AI & Learning Analytics
Use data and AI to better understand and improve how people learn.
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The AI & Learning Analytics concentration prepares students to apply data science, artificial intelligence, and learning science to real-world educational challenges.
Through hands-on coursework, you’ll learn to turn data into meaningful insights that can inform teaching, learning, programs, and decision-making. No prior experience in AI or coding is required. |
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Program Highlights
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12 MONTHS Complete the program in one year |
FULLY ONLINE Asynchronous coursework |
2 COURSES Typically per quarter |
COHORT-BASED Learn alongside your peers |
CAPSTONE Apply your learning to a real-world project |
Program structure:
- One course in Summer 1
- Two courses per quarter Fall-Spring
- Two courses in Summer 2
Who This is For
Designed for professionals who want to use data and AI to better understand and improve learning.
You may be a good fit if you:
You may be a good fit if you:
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Want to use data to improve learning across classrooms, programs or systems. |
Want to grow into roles involving data-informed decision-making. |
Are new to coding or AI and ready to build your skills from the fundamentals. |
Want to build practical skills in analytics, AI, and data interpretation. |
What You’ll Learn + What You’ll Do
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UNDERSTAND Learn how learning and cognitive sciences shape the way we interpret educational data. Vertical Divider
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DESIGN Clean, manage, analyze and visualize data from digital learning environments. Vertical Divider
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CREATE Use AI and analytics to investigate real educational questions, communicate insights and complete applied projects. |
Throughout the program, you’ll work with real or simulated datasets, complete case studies and project-based assignments, collaborate with peers and faculty, and complete a capstone focused on a real-world problem or area of interest.
Career Pathways
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Build skills for roles at the intersection of education, data, AI, and technology.
Potential career paths include:
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Career opportunities vary based on professional experience and background.
Curriculum
Build from foundational concepts in educational data science and AI to advanced applications in learning analytics.
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Introduction to educational data science
This course will teach you how to use R (for beginners) or advanced techniques (for experienced users) to wrangle, analyze, and visualize education data. In addition, you will learn how to think with and about data in ethical ways. You will explore questions with data and communicate results using R-Markdown, which lets you build interactive lab notebooks and rich data visualizations. In addition to working with traditional data sources (e.g., gradebook data, institutional data), students will also learn how to clean, explore, analyze, and visualize clickstream data obtained from learning management systems. By the end of this course, you will be able to transform data into insights that instructors can use to better understand their students and improve learning outcomes. |
Introduction to AI in Education
This course introduces how artificial intelligence—especially generative AI—is being used to support teaching, learning, and educational research. You’ll learn key concepts and practical strategies for using AI thoughtfully, effectively, and responsibly in real educational settings. Throughout the term, you’ll build a digital portfolio featuring concrete examples, an annotated bibliography of research and resources, and a final project aligned with your professional interests. By the end, you’ll be able to evaluate AI tools critically and design meaningful, accessible learning experiences that make smart use of them. |
Foundations of Learning Analytics
This course will provide students with a general survey of learning analytics, emphasizing its application across various educational contexts, rather than its underlying algorithmic details. In particular, we will discuss the foundations of learning analytics in the context of relevant sociotechnical shifts and theoretical frameworks; discuss emerging forms of assessment and common data analytic approaches, including those using artificial intelligence; review learning analytical tools and illustrative studies, and design analytics for each student’s research area. Overall, this course provides a comprehensive, theory-driven overview of learning analytics to orient students to this nascent field and prepare them for advanced research/practice in learning analytics. |
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Educational Statistics and Data Analysis
This course will introduce students to key statistical concepts and techniques for exploring and analyzing educational data using R. These concepts include understanding estimation and uncertainty in data, sampling, sampling error, hypothesis testing, descriptive statistics, statistical tests of association (e.g., correlation, independent tests), and linear models (e.g., regression). Students will design a research hypothesis for testing, implement appropriate statistical procedures, and write a formal research report. Unique to this course is that we will explore these concepts using a real data set from an online undergraduate course. This data set contains demographic, gradebook, survey, and clickstream data. |
Data Visualization and Communication
Students in this course will learn about the theories and techniques of data visualization, with a focus on applications in education and learning analytics. The course will provide an introduction to topics such as perception and cognition, data representations, and visualization techniques for specific data types. Students will also gain experience in utilizing human-centered design approaches and implementing different workflows: from creating visualizations with standard packages (e.g., R/Shiny) to designing new visualizations with prototyping tools (Figma). Finally, the course will focus on the unique needs and design challenges for visually and verbally communicating data to improve teaching, learning, and equity in education settings. |
Applications of AI in Education
This course will allow students to explore various applications of AI in education, by learning about the theories and methods underpinning these applications and building their own AI tools. The course will specifically look at applications that contribute to three foundational aspects of education: assessment, learning, and instruction. Students will (a) be exposed to some of the key theories and controversies around these topics in education; (b) learn about state-of-the-art algorithmic approaches to assessment, learning, and instruction; and (c) gain hands-on experience with these algorithms through programming and data-mining exercises. Specific topics include item response theory, computerized-adaptive testing, learning curve analysis, knowledge tracing, intelligent tutoring systems, constructionist microworlds, and generative AI. |
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Special Topics in AI & Learning Analytics
We use measurement and assessment as a way to answer questions through use of psychological and educational tests and surveys. These tests cover many different domains (e.g., intelligence, cognition, personality) and take many different forms. These however all rely on psychometrics, which is a field of study dedicated to the theory and technique underlying the measurement of psychological phenomena (e.g., knowledge, abilities, attitudes, and personality traits). In this course, we focus on appraisal and development of measures. We will consider measurement theory and its application in education using a classical test theory approach. Students will have the opportunity to apply aspects of psychometric theory including scaling, test construction, reliability and validity assessment, and item analysis to their own projects. Cross-cultural, cross-linguistic, and bilingual issues in test performance, item selection, and development will be considered. |
Capstone in AI & Learning Analytics
Students in this final course will work closely with the instructor to devise an independent learning analytics project, implement this project, and present final analysis and results in the culminating program expo. Students will travel to UCI for a final two days as a cohort, where they will present their final capstone projects, interact with incoming cohorts to the program, and meet industry professionals in networking events. |
FAQs
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 Fall 2026
- 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. 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.
Veterans' Educational Benefits
If you are or will be utilizing veterans’ educational benefits to fund your degree, please contact the School Certifying Official (SCO) at UCI for information regarding whether the program fee is covered or includes components that are not covered. The SCO can provide guidance on how program fees are classified for the purposes of benefits administered by the U.S. Department of Veterans Affairs. The best way to contact the Veteran Benefits Office is by email at [email protected].
Prospective and current students are encouraged to consult with the SCO early in the application or enrollment process to better understand their eligibility and coverage.
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.
Veterans' Educational Benefits
If you are or will be utilizing veterans’ educational benefits to fund your degree, please contact the School Certifying Official (SCO) at UCI for information regarding whether the program fee is covered or includes components that are not covered. The SCO can provide guidance on how program fees are classified for the purposes of benefits administered by the U.S. Department of Veterans Affairs. The best way to contact the Veteran Benefits Office is by email at [email protected].
Prospective and current students are encouraged to consult with the SCO early in the application or enrollment process to better understand their eligibility and coverage.
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.
