Artificial Intelligence & Learning Analytics
Master of Education Sciences
IS THIS PROGRAM A GOOD FIT FOR SOMEONE WITHOUT A TECH/AI BACKGROUND?
Yes! The MES-AILA program is designed for professionals from a variety of academic and career paths—including those with little or no prior experience in coding or AI. You’ll start with the fundamentals of educational data science and learning analytics, then build toward advanced applications. The goal is to develop both the skills and the mindset needed to work confidently as an educational data scientist.
CAN I DO THIS PROGRAM WHILE WORKING FULL-TIME?
Absolutely. All courses are fully online and asynchronous, allowing you to study on your own schedule. The program also offers optional live sessions and one-on-one check-ins to support collaboration across different time zones and working hours.
WHAT WILL I LEARN IN THESE COURSES?
Across nine courses, you’ll gain both theory and applied skills. By the end you’ll be able to:
- Apply principles from the learning analytics and cognitive sciences
- AI and Education to make sense of educational data
- Access, clean, manage, and analyze data from digital learning platforms
- Communicate results through writing, dashboards, and data visualizations
- Understand and uphold the ethical responsibilities of education data professionals
- Introduction to Educational Data Science
- Foundations of Learning Analytics
- Educational Statistics and Data Analysis
- Data Visualization and Communication
- AI in Education and Applications of AI in Education
- Educational Research and Evaluation
- Special Topics in AI & Learning Analytics
- Capstone in AI & Learning Analytics
WHAT CAREERS CAN I PURSUE AFTER COMPLETING THE PROGRAM?
Graduates are prepared for roles in:
- K-12 or higher education (assessment, curriculum, or institutional research)
- EdTech and corporate training (data, analytics or product roles)
- Research, nonprofits, or policy organizations
- Ph.D. and other graduate studies
WHAT DOES THE CAPSTONE PROJECT INVOLVE?
The Capstone in AI & Learning Analytics is a hands-on project where you’ll work with real or simulated data to solve practical challenges. Projects might include building dashboards, conducting evaluations, or generating data-informed recommendations in collaboration with schools, EdTech firms, or community partners.
WHAT ARE THE APPLICATION REQUIREMENTS?
- Bachelor’s Degree
- Transcripts
- Two letters of recommendation
- Optional: personal video, technical portfolio, code samples.
WHEN ARE APPLICATIONS REVIEWED?
- Application opens October 1
- Priority deadline (submit by this date for scholarship consideration): March 1
- Rolling review through: June 30
ARE SCHOLARSHIPS AVAILABLE?
Yes, a limited number of scholarships are available. No separate application is required – apply by March 1 to be considered.
WILL THERE BE SUPPORT FOR CONFERENCES, PUBLICATIONS, OR NETWORKING?
While we don’t offer direct financial support for conferences, faculty and industry advisors actively mentor students on research papers, presentations, and networking opportunities—helping you grow as a professional in the field.
WHAT IS THE EXPECTED COHORT SIZE?
We expect 20-30 students in our inaugural cohort.
WILL I LEARN / UTILIZE SPECIFIC TOOLS?
Yes. You’ll work with widely used applications and platforms for data access, analysis, and visualization. The program emphasizes adaptable, future-ready skills—so you’re not tied to a single tool or platform but prepared to lead as the field evolves.
optional background reading / context building
The following resources are optional and are provided for prospective students who would like to learn more about the field of learning analytics and AI in education. They are not required for application or admission, but may be helpful for those seeking additional context, familiarity with key concepts, or examples of current research and practice in the field. All resources listed below are freely available online and can be explored at your own pace.
- The Handbook of Learning Analytics (2022)
Editors: Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, Agathe Merceron
https://www.solaresearch.org/publications/hla-22/
The Handbook of Learning Analytics is a comprehensive introduction to the field of learning analytics. It provides an accessible overview of key concepts, research areas, and current thinking in the field, making it a helpful starting point for students who want to build foundational knowledge. The 2022 edition reflects the state of learning analytics research at that time and includes contributions from leading scholars in the field. Overall, the handbook is a useful resource for gaining context and understanding how learning analytics is studied and applied. The handbook is open access, free to download, and available online, making it an easy resource to explore before applying. - Learning Analytics Research Network (LEARN): Learning Analytics 101
https://steinhardt.nyu.edu/learn/learning-analytics-101
Learning Analytics 101 is a free, web-based resource designed to introduce the fundamentals of learning analytics in a clear and approachable way. Curated by the Learning Analytics Research Network (LEARN), the site brings together short readings and resources from leading scholars and practitioners in the field. Students who are new to learning analytics are encouraged to begin with the Overview and Development section, then explore other topic areas at their own pace. Most materials are openly available, making this a convenient starting point for building background knowledge before applying to the program. - Browse Learning Analytics & Knowledge (LAK) Conference Proceedings
https://dl.acm.org/conference/lak/proceedings
Learning Analytics & Knowledge (LAK) Conference Proceedings provide access to research presented at the leading international conference in the field of learning analytics. The LAK conference brings together researchers, educators, instructional designers, and data and technology professionals to explore how data and analytics can support learning and education. Browsing the conference proceedings allows prospective students to see examples of current research topics, methods, and applications in learning analytics and AI in education. The proceedings are openly accessible and can be explored at your own pace.
What are the Tuition, Fees, and Program Costs?
The MES-AILA is a self-supporting professional degree program. Tuition is anticipated to be $49,500, pending approval by the University of California Regents in Summer 2026. The tuition rate is the same for all students.
In addition to tuition, enrolled students are responsible for standard graduate student fees, as published by the university Registrar. Additional costs (e.g., textbooks, course materials, or supplemental software) may vary by course.
Applicants must also pay a non-refundable application fee required by the UCI Graduate Division: $135 for U.S. citizens and permanent residents; $155 for all other applicants.
In addition to tuition, enrolled students are responsible for standard graduate student fees, as published by the university Registrar. Additional costs (e.g., textbooks, course materials, or supplemental software) may vary by course.
Applicants must also pay a non-refundable application fee required by the UCI Graduate Division: $135 for U.S. citizens and permanent residents; $155 for all other applicants.
Do you have internship requirements?
The MES-AILA program does not require an internship for degree completion. As a fully online professional program, it is designed to accommodate working professionals and students balancing other commitments.
While the program does not formally place students in internships, students are encouraged to pursue professional opportunities independently if they chose
While the program does not formally place students in internships, students are encouraged to pursue professional opportunities independently if they chose
What kind of applied or capstone experience does the program include?
Students complete an applied capstone project using curated datasets provided by the program. With instructor approval and consultation, students may also propose using their own datasets if appropriate for the course objectives.
All students gain hands-on experience working with real or simulated data to address practical challenges. Capstone projects may include building dashboards, conducting program evaluations, or generating data-informed recommendations.
All students gain hands-on experience working with real or simulated data to address practical challenges. Capstone projects may include building dashboards, conducting program evaluations, or generating data-informed recommendations.
Does the program offer Visas and Housing?
The Master of Education Sciences is offered as a fully online program. Since this is not a residential program, no housing or visa is offered or supported for participation.
Do you admit international students?
Yes, we admit international students. The courses are taught online and students may participate from any location, except for sanctioned countries. Please note though, as an online program, we are not able to offer visa support or OPT authorization.
What career path would this degree prepare me for?
