Artificial Intelligence & Learning Analytics
Master of Education Sciences
Shape the future of education through data-driven insights and AI innovations.
Apply by March 1, 2026 for priority scholarship consideration. Start your application today.
Program Overview
The UC Irvine School of Education is excited to launch a cutting-edge graduate program, the Master of Education Sciences with a concentration in Artificial Intelligence & Learning Analytics. This innovative, fully online 12-month part-time program is designed for high-achieving graduates and working professionals seeking advanced expertise in learning analytics and AI. While the program offers the flexibility of online learning, students are strongly encouraged to attend an in-person orientation and final capstone event at UCI. The curriculum, delivered by esteemed faculty, balances theoretical insights with practical applications, making it accessible to individuals from diverse academic and professional backgrounds.
Program Highlights
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Typical course load
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UC Irvine's School of Education offers a degree of Master of Education Sciences, with a concentration in Artificial Intelligence & Learning Analytics. This terminal degree program will be administered by the School of Education. The program will offer high-performing graduates and working professionals the opportunity for efficient and effective graduate-level training in the emerging discipline of AI & learning analytics. This will be a 12-month part-time program that will begin with one course at the end of summer, followed by two courses each in fall, winter, spring, and summer, and culminating in a capstone and graduation event.
The Master of Education Sciences will be offered as a fully online program, but students who are able to will be strongly encouraged to attend the first week of courses and final culminating event at UC Irvine. The program will be open to college graduates from a range of majors as well as working professionals. The program will be staffed by faculty in the School of Education and will focus on both theoretical and applied knowledge in using advanced tools to analyze learning. An industry and professional advisory board will assist in developing and promoting career opportunities for graduates who will find employment in schools and universities, the business sector, and nongovernmental organizations. |
Curriculum
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Introduction to educational data science
This course will teach you how to use R 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 have one course the first summer which are conducted via a one-week program at Irvine, Calif. and three follow-up weeks online. Students then have two online courses each in fall, winter, spring, and summer, followed by a two-day capstone event in Irvine. Students are highly encouraged to join the one-week introduction and the two-day capstone in person, but may participate remotely if in-person attendance is not possible.
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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