Learning Analytics and Artificial Intelligence, MSEd
The Learning Analytics and Artificial Intelligence and Online Master’s Degree will empower you to leverage data analytics and artificial intelligence (AI) to drive high-quality decisions within the educational context, and develop adaptive learning systems that leverage analytics and AI to improve student outcomes. 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.
This fully online program prepares graduates to work as data scientists and AI practitioners 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. The program teaches you both 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 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.
For more information: https://www.gse.upenn.edu/academics/programs/learning-analytics-online-masters
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2026 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.
Curriculum
This program requires a total of 10 CUs: six of these will involve core required courses and the remaining four CUs will involve elective courses approved by the program. Coursework in this program focuses on AI components and AI application in educational settings.
The program includes 9 CUs of instruction and 1 CU of a capstone seminar, 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 Artificial Intelligence and Learning Analytics Capstone Seminar course provides the foundation leading to the Capstone project.
| Code | Title | Course Units |
|---|---|---|
| Required Courses | ||
| EDUC 6116 | Foundations of Teaching and Learning | 1 |
| EDUC 6123 | Big Data, Education, and Society | 1 |
| EDUC 6191 | Core Methods in Educational Data Mining | 1 |
| EDUC 6192 | Large Language Model Applications in Education | 1 |
| EDUC 6195 | Capstone Seminar: Artificial Intelligence and Learning Analytics | 1 |
| EDUC 5919 | Deep Learning and Transformer Models | 1 |
| Elective Courses Approved by Program 1 | ||
| Take four of the following: | 4 | |
| Dashboards for Discovery and Learning Applications | ||
| Adaptive Learning Systems | ||
| Artificial Intelligence, Learning Sciences, and Education Policy Worldwide | ||
| Designing for Learning with AI | ||
| Databases and Data Management | ||
| Feature Engineering | ||
| Master's Capstone Project, supported by EDUC 6195 | ||
| Total Course Units | 10 | |
- 1
Other online graduate-level courses across Penn can be taken as an elective if approved by the program.
| First Year | ||
|---|---|---|
| Fall | Course Units | |
| EDUC 6191 | Core Methods in Educational Data Mining | 1 |
| EDUC 6192 | Large Language Model Applications in Education | 1 |
| Two Program Approved Electives | 2 | |
| Course Units | 4.00 | |
| Spring | ||
| EDUC 5919 | Deep Learning and Transformer Models | 1 |
| One Program Approved Elective | 1 | |
| EDUC 6123 | Big Data, Education, and Society | 1 |
| EDUC 6116 | Foundations of Teaching and Learning | 1 |
| Course Units | 4.00 | |
| Summer | ||
| EDUC 6195 | Capstone Seminar: Artificial Intelligence and Learning Analytics | 1 |
| One Program Approved Elective | 1 | |
| Course Units | 2.00 | |
| Total Course Units | 10.00 | |
Learning Analytics and Artificial Intelligence, MSEd and Computer & Information Technology, MCIT (online) Dual Degree
| Code | Title | Course Units |
|---|---|---|
| Dual Degree Requirements | ||
| Computer and Information Technology Requirements | ||
| CIT 5910 | Introduction to Software Development | 1 |
| CIT 5920 | Mathematical Foundations of Computer Science | 1 |
| CIT 5930 | Introduction to Computer Systems | 1 |
| CIT 5940 | Data Structures and Software Design | 1 |
| CIT 5950 | Computer Systems Programming | 1 |
| CIT 5960 | Algorithms and Computation | 1 |
| Two Electives 1 | 2 | |
| Learning Analytics and Artificial Intelligence Requirements | ||
| EDUC 6191 | Core Methods in Educational Data Mining | 1 |
| EDUC 6116 | Foundations of Teaching and Learning | 1 |
| EDUC 6190 | Feature Engineering | 1 |
| EDUC 6195 | Capstone Seminar: Artificial Intelligence and Learning Analytics | 1 |
| EDUC 5918 | Large Language Model Seminar | 1 |
| EDUC 6XXX - Deep Learning and Transformer Models | 1 | |
| Two EDUC Electives 2 | 2 | |
| Other Requirements | ||
| Masters Capstone Project, supported by EDUC 6195 | ||
| Total Course Units | 16 | |
- 1
Electives cannot be taken from EDUC courses. Students may select other online courses within Engineering.
For MCIT Online students pursuing the dual degree, they may choose to substitute up to 2 of the following courses from the MCIT Online curriculum to satisfy up to 2 CUs toward the Learning Analytics and AI degree:
-
CIS 5450: Big Data Analytics
-
CIS 5210: Artificial Intelligence
-
CIS 5300: Natural Language Processing
-
- 2
Electives are taken from other online EDUC courses offered by the Learning Analytics and Artificial Intelligence program.
For Learning Analytics and AI students pursuing the dual degree, they may choose to substitute up to two of the following courses from the Learning Analytics and AI curriculum to satisfy up to 2 CUs toward the MCIT Online degree:
-
EDUC 6123 Big Data, Education, and Society
-
EDUC 6185: Databases and Data Management*
-
EDUC 5183: Adaptive Learning Systems
-
EDUC 5144: Dashboard for Discovery and Learning Applications
*Elective that cannot be taken by dual degree students without them having to take an additional required course.
-
Suggested Plan of Study - MSED Start
| First Year | ||
|---|---|---|
| Fall | Course Units | |
| EDUC 6191 | Core Methods in Educational Data Mining | 1 |
| EDUC Elective 1 | 1 | |
| Course Units | 2.00 | |
| Spring | ||
| EDUC 6190 | Feature Engineering | 1 |
| EDUC Elective 2 | 1 | |
| Course Units | 2.00 | |
| Summer | ||
| EDUC 6195 | Capstone Seminar: Artificial Intelligence and Learning Analytics | 1 |
| Course Units | 1.00 | |
| Second Year | ||
| Fall | ||
| EDUC 5918 | Large Language Model Seminar | 1 |
| Course Units | 1.00 | |
| Spring | ||
| EDUC 6116 | Foundations of Teaching and Learning | 1 |
| EDUC 6XXX - Deep Learning and Transformer Models | 1 | |
| Course Units | 2.00 | |
| Summer | ||
| CIS Elective 1 | 1 | |
| CIS Elective 2 | 1 | |
| Course Units | 2.00 | |
| Third Year | ||
| Fall | ||
| CIT 5910 | Introduction to Software Development | 1 |
| CIT 5920 | Mathematical Foundations of Computer Science | 1 |
| Course Units | 2.00 | |
| Spring | ||
| CIT 5930 | Introduction to Computer Systems | 1 |
| CIT 5940 | Data Structures and Software Design | 1 |
| Course Units | 2.00 | |
| Summer | ||
| CIT 5950 | Computer Systems Programming | 1 |
| CIT 5960 | Algorithms and Computation | 1 |
| Course Units | 2.00 | |
| Total Course Units | 16.00 | |
Suggested Plan of Study - MCIT Start
| First Year | ||
|---|---|---|
| Fall | Course Units | |
| CIT 5910 | Introduction to Software Development | 1 |
| CIT 5920 | Mathematical Foundations of Computer Science | 1 |
| Course Units | 2.00 | |
| Spring | ||
| CIT 5930 | Introduction to Computer Systems | 1 |
| CIT 5940 | Data Structures and Software Design | 1 |
| Course Units | 2.00 | |
| Summer | ||
| CIT 5950 | Computer Systems Programming | 1 |
| CIT 5960 | Algorithms and Computation | 1 |
| Course Units | 2.00 | |
| Second Year | ||
| Fall | ||
| CIT Elective 1 | 1 | |
| CIT Elective 2 | 1 | |
| Course Units | 2.00 | |
| Spring | ||
| EDUC Elective 1 | 1 | |
| EDUC 6116 | Foundations of Teaching and Learning | 1 |
| Course Units | 2.00 | |
| Third Year | ||
| Fall | ||
| EDUC 5918 | Large Language Model Seminar | 1 |
| EDUC 6191 | Core Methods in Educational Data Mining | 1 |
| EDUC Elective 1 | 1 | |
| Course Units | 3.00 | |
| Spring | ||
| EDUC 6190 | Feature Engineering | 1 |
| 1 | ||
| Course Units | 2.00 | |
| Summer | ||
| EDUC 6195 | Capstone Seminar: Artificial Intelligence and Learning Analytics | 1 |
| Course Units | 1.00 | |
| Total Course Units | 16.00 | |