Your students already use AI. This textbook teaches them to use it without losing the physics.

Learn Engineering Thermodynamics with AI is a complete first course in engineering thermodynamics, written for a classroom where a chatbot can return a confident, fluent, and sometimes wrong answer in seconds. It covers everything a first course needs, then carries on into the mechanical and chemical applications engineers use in practice. Each chapter also teaches a second skill: working with AI so that your own understanding grows instead of being replaced.

Four defined roles for AI

AI never appears as a vague instruction to “use it responsibly.” It always works in one of four roles:

Tutor: withholds the answer until you have attempted the reasoning.
Verifier: finds your first mistake without fixing it for you.
Critic: hunts for invalid assumptions and unit errors.
Calculator: limited to a narrow, pre-specified numerical task.

Chapters include AI failure cases, where a plausible answer turns out to be technically wrong, and transcripts of students catching a mistake mid-conversation. A prompt protocol, academic integrity guidance, and rules for recording data sources and property libraries complete the method.

What you will learn

Properties, phase behavior, ideal and real gases, and equations of state.
The first law for closed systems and control volumes.
The second law, entropy balances, isentropic efficiency, and exergy.
Thermodynamic potentials and property relations.
Steam power, gas turbine, and internal-combustion cycles.
Refrigeration, heat pumps, refrigerant selection, psychrometrics, and HVAC.
Mixtures, fugacity, activity coefficients (NRTL and UNIQUAC), and phase equilibrium.
Reaction equilibrium, Gibbs minimization, combustion, flame temperature, and fuel cells.

Built for learning

Three running case studies, a reheat steam power plant, an air-source heat pump, and a methane combustion train, return with fixed numbers so you see the same system at greater depth.
Learning objectives, worked examples, exercises marked by difficulty, and mastery checks for every chapter.
Integrative projects and a guide to further exploration.

Who it is for: undergraduate engineering students, self-learners, and the instructors preparing them for careers in which AI is part of the job.

About the author: Seif-Eddeen K. Fateen is a Professor of Chemical Engineering at Zewail City of Science and Technology in Egypt. He holds a PhD in Chemical Engineering from MIT and has published more than twenty peer-reviewed papers on applied thermodynamics.