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.

Required Courses
EDUC 6116Foundations of Teaching and Learning1
EDUC 6123Big Data, Education, and Society1
EDUC 6191Core Methods in Educational Data Mining1
EDUC 6192Large Language Model Applications in Education1
EDUC 6195Capstone Seminar: Artificial Intelligence and Learning Analytics1
EDUC 5919Deep Learning and Transformer Models1
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 Units10
1

Other online graduate-level courses across Penn can be taken as an elective if approved by the program.

Plan of Study Grid
First Year
FallCourse 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 Units4.00
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 Units4.00
Summer
EDUC 6195 Capstone Seminar: Artificial Intelligence and Learning Analytics 1
One Program Approved Elective 1
 Course Units2.00
 Total Course Units10.00

Learning Analytics and Artificial Intelligence, MSEd and Computer & Information Technology, MCIT (online) Dual Degree

Dual Degree Requirements
Computer and Information Technology Requirements
CIT 5910Introduction to Software Development1
CIT 5920Mathematical Foundations of Computer Science1
CIT 5930Introduction to Computer Systems1
CIT 5940Data Structures and Software Design1
CIT 5950Computer Systems Programming1
CIT 5960Algorithms and Computation1
Two Electives 12
Learning Analytics and Artificial Intelligence Requirements
EDUC 6191Core Methods in Educational Data Mining1
EDUC 6116Foundations of Teaching and Learning1
EDUC 6190Feature Engineering1
EDUC 6195Capstone Seminar: Artificial Intelligence and Learning Analytics1
EDUC 5918Large Language Model Seminar1
EDUC 6XXX - Deep Learning and Transformer Models1
Two EDUC Electives 22
Other Requirements
Masters Capstone Project, supported by EDUC 6195
Total Course Units16
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

Plan of Study Grid
First Year
FallCourse Units
EDUC 6191 Core Methods in Educational Data Mining 1
EDUC Elective 1 1
 Course Units2.00
Spring
EDUC 6190 Feature Engineering 1
EDUC Elective 2 1
 Course Units2.00
Summer
EDUC 6195 Capstone Seminar: Artificial Intelligence and Learning Analytics 1
 Course Units1.00
Second Year
Fall
EDUC 5918 Large Language Model Seminar 1
 Course Units1.00
Spring
EDUC 6116 Foundations of Teaching and Learning 1
EDUC 6XXX - Deep Learning and Transformer Models 1
 Course Units2.00
Summer
CIS Elective 1 1
CIS Elective 2 1
 Course Units2.00
Third Year
Fall
CIT 5910 Introduction to Software Development 1
CIT 5920 Mathematical Foundations of Computer Science 1
 Course Units2.00
Spring
CIT 5930 Introduction to Computer Systems 1
CIT 5940 Data Structures and Software Design 1
 Course Units2.00
Summer
CIT 5950 Computer Systems Programming 1
CIT 5960 Algorithms and Computation 1
 Course Units2.00
 Total Course Units16.00

Suggested Plan of Study - MCIT Start

Plan of Study Grid
First Year
FallCourse Units
CIT 5910 Introduction to Software Development 1
CIT 5920 Mathematical Foundations of Computer Science 1
 Course Units2.00
Spring
CIT 5930 Introduction to Computer Systems 1
CIT 5940 Data Structures and Software Design 1
 Course Units2.00
Summer
CIT 5950 Computer Systems Programming 1
CIT 5960 Algorithms and Computation 1
 Course Units2.00
Second Year
Fall
CIT Elective 1 1
CIT Elective 2 1
 Course Units2.00
Spring
EDUC Elective 1 1
EDUC 6116 Foundations of Teaching and Learning 1
 Course Units2.00
Third Year
Fall
EDUC 5918 Large Language Model Seminar 1
EDUC 6191 Core Methods in Educational Data Mining 1
EDUC Elective 1 1
 Course Units3.00
Spring
EDUC 6190 Feature Engineering 1
1
 Course Units2.00
Summer
EDUC 6195 Capstone Seminar: Artificial Intelligence and Learning Analytics 1
 Course Units1.00
 Total Course Units16.00