AI is reshaping how educational institutions operate, but adopting it responsibly requires more than technical fluency — it requires leadership.
The Artificial Intelligence Leadership in Education (AILE) concentration prepares you to lead the adoption and governance of AI in educational settings, connecting institutional priorities, ethical frameworks, and organizational change to the realities of how AI systems are designed, evaluated, and deployed.
Who is AILE Built For
AILE is designed for current or aspiring educational leaders who need to make consequential decisions about AI — and want to make them well. Strong candidates typically have 3 or more years of experience in education or related fields such as corporate learning development, government and military education, healthcare, and clinical education, and are already operating in or moving toward leadership roles. Common backgrounds include school or district administration, higher education leadership, instructional technology and ed-tech implementation, curriculum and academic program management, and education policy or strategy. You don’t need a technical background, but you should have a genuine interest in understanding how AI systems work, not just what they produce. If you find yourself evaluating ed-tech vendors, navigating institutional AI policy, or fielding questions from faculty and staff about AI adoption, this concentration was built with you in mind.
What You’ll Focus On
Two of this concentration’s three courses are offered through the Johns Hopkins Whiting School of Engineering, placing you in interdisciplinary classes alongside peers from engineering and related fields. This structure gives you direct exposure to foundational AI concepts and contemporary development practices while situating your work within broader technical and organizational conversations.
Across the sequence, you’ll develop practical fluency with contemporary AI approaches — including generative AI — while grounding your work in responsible AI principles. Coursework covers core AI concepts and vocabulary, data quality and bias, privacy and security, risk identification and mitigation, and the broader implications of AI for educational practice. You’ll carry out hands-on applied work with current development and evaluation patterns, including prompt and workflow design, solution testing and validation, and architectures such as retrieval-augmented generation (RAG) and agentic systems.
What You’ll Be Prepared For
The goal is not to make you an AI developer. Instead, this technical grounding supports informed leadership: interpreting technical claims, asking better questions of vendors and internal teams, setting appropriate requirements and guardrails, and overseeing implementation in ways that align with educational goals, institutional values, and stakeholder trust. Explore a career path in AI Leadership.
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Master of Education in Learning Design and Technology
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