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
Required for all BSChE degrees:
- CHE 300 Chemical Engineering Seminar
- CHE 320 Statistical Modeling And Quality Enhancement
Required for BSChE degrees with a focus on data science concentration:
- CS 159 or CS 177 or CS 180: Programming
Level 3: AI-Subject Requirements
Required: At least one of the following courses:
- CHE 30600 Design Of Staged Separation Processes
- CHE 34800 Chemical Reaction Engineering
- CHE 37700 Momentum Transfer
- CHE 42000 Process Safety Management And Analysis
- CHE 43500 Chemical Engineering Laboratory
- CHE 45600 Process Dynamics And Control
- CHE 54000 Transport Phenomena
- CHE 59700 Data Science in ChE
- ECE 50024 Machine Learning (Formerly ECE 59500)
- IE 49000 Special Topics in Industrial Engineering
- ILS 29500 Statistical Learning
- STAT 41600 Probability
Level 4: AI-Builder Requirement
All ChE students are already required to complete a capstone project for their bachelor’s degree. The School of ChE will introduce new AI elements in the following design courses:
Capstone Project (Senior Design): Teams will design a chemical process (flowsheet + economics + safety + environment constraints) and deliver a final report/presentation. AI will be added to assist students in the brainstorming process, alternatives, hazard checklist, regulatory considerations, conduct sensitivity analysis, define uncertainty range, perform regression fit for property and kinetic parameters; draft code utilities for scenario analysis, ensuring unit tests and sanity checks; improve report structure and clarity, and include disclosure, a technical accuracy check list, and an “AI trace” appendix.
CHE Lab sequence (34800/37700/37800/43500): Students will collect experimental data and build predictive models (including machine learning models) for an observable (e.g., yield, conversion, pressure drop). Students will then be asked to report how AI has been used in, for example, brainstorming and gathering information, suggesting plausible propagation uncertainties, analyzing residual, providing programming/coding assistance, and writing assistance.
CHE Undergrad Research (CHE 41100/49800/49900): Faculty-mentored research projects will include new AI elements for (i) Literature analysis and identifying gaps, enforcing citation discipline and verifying sources; (ii) Surrogate modeling, uncertainty quantification, and experimentation; (iii) Speed up prototyping, and understand reproducible research, and repository; (iv) Improve report writing.