Level 1: AI-Exposure Requirement
Complete the First Engineering Requirement by following one of the four pathways:
- ENGR 13100 and ENGR 13200
- ENGR 13000
- ENGR 13300 + VIP/EPICS
- ENGR 16100 and ENGR 16200
Topics covered: Knowledge about how AI works; Critical thinking; Assess and evaluate AI responses; Data-informed decision making; Ethical aspects of AI.
Level 2: AI-Knowledge Requirement
Take all the following courses:
- One of: CS 15900 / CS 17700 / CS 18000 Programming
- MA 26500 Linear Algebra
- AAE 30100 Signal Analysis for Aerospace Engineering
Level 3: AI-Subject Requirements
Take at least two of the following courses:
- AAE 36100 - Introduction to Random Variables In Engineering
- AAE 36400 Control Systems Analysis
- AAE 36401 Control Systems Lab
- AAE 50800 - Optimization in Aerospace Engineering
- AAE 52300 - Introduction to Remote Sensing
- AAE 56100 - Introduction to Convex Optimization
- AAE 56400 - Systems Analysis and Synthesis
- AAE 56700 - Introduction to Applied Stochastic Processes
- AAE 57500 - Introduction to Satellite Navigation and Positioning
- ECE 30200 - Probabilistic Methods in Electrical and Computer Engineering
- IE 33000 - Probability And Statistics in Engineering I
- MA 41600 - Probability
- STAT 22500 - Introduction To Probability Models
- STAT 31100 - Introductory Probability
- STAT 35000 - Introduction To Statistics
- STAT 35500 - Statistics For Data Science
- STAT 41600 - Probability
- STAT 51100 - Statistical Methods
Level 4: AI-Builder Requirement
Senior design projects in AAE will include elements of AI to support system-level decision making and tool integration while reinforcing aerospace engineering competencies and explicitly requiring verification and professional engineering judgement. The following aircraft and spacecraft senior design projects provide representative examples.
Project 1: Next Generation Carrier Based Strike Fighter
Senior Design (AAE451 – Aircraft Design)
This project designs a candidate carrier based strike fighter aircraft to replace the FA 18E/F, achieving improved mission performance while maintaining a comparable unit acquisition cost and meeting naval aviation operational constraints. The project focuses on aircraft configuration, performance, and systems level design. AI is used as a design support and analysis aid during configuration generation, integration of analysis tools, and evaluation of historical data—not as a substitute for aerodynamic, propulsion, or structural reasoning. Students are required to verify AI-assisted results using independent calculations, physics-based reasoning, and bounding and sanity checks. AI-generated outputs cannot be accepted without justification to reinforce professional judgement and avoid over-reliance on automated tools.
Project 2: Autonomous Mission Operations & Guidance for a Resource Constrained Deep Space Spacecraft
Senior Design (AAE450 – Spacecraft Design)
This project designs a deep space spacecraft mission whose feasibility depends on autonomous mission operations and guidance decisions, driven by limited communications, long light time delays, and tight resource margins. Example contexts include SmallSat lunar polar orbiter with intermittent Earth contact, and asteroid flyby with weeks between ground contacts. AI is used as a decision making aid inside the spacecraft’s operations and guidance logic. Students will design mission-level decision modules that answer questions such as whether the data should be stored or downlink; should the priority be given to attitude stability, power generation, or thermal safety. AI is used to evaluate competing operational choices under constraints. AI will also be used to assist high-level guidance decisions to decide when to maneuver and not how to actuate. Students are required to verify AI-assisted results using independent calculations, physics-based reasoning, and bounding and sanity checks. AI-generated outputs cannot be accepted without justification to reinforce professional judgement and avoid over-reliance on automated tools.