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645.671.8VL - AI for Systems Engineering

Systems Engineering

Summer 2026

Description

This course explores the application of Artificial Intelligence (AI) across the systems engineering (SE) lifecycle. Students examine how AI can augment traditional systems engineering activities including requirements analysis, functional decomposition, architecture development, test and evaluation, decision analysis, risk management, and technical project management. Emphasis is placed on responsible, traceable, and auditable use of AI as an engineering tool—not a decision authority. Through lectures, discussions, hands-on labs, and a multi-week team project, students gain practical experience integrating AI into systems engineering workflows while critically evaluating its limitations, risks, and ethical implications. The course concludes with an exploration of Model-Based Systems Engineering (MBSE) combined with AI and a discussion of future trends in the discipline. Students are expected to register for ChatGPT Plus for the duration of the semester as the primary tool used in the course.

Instructor

CJ Utara

Christian.Utara@jhu.edu

Course Structure

The course materials are divided into modules which can be accessed by clicking Modules on the course menu. A module will have several sections including the overview, objectives, content, and assignments/labs.
You are encouraged to preview all sections of the module before starting.
Please review the Course Outline to align course modules with semester weeks.
You should regularly check the Calendar and Announcements for course updates.

Course Topics

Module 1 - Course Overview & Foundations of AI

Module 2 - Opportunities, Challenges, and Prompt Engineering

Module 3 - Managing and Analyzing Information with AI

Module 4 - AI for Requirements Analysis

Module 5 - AI for Functional Analysis

Module 6 - AI for Physical Architecture & Implementation

Module 7 - AI for Test & Evaluation

Module 8 - AI for Decision Analysis

Module 9 - AI for Risk Analysis

Module 10 - AI for Technical Project Management

Module 11 - Retrieval-Augmented Generation (RAG) and Custom Bots

Module 12 - Ethics and Responsible Use of AI

Module 13 - MBSE+AI & The Future of AI in Systems Engineering

Module 14 - Team Presentations and Course Closeout

Course Goals

To learn how to ethically and professionally use Artificial Intelligence (AI) to perform the roles of a Systems Engineer, specifically data analysis, requirements analysis, functional analysis, physical allocation, test and evaluation, decision analysis, risk analysis and technical project management.

Course Learning Outcomes (CLOs)

1. Explain fundamental AI concepts and their relevance to systems engineering.
2.Critically evaluate AI-generated outputs for accuracy, bias, and engineering validity.
3.Apply AI tools to support requirements analysis, functional analysis, and physical architecture development.
4.Establish and maintain traceability across requirements, functions, components, and test artifacts using AI-assisted methods.
5.Use AI to support decision analysis, risk management, and technical project management activities.
6.Design and implement Retrieval-Augmented Generation (RAG) solutions for engineering knowledge management.
7.Assess ethical, professional, and governance considerations associated with AI use in engineering.
8.Integrate AI concepts with Model-Based Systems Engineering approaches.
9.Communicate engineering results clearly and professionally, including limitations of AI-supported analyses.

Textbooks

There are no required textbooks for this course.

ChatGPT Plus is required for this course. Signing up for ChatGPT Plus can be performed at this link: ChatGPT

Other Materials & Online Resources

All materials for this course will be provided within the Canvas course shell.

Required Software

ChatGPT is required for this course (https://chatgpt.com).

Student Coursework Requirements

#	Assignment (linked module)	Individual / Team	Timing (Week)	Contribution to Final Grade
A-1	Engage with AI – choose a subject/topic that you would like to research and use ChatGPT to learn more about it. Summarize what you have learned.	Individual	1	10 %
A-2	Apply different AI models – use different AI models to research the same topic. Compare and contrast the different responses.	Team	2	10 %
A-3 (Lab/HW)	Analyze Data Using AI - Perform AI-assisted exploratory data analysis on a provided dataset.	Team/Individual	3	10 %
A-4 (Lab/HW)	Apply AI to Requirements Analysis - Critique an existing requirements document. · Select one of four provided system topic areas. · Critique an existing requirements document. · Use AI to: o Identify ambiguities, inconsistencies, and gaps o Improve requirement quality · Establish initial requirements traceability. · Document AI usage and rationale for changes	Team/Individual	4	10 %
A-5 (Lab/HW)	Apply AI to Functional Analysis · Develop or refine functional decomposition · Use AI to improve functional allocation · Create Requirements ↔ Functions traceability. · Identify gaps or redundancies.	Team/Individual	5	10 %
A-6 (Lab/HW)	Apply AI to Physical Synthesis · Develop a physical architecture for the same system. · Use AI to assess alignment of components to functions and requirements. · Create Functions ↔ Components traceability. · Evaluate architectural alternatives.	Team/Individual	6	10 %
A-7 (Lab/HW)	Apply AI to Generate Test Cases · Generate a structured set of test cases using AI. · Trace test cases to requirements and components. · Identify coverage gaps and AI-related risks.	Team/Individual	7	10 %
Class Participation	Individual	all weeks		10 %
Course Project	Team	Team	8-14	20 %
	Total			100 %

Grading Policy

EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).

Score Range	Letter Grade
100-97	= A+
<97-93	= A
<93-90	= A−
<90-87	= B+
<87-83	= B
<83-80	= B−
<80-77	= C+
<77-73	= C
<73-70	= C−
<70-67	= D+
<67-63	= D
<63	= F

Course Evaluation

Students will provide feedback on this course at the mid-point and end of semester.

Course Policies

Course LabsIf a student is unable to participate during an in-class lab, they are required to submit the lab as an individual homework assignment.
AI Conversation CaptureWhen AI is used, include the following as an appendix to the assignment submission:

1. A Reference using APA interview format.
Example: ChatGPT-o5. (Year, Month Day). Purpose of Interaction. (Student_Name, Interviewer)

2. The prompt(s) used and the responses provided (include the conversation). Follow specific guidelines for each assignment
AI Tool UseFor this course, you can use either the following:

ChatGPT (https://chatgpt.com/)
- Plus version required for this course ($20/month)
- History of chats will be saved for the length of the course
HopGPT (https://chat.ai.jh.edu/login) - not recommended due to limited chat retention
- Free to students
- History of chats will be deleted after 30 days

Student Generative AI (GenAI) Use

Green: Liberal GenAI use is permitted, with expectations for transparency and accountability

GenAI may be used throughout the workflow.
Disclosure of GenAI use may be required, depending on course or assignment guidelines.
Students remain fully responsible for verifying accuracy, identifying bias, and ensuring the quality of final submissions.

Academic Policies

Deadlines for Adding, Dropping, and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar. Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students. This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Our courses are designed with a proactive approach to accessibility to minimize the need for disability disclosure and accommodation requests, but we recognize that you may need additional support. Students with disabilities (including those with psychological conditions, medical conditions, and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements. For further information or to start the process of requesting accommodations, please contact EP Student Disability Services at ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. For a full description of the code please visit the Student Conduct Code website.

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team (EP-Registration@exchange.johnshopkins.edu) in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.