Master of Education (EdM) in AI & Education

You’ll learn to lead AI integration with intention, going beyond surface-level tools to focus on human-centered AI. You’ll develop the judgment to know when AI can be used to support a student, and when we should step back to protect the deeper work of learning. Through a flexible 30-credit curriculum, you’ll gain the expertise to shape classrooms and schools where human judgment stays at the center.

You’ll learn how to apply evidence-based instructional design to AI-supported learning tools and assess whether they improve outcomes in real PreK–12 and higher ed settings. You’ll also integrate AI into teaching and assessment while keeping student learning and well-being at the center.

You’ll build the leadership toolkit to guide responsible AI adoption across PreK–12 classrooms, schools, and districts, as well as higher education and education organizations. Develop practical governance and implementation skills that address privacy, bias, accessibility (including disability and special education considerations), and appropriate use. Learn to evaluate vendor claims, set clear goals and guardrails, and create implementation plans that earn trust with educators, families, and decision-makers.

At BU Wheelock, you get research and hands-on experience. Boston University is an R1 university, and BU Wheelock brings deep research strength ($15.2M in external research funds in 2024) alongside real-world partnerships and field-based learning.

You’ll complete the part-time EdM in AI and education online, with support designed for working educators and education leaders. Course delivery details (live online vs. asynchronous), pacing options, and advising support will be shared by the program team, allowing you to choose the approach that best fits your schedule.

Courses span the full arc of AI in education—from foundations and learning design to assessment, data analytics, research methods, and implementation—culminating in a research-to-practice capstone. Courses include:

• Foundations of AI in Educational Contexts
• AI in Teaching and Learning: Pedagogical Applications
• AI and Assessment of Student Learning and Experience
• AI in Education: Historical Perspectives and Design Approaches for Learning
• AI in Educational Data Analytics and Visualization 
• Research Methods and Evidence in Educational AI 
• AI Implementation and Professional Leadership
• AI & Education Research-to-Practice Capstone 

Your capstone is built for real educational impact. You’ll create a polished, shareable leadership project in collaboration with an industry partner that demonstrates you can vet AI tools and claims, design with ethics and equity in mind, use data responsibly, and deliver clear recommendations to diverse stakeholders.

You’ll take on a real AI-and-learning challenge—then design, pilot, and evaluate an AI-supported practice or strategy with feedback from faculty and peers. Fully online and flexible across time zones, the capstone helps you graduate with proof you can lead implementation and instructional improvement.

You’ll be able to:

• Support AI integration in classrooms, schools, and districts
• Evaluate AI use with evidence, data, and clear success measures
• Design AI-supported instruction aligned to learning and student well-being

Example roles include:

• AI-Ready Classroom Teacher: Use AI to plan, differentiate, and give feedback more efficiently—while grounding every choice in evidence, student well-being, and equity.
• Classroom AI and Evidence Integration Coach: Make AI-supported instruction observable, explainable, and improvable.
• AI-Supported Assessment Designer: Use AI to scale high-quality, valid assessments.
• Accessibility and Language Equity Specialist for AI: Ensure AI improves access for multilingual learners and students with disabilities.
• Family and Community Engagement Lead for AI: Build trust and shared understanding.
• District AI Implementation Lead: Run end-to-end pilots and scale what works.
• Director for Teaching, Learning, and AI: Align AI with curriculum and professional learning.

You’ll be able to:

• Set clear guardrails for student data privacy, consent, and responsible AI use in PreK–12 contexts
• Assess and reduce risk, vetting tools for security, bias, and compliance, and monitoring performance over time
• Turn educational data into decision-ready insights and visuals that support transparent, accountable implementation

Example roles include:

• Student Data Privacy Officer for AI: Protect learners and ensure lawful, ethical use.
• Learning Analytics Lead: Turn raw data into decision-ready visuals.
• AI Risk and Safety Manager: Monitor models and practices over time.

You’ll be able to:

• Collaborate with ed tech teams to pilot and improve AI-supported products
• Translate educator needs into implementation plans and product feedback
• Support responsible data practices and evidence of impact in real settings

Example roles include:

• Education Partnerships Manager, AI and Data: Align vendors and research with instructional goals.
• Program or Product Liaison with Ed Tech Partners: Improve usability and impact through co-design.
• Implementation Scientist in Education: Study adoption in the wild and refine at scale.

You’ll be able to:

• Contribute to research and evaluation of AI in education settings
• Translate findings into guidance for schools, districts, or government agencies
• Support decisions tied to privacy, fairness, accessibility, and accountability

Examples roles include:

• AI Policy and Governance Lead: Create clear, enforceable guardrails.
• Research–Practice Partnership Fellow: Bridge districts on priority questions.