Harness the power of generative AI to enhance the efficiency and quality of data analysis in educational settings.
The Learning Analytics and Artificial Intelligence (AI), M.S.Ed. will empower you to leverage data analytics and AI to drive high-quality decisions within the educational context. The program prepares data scientists to develop advanced skills in measurement, analysis, and predictive modeling, leveraging state-of-the-art methodologies such as machine learning, generative AI, and deep learning, while avoiding algorithmic bias. Students will also develop skills in real-time data analysis and visualization, personalized learning recommendation generation, and data management, equipping them to develop and enhance data-driven educational environments.
What Sets Us Apart
Ivy League Education Master's Program in Artificial Intelligence
About the Program
This fully online program prepares graduates to work as data scientists in research and development in areas such as at-risk prediction, and AI-based adaptive learning systems such as intelligent tutoring systems and educational recommender systems. You will emerge understanding when and why to use different methods for a range of applications in order to make a real-world impact.
Fall: 3 courses; Spring: 3 courses; Summer: 2 courses; Fall: 2 courses and capstone.
Culminating experienceMaster’s capstone project
- Full-Time
- Part-Time
- Online
- Online
Learning Analytics and AI uses educational data and modern algorithms to develop learning technologies that enhance student outcomes, support teachers and school leaders, and help identify and address gaps. The program teaches you the latest AI techniques, learning analytics algorithms and tools, as well as how to engineer data streams to turn raw data into features that are interpretable to humans and large language models (LLMs). Your use of contemporary methods will be grounded in the rich history of educational thought, with an understanding of how this grounding can support efforts to address challenges such as algorithmic bias to improve educational outcomes at scale.
This program can be completed within 16 months by following the recommended schedule (Fall, Spring, Summer, Fall), or students can enroll part-time and complete the program within two years. The program consists of coursework and a capstone project where students will develop projects with real-world relevance and of a quality that can be submitted as a demo or short papers to international conferences. The program is fully online (no in-person component) and will have a mixture of synchronous and asynchronous activities, with multiple sections/time slots for synchronous activities to accommodate students around the world.
Curriculum
This program requires a total of 10 CUs: eight of these are core required courses and the remaining two CUs will involve concentration courses. It culminates in a capstone, where students demonstrate their readiness to work in industry and/or produce competitive submissions to demo and short paper categories at top international conferences on learning analytics and AI in Education.
Required Courses
- Dashboards for Discovery and Learning Applications
Dashboards are a type of report for teachers, school leaders, school counselors, and other stakeholders. This class will cover design principles and current use cases. - Master's Foundations of Teaching and Learning
The course covers key thinkers and ideas in education and the application of their ideas around the world. - Big Data, Education, and Society
Prepares students for opportunities, challenges, and constraints in real-world usage of learning analytics, including challenges around algorithmic bias. Covers key applications in practice and factors accelerating and impeding their usage. - Feature Engineering
Feature engineering is the practice of transforming complex data streams into interpretable variables that can be used within algorithms to generate models that can be validated and used. - Core Methods in Educational Data Mining
Teaches essential methods and algorithms used in practice. - Adaptive Learning Systems
The course teaches about the pedagogy and technology of adaptive learning systems, as well as individualized and personalized technology that helps students construct understanding and develop skills. - Databases and Data Management
This course teaches the use of databases within analytics -- i.e. how to set up a database, extract data from it, and analyze data. - Learning Analytics Masters Capstone Seminar
Elective Courses
Take 2 additional course units (CUs) of EDUC courses at the 5000 level or above. They should focus on Large language models, Deep Learning, and Transformer Models. See advisor for details.
Our Faculty
Affiliated Faculty
Our affiliated faculty members are valued as part of our intellectual community, and students are encouraged to take their courses and to connect on research matters and for mentorship.
Haiying Li
Program Manager
Ph.D., University of Memphis
Jaclyn Ocumpaugh
Associate Director, Penn Center for Learning Analytics
Ph.D., Michigan State University
Maciej Pankiewicz
Senior Research Investigator, Penn Center for Learning Analytics
Ph.D., University of Bonn
Accreditation Information
The University of Pennsylvania is accredited by the Middle States Commission on Higher Education.
The University of Pennsylvania is accredited by the Middle States Commission on Higher Education (MSCHE), 3624 Market Street, Philadelphia, PA 19104. (267) 284-5000. www.msche.org.
The MSCHE is an institutional accrediting agency recognized by the US Secretary of Education and the Council for Higher Education Accreditation (CHEA). Information about Penn’s accreditation with MSCHE is available here.
To make a complaint to MSCHE, visit this website.
For more information about Penn’s accreditation status, visit the Penn Provost website.
Required Disclosure Information
Our Graduates
The Learning Analytics program prepares students for careers in industry, government, non-profits, higher education, and K-12 school systems, as well as to go on to pursue doctoral programs. The program prepares graduates to work in research and development in areas such as at-risk prediction, intelligent tutoring systems, and educational recommender systems.
Admissions & Financial Aid
Please visit our Admissions and Financial Aid pages for specific information on the application requirements, as well as information on tuition, fees, financial aid, scholarships, and fellowships.
Contact us if you have any questions about the program.
Financial Aid
Graduate School of Education
University of Pennsylvania
3700 Walnut Street
Philadelphia, PA 19104
(215) 898-6415
admissions@gse.upenn.edu
finaid@gse.upenn.edu
Please view information from our Admissions and Financial Aid Office for specific information on the cost of this program.
Most students in this program are anticipated to fund their degree through a combination of personal resources, employer benefits, and student loans.
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