Artificial Intelligence & Learning Analytics
Master of Education Sciences
Shape the future of education through data-driven insights and AI innovations.
New concentrations coming in 2027:
• STEM+AI Teacher Leadership
• Connected Learning, Technology, and Design
• STEM+AI Teacher Leadership
• Connected Learning, Technology, and Design
Program Overview
The UC Irvine School of Education’sMaster of Education Sciences with a concentration in Artificial Intelligence & Learning Analytics (MES-AILA) is a fully online 12-month, part-time graduate program designed for high-achieving graduates and working professionals.
The program prepares students to apply AI and learning analytics to improve learning outcomes, combining education research, data science, and practical application. Courses are taught by leading faculty and designed to be accessible to learners from a wide range of academic and professional backgrounds.
While the program offers the flexibility of fully online learning, students are encouraged, if possible, to attend a week-long in-person kickoff at the start of the program and final two-day capstone event at UC Irvine.
The program prepares students to apply AI and learning analytics to improve learning outcomes, combining education research, data science, and practical application. Courses are taught by leading faculty and designed to be accessible to learners from a wide range of academic and professional backgrounds.
While the program offers the flexibility of fully online learning, students are encouraged, if possible, to attend a week-long in-person kickoff at the start of the program and final two-day capstone event at UC Irvine.
Program Highlights
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Typical course load
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Students learn in a cohort-based environment that emphasizes collaboration, with opportunities to engage closely with faculty and peers throughout the program. The curriculum focuses on both theoretical and applied approaches, emphasizing the use of advanced tools to analyze learning and address real-world challenges in education and related fields.
The program follows a structured sequence, beginning with one course in late summer, followed by two courses each in fall, winter, spring and summer, and culminating in a capstone project that allows students to apply their learning in a practical context. Graduates are prepared for roles across education, EdTech, research, and related sectors. The curriculum is informed by an industry advisory board, helping ensure alignment with emerging workforce needs and career opportunities. |
Curriculum
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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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Educational Research and Evaluation
This course will cover the theory and methods of educational evaluation and research, with a focus on digital learning environments, and how they can be used in conjunction with learning analytics to illuminate students’ learning processes and outcomes. Topics addressed include measurement and sampling; validity, reliability, and assessment; correlational and survey research; experimental and quasi-experimental studies; observation and interviews; design-based research; qualitative and quantitative data analysis; approaches to educational evaluation; and the role of artificial intelligence in facilitating educational data collection and analysis. |
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. |
Students begin with a two-week intensive course at the end of summer, followed by two online courses each in fall, winter, spring, and summer, and concluding with a two-day capstone event. Students are encouraged, if possible, to attend the first week of the intensive course and the final capstone event in person at UC Irvine, but may participate remotely if in-person attendance is not feasible.
Students in these courses will master the following:
Students in these courses will master the following:
- knowledge of the conceptual and theoretical principles from learning and cognitive sciences that underlie the field of learning analytics
- the ability to use major applications to access, clean, manage, and analyze data from digital learning platforms
- the ability to effectively communicate learning analytics results through writing and visualizations
- an understanding of the ethical responsibilities of a learning analytics professional and of how to comply with them
Lead Faculty
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Core Faculty
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Advisory Board
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