Fundamentals of
AI-Augmented Engineering
A Conversational Approach
Engineering Computation, Modeling, AI Collaboration, and Validation
Engineering computation for a new era.
Fundamentals of AI-Augmented Engineering is a developing, institution-independent learning resource exploring how engineers combine computation, programming, mathematical modeling, artificial intelligence, physical understanding, validation, and human engineering judgment.
The publication emphasizes transferable engineering thinking rather than dependence on a single programming language or computational platform. AI is treated as a powerful engineering collaborator, while the human engineer retains responsibility for the problem, model, assumptions, validation, interpretation, and final decision.
Current Publication Content
Chapters are released individually as they are completed and prepared for publication.
Select a chapter to expand or collapse its lecture parts and supporting materials.
Stopping Distance Animator
Explore reaction distance, braking distance, total stopping distance, road conditions, safe stopping margin, and collision behavior through an interactive engineering model.
01Chapter 1 — Engineering in the Age of AI
Introduces AI-Augmented Engineering, the evolution of engineering computation, emerging forms of Conversational Engineering, human–AI collaboration, and the continuing importance of engineering judgment, validation, and professional responsibility.
AVAILABLE
Chapter 1 — Engineering in the Age of AI
Introduces AI-Augmented Engineering, the evolution of engineering computation, emerging forms of Conversational Engineering, human–AI collaboration, and the continuing importance of engineering judgment, validation, and professional responsibility.
Laboratory instructions, report templates, and the Four-Ball Drop extension challenge.
Original iPhone recordings for motion analysis. Each ball is provided as a standard 30 fps video and a 120 fps slow-motion recording.
Chapter 1 engineering homework and editable student document.
Chapter 1 self-assessment and editable response document.
02Chapter 2 — From Engineering Procedure to Algorithm
Develops algorithmic engineering thinking through decisions, repetition, engineering search, functions and decomposition, AI-assisted coding, and systematic testing, debugging, and validation.
AVAILABLE
Chapter 2 — From Engineering Procedure to Algorithm
Develops algorithmic engineering thinking through decisions, repetition, engineering search, functions and decomposition, AI-assisted coding, and systematic testing, debugging, and validation.
Laboratory instructions, report templates, and the Reaction-Time Sensitivity extension challenge.
Chapter 2 engineering homework and editable student document.
Chapter 2 self-assessment and editable response document.
03Chapter 3 — Error, Convergence, and Engineering Validation
Develops engineering judgment for computational results through disagreement, measurement of error, step size and resolution, convergence and tolerance, stopping criteria, validation, and engineering decision-making.
AVAILABLE
Chapter 3 — Error, Convergence, and Engineering Validation
Develops engineering judgment for computational results through disagreement, measurement of error, step size and resolution, convergence and tolerance, stopping criteria, validation, and engineering decision-making.
Laboratory instructions, report templates, and the When Does the Model Stop? extension challenge.
Chapter 3 engineering homework and editable student document.
Chapter 3 self-assessment and editable response document.
04Chapter 4 — Numerical Root Finding
Develops numerical root-finding strategies through bracketing, bisection, stopping criteria, convergence, validation, and engineering interpretation of computational solutions.
AVAILABLE
Chapter 4 — Numerical Root Finding
Develops numerical root-finding strategies through bracketing, bisection, stopping criteria, convergence, validation, and engineering interpretation of computational solutions.
Laboratory instructions, report templates, and the One Cup, Five Targets extension challenge.
Chapter 4 engineering homework and editable student document.
Chapter 4 self-assessment and editable response document.
05Chapter 5 — Newton's Method
Extends numerical root finding through Newton's method, using slope information for faster iteration, examining when Newton's method works well or fails, and comparing Newton and bisection based on the information available.
AVAILABLE
Chapter 5 — Newton's Method
Extends numerical root finding through Newton's method, using slope information for faster iteration, examining when Newton's method works well or fails, and comparing Newton and bisection based on the information available.
Laboratory instructions, report templates, and the Same Beam, Six Starts extension challenge.
Chapter 5 engineering homework and editable student document.
Chapter 5 self-assessment and editable response document.
06Chapter 6 — Interpolation
Develops interpolation as an engineering computational tool for estimating values between measured data points, building the method from data, checking results against physical behavior, and automating the process with appropriate safeguards.
AVAILABLE
Chapter 6 — Interpolation
Develops interpolation as an engineering computational tool for estimating values between measured data points, building the method from data, checking results against physical behavior, and automating the process with appropriate safeguards.
Laboratory instructions, report template, and extension challenge materials.
Chapter 6 engineering homework.
Chapter 6 self-assessment and editable response document.
07Chapter 7 — Curve Fitting and Model Validation
Develops curve fitting as an engineering modeling process, moving from measured data to best-fit relationships, interpreting the physical meaning of fitted curves, and testing whether a fitted model can be trusted.
AVAILABLE
Chapter 7 — Curve Fitting and Model Validation
Develops curve fitting as an engineering modeling process, moving from measured data to best-fit relationships, interpreting the physical meaning of fitted curves, and testing whether a fitted model can be trusted.
08Chapter 8 — Linear Systems and Matrix-Based Engineering Computation
Develops linear systems from coupled engineering equations, represents them in matrix form, solves them by hand and computationally, scales the model as the system grows, and validates whether a correct numerical solution represents the correct physical model.
AVAILABLE
Chapter 8 — Linear Systems and Matrix-Based Engineering Computation
Develops linear systems from coupled engineering equations, represents them in matrix form, solves them by hand and computationally, scales the model as the system grows, and validates whether a correct numerical solution represents the correct physical model.
Laboratory instructions, report template, and extension challenge materials.
Chapter 8 engineering homework and editable student document.
Chapter 8 self-assessment and editable response document.
09Chapter 9 — Iterative Methods for Linear Systems
Introduces iterative approaches to solving engineering linear systems, including Jacobi and Gauss-Seidel methods, convergence and failure, computational scaling, and validation of numerical solutions and AI-assisted results.
AVAILABLE
Chapter 9 — Iterative Methods for Linear Systems
Introduces iterative approaches to solving engineering linear systems, including Jacobi and Gauss-Seidel methods, convergence and failure, computational scaling, and validation of numerical solutions and AI-assisted results.
1–6Chapters 1–6 — Comprehensive Assessment
A cumulative learning checkpoint integrating the engineering computation, modeling, numerical methods, interpolation, validation, and AI-augmented problem-solving approaches developed in Chapters 1–6.
AVAILABLE
Chapters 1–6 — Comprehensive Assessment
A cumulative learning checkpoint integrating the engineering computation, modeling, numerical methods, interpolation, validation, and AI-augmented problem-solving approaches developed in Chapters 1–6.
PROJECTS: Put Engineering Models to the Test
Apply engineering modeling, numerical computation, experimental measurement, and validation in extended, real-world investigations. Projects are organized independently of the chapter materials.
P1Project 1 — Can We Predict the Shot?
Analyze basketball motion using early-flight measurements, construct and test a predictive model, and compare the committed prediction against subsequently released full-flight evidence.
AVAILABLE
Project 1 — Can We Predict the Shot?
Analyze basketball motion using early-flight measurements, construct and test a predictive model, and compare the committed prediction against subsequently released full-flight evidence.
Start with the Student Manual. Consult the team assignment spreadsheet to identify your three assigned Shot IDs.
Project 1 — PARTIAL Video Library (30 videos)
Expand to select your assigned Shot IDs.
Project 1 — PARTIAL Video Library (30 videos)
Expand to select your assigned Shot IDs.
Only use the three Shot IDs assigned to your team. These recordings show the early portion of each shot for measurement and prediction.
Project 1 — FULL Video Library (30 videos)
Expand to select your assigned Shot IDs.
Project 1 — FULL Video Library (30 videos)
Expand to select your assigned Shot IDs.
Full-flight recordings for Project 1 validation and comparison with your committed predictions.
A living engineering publication.
Chapters may be updated independently as new examples, applications, validation methods, and AI-Augmented Engineering practices are developed. Each published chapter will identify its version and update date so readers and educators can cite the material accurately.
When a complete-book edition becomes available, it may also be offered as a single downloadable PDF in addition to the individual chapter PDFs.
Recommended Publication Citation
How This Was Made and Validated
Significant AI First Engineering learning materials will identify their human authorship, AI assistance where appropriate, version, publication date, validation approach, and known limitations.
This reflects a core AI First Engineering principle:AI accelerates creation. Engineering judgment provides validation.