Students complete 30 credits beginning with three required core courses: CEP 808 (Introduction to Educational Measurement), CEP 834 (Inference in Educational Statistics), and CEP 835 (Artificial Intelligence (AI) and Data Science in Education).
They then complete six elective courses (18 credits) selected from offerings such as CEP 819, CEP 821, CEP 823, CEP 826, CEP 863, and CEP 867, allowing them to tailor their studies to interests in areas including experimental design, causal inference, multilevel analysis, AI for data collection and analysis, advanced statistical modeling, path analysis, and AI ethics.
The program concludes with CEP 898 (Educational Statistics and AI Capstone), where students apply what they have learned to an authentic educational research or data science project. Throughout the curriculum, students develop the statistical, technical, and ethical skills needed to analyze educational data and responsibly leverage AI in educational contexts.
Financial aid and scholarship options are available. Please visit the financial aid page and Graduate School Funding Opportunities page for application deadlines and requirements.
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.
The program offers a flexible, fully online format, typically completed within two years depending on individual pacing.
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.
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 M.A. is 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 decision-makers, and design ethical, data-driven 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.