enough that MCPS chose not to expand to district-wide deployment in 2025 — and that decision has informed how other Maryland districts approach ELL AI tools, because MCPS's evaluation process is trusted enough that its conclusions carry weight beyond its own borders.
Johns Hopkins University's Center for Technology in Education (CTE) — the oldest and most prolific education technology research center at any university in the country — has been generating AI-in-education research since before the term 'AI education' was in common use. Its current work on AI-assisted reading intervention, predictive dropout risk modeling, and AI-based special education service coordination is directly influencing MSDE policy in ways that the center's university-government proximity enables: researchers at JHU CTE regularly participate in MSDE's AI guidance development working groups, closing the gap between published research and state education policy. The University of Maryland College Park's College of Education and its Institute for Advanced Computer Studies have been the primary source of AI fairness research applied to K-12 — the UMIACS Center for Machine Learning's work on algorithmic bias in student assessment tools has been cited in MSDE guidance on AI procurement equity requirements. UMBC — which ranks third nationally in producing STEM PhDs who go on to earn PhDs at other institutions, and first in producing STEM PhDs among minority-serving institutions — has made AI literacy a formal institutional priority through its Center for Information Security and Assurance (CISA), which has direct ties to the NSA and its National Centers of Academic Excellence in Cybersecurity program. UMBC's AI education work is distinctly shaped by this national security adjacent context: AI tools are evaluated not just for learning outcomes but for data security and foreign adversary exposure risk — a consideration that is unusually visible in Maryland given the NSA's institutional presence.
Baltimore City Schools serves 78,000 students in a city where 70% qualify for free or reduced lunch, 19% are English language learners, and the district has been under state oversight through the Blueprint for Maryland's Future accountability framework since 2021. The Blueprint's AI provisions — including requirements that technology investments be tied to documented equity outcomes — have made Baltimore City Schools one of the more carefully monitored AI adoption environments in the state. The district's 2024 ESSER spend included $12 million in AI-adjacent technology investments, primarily in reading intervention tools (specifically Amplify Reading with its AI diagnostic component) and AI-assisted IEP documentation tools that have reduced special education paperwork hours by an estimated 2.5 hours per case per week. The Baltimore Education Research Consortium (BERC), a partnership between Baltimore City Schools and the Johns Hopkins School of Education, provides the independent evaluation infrastructure that MSDE requires for equity accountability — any AI tool deployed in Baltimore City Schools that is funded through Blueprint accountability grants must be evaluated by BERC within 18 months of deployment. That requirement is unusual in K-12 nationally and has created a culture of evidence-based adoption in Baltimore City that is more rigorous than most peer urban districts. In practice, the gap between a promising AI ed-tech pitch and a funded Baltimore City deployment is a BERC-compatible evaluation design — vendors who propose one from the start move significantly faster through procurement.
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