The program offers a 30 credit online MA in Educational Statistics and AI and a set of fully online graduate certificates in Foundations of Educational Statistics, AI & Ethics; Educational Measurement & Data Analysis; Advanced Statistical Modeling for Education; and Capstone: Applied Educational Statistics and AI. Together these options provide flexible, stackable pathways for education professionals worldwide.
Students complete ten 3 credit courses—CEP 834, 835, 808, 845, 846, 867, 848, 849, 850, and 898—moving from foundational statistics and measurement through AI techniques, data visualization, advanced modeling, and an applied research capstone. The curriculum integrates FERPA/COPPA aligned data privacy content and emphasizes practical projects that apply methods to real educational datasets.
Yes. Courses are delivered in a fully online format designed for working professionals, and students may participate from anywhere in Michigan, across the U.S., or internationally. The program is structured as a flexible online option rather than a residential cohort.
Yes. The program is built for part time enrollment, with most students taking one or two courses (3–6 credits) per semester so they can balance coursework with professional and personal responsibilities.
Applicants should be comfortable with basic algebra and introductory statistics; the ten course sequence is designed to build advanced skills with support rather than assume a prior data science degree or computer science major.
Each graduate certificate consists of 9 credits drawn from the MA course set, and completed certificate coursework can count toward the 30 credit MA if students later apply and are admitted, subject to Graduate School and program policies in place at that time. This design allows students to “stack” credentials over time.
CEP 867 – Ethics of AI in Education focuses on ethical frameworks, equity, algorithmic bias, and privacy regulations such as FERPA and COPPA, and ethical reflection is threaded across methods courses and the CEP 898 capstone so students learn to use AI and data responsibly.
The MA and certificates are designed for education researchers, policy analysts, evaluators, K–12 and higher education professionals, and data scientists who want to specialize in education. Applicants are typically professionals seeking to deepen their quantitative and AI expertise while continuing to work.
Graduates learn to analyze complex educational datasets, apply statistical and AI techniques, interpret results for decisionmakers, and design ethical, datadriven solutions to problems of practice. They complete a capstone project that demonstrates applied mastery of educational statistics, AI tools, visualization, and responsible data stewardship.
Alumni will be prepared for roles such as educational data scientist, research analyst, learning analytics manager, AI ethics specialist in education, and education policy analyst in school systems, higher education institutions, research centers, nonprofits, and edtech organizations. Program materials highlight strong demand for professionals who can combine AI, statistics, and educational expertise.
Yes. Students gain hands on experience through applied course projects, independent study or internship options, and the CEP 898 capstone, where they work with real educational data or partner organizations to address authentic research or policy questions.
Applicants must hold a bachelor’s degree from an accredited institution and submit transcripts, a statement of purpose, résumé/CV, and (for the MA) letters of recommendation; GRE scores are optional. Specific term by term deadlines and application links will be posted on the Apply page and on the Graduate School’s application portal.
Yes. Michigan State University is accredited by the Higher Learning Commission, and the College of Education’s graduate programs in this area adhere to the university’s standards for quality, rigor, and continuous improvement. This program is not a licensure or certification program, but is designed for professionals seeking advanced preparation in educational statistics, data science, and AI.