EdM in AI & Education

Artificial intelligence is increasingly present in education, yet fundamental questions remain about whether, when, and how these tools should be adopted. This program prepares educators and leaders to critically engage with AI technologies, evaluating their potential benefits against their costs, limitations, and risks. Most students and educators now use AI every day, yet essential questions remain—which AI literacy skills matter for future study and work, how AI influences learning and development, how to tell which tools actually help, and how to use data and visualization to guide instruction and policy. This program answers those questions with a design that is rigorous and adaptive. You will learn to navigate, evaluate, and lead AI integration while keeping student learning and well-being at the center.

The Master of Education (EdM) in AI & Education is a 30-unit graduate degree that prepares educators, instructional leaders, and educational technologists to leverage data and scientific evidence to integrate AI in ways that inform instructional and policy decisions, support student learning, and drive curriculum implementation. The program also prepares educators to deliver comprehensive, evidence-based AI literacy instruction that builds students’ foundational knowledge for a rapidly evolving workplace.

Why This Program

Educational institutions are increasingly exploring AI technologies, creating demand for professionals who can navigate these opportunities with informed judgment and contextual wisdom. This program develops your capacity to:

• Evaluate evidence rigorously: Distinguish substantiated claims about AI’s impact from hype, using research methods to assess what works, for whom, and under what conditions.
• Apply historical perspective: Draw on lessons from past educational technology cycles to make wiser implementation decisions.
• Bridge technical and pedagogical expertise: Understand how AI systems work while maintaining focus on learning sciences and student well-being.
• Lead with values: Apply justice-oriented frameworks to ensure AI adoption serves equity, centers marginalized voices, and advances collective goals.
• Exercise informed judgment: Make contextually appropriate decisions about whether, when, and how to use AI based on evidence, values, and professional expertise.
• Design with intention: Create or select AI tools that preserve what matters most in education: human relationships, educator agency, and meaningful learning.
• Navigate complexity: Balance innovation with caution, efficiency with humanity, and individual benefits with collective well-being. The program combines technical skills with critical analytical frameworks, ensuring graduates can engage productively with AI while upholding the pedagogical integrity and humanistic values central to education.

Learning Outcomes

At the end of this program, students will be able to:

• Explain AI fundamentals in educational contexts. Articulate how AI systems function, their limitations and opportunities, and their implications for curriculum, instruction, and policy in educational settings.
• Integrate AI into teaching and learning. Apply principles from the learning sciences and pedagogy to design, adapt, and evaluate instructional experiences where AI supports student reasoning, reflection, and creativity while maintaining human agency and transparency.
• Analyze and visualize educational data. Collect, prepare, and interpret educational data using AI-enabled tools and visualization techniques to reveal learning patterns, equity gaps, and system-level trends that inform teaching and decisionmaking.
• Generate and communicate evidence for educational decisions. Design and conduct rigorous or rapid-cycle evaluations of AI-supported practice and translate results into actionable recommendations for teachers, administrators, and policymakers.
• Implement AI responsibly across systems. Develop strategies for adopting, scaling, and governing AI use in classrooms, schools, and districts with attention to interoperability, data governance, and sustainability.
• Ensure ethical and equitable AI practices. Identify and mitigate bias, protect privacy, ensure accessibility, and align AI implementation with human-centered and equity-centered design principles and relevant data protection policies.
• Collaborate and lead responsible innovation. Work across disciplines and stakeholder groups to guide professional learning, communicate effectively with families and communities, and create local frameworks for responsible and transparent AI integration in education.

Requirements

Course Requirements

Students complete seven core courses (28 units) covering the technical, pedagogical, and critical dimensions of AI in education, followed by a capstone project (2 units).

Core Courses

• WED AI 601 Foundations of AI in Educational Contexts (4 units)
• WED AI 605 AI in Teaching and Learning: Pedagogical Applications (4 units)
• WED AI 620 AI and Assessment of Student Learning and Experience (4 units)
• WED AI 645 AI in Education: Historical Perspectives and Design Approaches for Learning (4 units)
• WED AI 662 AI in Educational Data Analytics and Visualization (4 units)
• WED AI 665 Research Methods and Evidence in Educational AI (4 units)
• WED AI 695 AI Implementation and Professional Leadership (4 units)

Capstone

• WED AI 699 AI & Education Research-to-Practice Capstone (2 units)

Total: 30 units

Course Substitution: With approval from their academic advisor, students may substitute one of the required courses with an existing online graduate course offered by BU Wheelock that aligns with their specific disciplinary focus or professional role (e.g., literacy education, STEM education, special education, or educational leadership). This flexibility allows students to tailor the program to their individual professional contexts while maintaining the program’s core focus on AI in education. For example, students whose professional roles do not require advanced data analytics skills may choose to substitute WED AI 662 AI in Educational Data Analytics and Visualization with a course that deepens expertise in their specific disciplinary area or leadership focus.

Average Time to Completion

The Master of Education (EdM) in AI & Education is designed for working professionals and can be completed in as few as 18 months (part time).