While ChatGPT is not the only U.S.-based engineering school technology, it is the most obvious example of how Generative AI technology will disrupt the way engineers learn and how students use engineering software tools.
In 2026, students will use AI to research problems, generate design alternatives, write code and programs, analyze data, work with technical writing, and use Structured Query Language (SQL) to chart the results of engineering database analysis.
This will be most obvious in Capstone courses in which students will apply their knowledge of multiple engineering systems to solve a real-world, multi-variable problem.
AI will have the greatest impact in senior design. In 2026, many Stanford University senior design projects utilized AI technologies. Further, some senior design teams built AI agents as their final project deliverables. These topics are also being discussed by educators at the 2026 ASEE conference, focusing on the dilemmas that AI poses for senior design.
What are the Actual Changes?
| Earlier Engineering Workflow | AI Assisted Engineering Workflow |
|---|---|
| Manually search for initial info | Use AI to explore and organize possiblity space |
| Manually develop design alternatives | Generate alternates and evaluate |
| Write most code from scratch | Use AI assisted coding and debugging |
| Read every tech doc independently | Use AI to summarize and find relevrnt secitons |
| Analyze datasets manually | Use AI assisted analysis and visualization |
| Prepare document post development | Use AI during documetnation and review |
| Faculty focus on the final output | More focus on how stusents reached and verified the ans |
In the end, it is more correct to say, AI is changing where students spend their time in the Engineering process, more than replacing Engineering work.
AI is Becoming a Design Assistant
Let’s say a mechanical engineering student builds a thermal system.
Before, the student would have to manually come up with a bunch of configuration possibilities, evaluate each one, and pick the best design.
Now, with Generative AI, the student can get alternatives, parameter suggestions, equation explanations, simulation code, and even summary of documentaion.
This results in a new consitent need.
The student has to check if the result AI produced is correct.
A 2026 ASEE study assessing chemical engineering senior design utilized technologies such as ChatGPT, Gemini, Claude, and Copilot to write engineering solutions. Students were required to manually verify and compare each solution. The goal of this study was to teach students to exercise engineering judgement, not to build a faster solution.
Senior Design Projects are Mimicking AI Integration
AI is helping students complete projects, but in many cases, AI is becoming part of a system that is being constructed.
Senior design project teams construct AI-integrated inspection systems, self-driving robots, engineering copilots, predictive maintenance systems, or intelligent transportation systems.
AI integration in engineering is crossing disciplines beyond computer science.
Students looking for practical project directions can also explore Fried Engineers engineering project and research resources.
The Importance of Engineering Standards
There is the added risk that, for an engineering solution, Generative AI will output an answer that is incorrect or inappropriate.
Engineering students will still need to consider the density of the problem, safety, requirements, standards, industry, codes, analysis, and design.
The 2026 ASEE study assessing capstones utilized AI in context with the IEEE, NEC, IEC, UL, and others, and mandated that students assess the reliability of the AI-generated information.
Knowing how to obtain an AI-generated answer is not the most important skill. Knowing how to verify an AI-generated answer may be one of the essential skills for engineers.
What Does This Mean for Engineering Students?
Engineering students must learn to combine the use of AI with foundational engineering principles. Simply knowing how to utilize ChatGPT will not give them a competitive advantage.
A strong engineer will understand how to validate code generated by AI and analyze the underlying equations.
Can you check the equations if code for MATLAB is generated by AI?
Can you check if the specifications of a component that an AI tool has generated, are actually fulfilling the design requirements?
Can you assess the accuracy of the prediction given by an AI model and analyze the failure cases and the limits of the model?
Can you verify the engineering standard if an AI assistant has developed a summary of it?
These are engineering skills, not tricks of using a prompt.
Students interested in AI-centered senior projects can also explore 15 Creative AI-Powered Senior Projects for Engineering Students.
AI Will Also Alter How Senior Designs Are Analyzed
As an engineering student produces more senior designs with the assistance of AI, evaluating completed design work will become even more of a challenge.
Engineering programs will start emphasizing design, checking work, and providing reasons to justify design decisions.
ABET expectations will also continue to prioritize complex engineering design, ethics, and effective communication.
Final Thoughts
Generative AI is not obviating the need to study engineering.
AI tools will become a greater challenge if integrated into engineering work.
The best engineering students in 2026 will not be the ones that do not use AI tools, nor will they be the ones that are completely subservient to AI tools.
Those students will use AI to answer engineering problems more quickly while still showing engineering judgment, verification, experimentation, and responsibility.
This shift may affect engineering education more than the tools themselves.
Or, to be more specific, consider how AI tools will change the criteria used to evaluate engineering students.
For years, students have been evaluated on their engineering judgment. Judging the veracity of a proposed engineering idea is where engineering judgment is most evident. Engineering judgment will continue to be relevant in the context of AI, but more instance examples are needed for judgment. More AI examples will be needed to satisfy judgment in engineering.
