Webinar Recap | Inside WashU Engineering: Three Mini Courses Exploring Innovation in Engineering During the AI Era

In June 2026, leading scholars from the McKelvey School of Engineering at Washington University in St. Louis (WashU), along with an industry CTO, provided students with an in-depth exploration of how traditional engineering disciplines are undergoing digital transformation in the AI era.

Through three mini courses covering business transformation, algorithm development, robotics design, advanced materials, and multiphysics simulation, the speakers analyzed emerging opportunities in engineering innovation and highlighted the core competencies that engineering graduates will need to remain globally competitive over the next 5–10 years.

About the McKelvey School of Engineering at Washington University in St. Louis

The McKelvey School of Engineering at Washington University in St. Louis (WashU) has established a world-class research and education ecosystem in areas including artificial intelligence, robotics, and systems science.

This webinar featured faculty members and admissions representatives from the Department of Computer Science & Engineering (CSE) and the Department of Electrical & Systems Engineering (ESE), who provided applicants with a comprehensive overview of program selection, academic expectations, and career opportunities in the United States.

Course 1: Understanding How Disruptive Technologies Transform the Business World

The first session was delivered by Professor Dan Sherman.

With extensive industry experience in enterprise technology and digital transformation, Professor Sherman introduced key concepts from his McKelvey Engineering course, “Emerging and Disruptive Technologies,” explaining how companies evaluate, adopt, and implement emerging technologies.

The session explored three major theoretical frameworks that influence technology development.

1. Law of Diffusion of Innovation

The Diffusion of Innovation theory explains that the adoption of new technologies typically occurs through different groups of users:

• Innovators

• Early Adopters

• Early Majority

• Late Majority

• Laggards

The widespread adoption of a technology often depends on successfully engaging the key early user groups who are willing to embrace innovation first.

2. Crossing the Chasm Theory

The session introduced the “Crossing the Chasm” theory proposed by Geoffrey Moore.

According to this theory, emerging technologies often face a significant challenge when transitioning from early adopters to the mainstream market.

Only by successfully overcoming this gap and achieving broader market acceptance can a technology achieve large-scale adoption.

3. Prospect Theory

The course also introduced Prospect Theory, developed by Nobel Prize-winning economist Daniel Kahneman.

The theory highlights that in business decision-making, innovations are more likely to be accepted when their perceived value significantly outweighs potential risks.

Course 2: AI Empowering Mechanical Engineering and Materials Science Research

The second course focused on the application of artificial intelligence in mechanical engineering and materials science.

Faculty members introduced how researchers at McKelvey Engineering integrate AI, machine learning, and robotics technologies into traditional engineering research.

Biomechanics

Research examples included:

• Using AI to automatically analyze complex medical brain imaging data and assist in identifying physical differences between healthy and abnormal tissues;

• Applying machine learning to build three-dimensional models of the human heart, improving the efficiency of dynamic simulations and enabling faster predictive analysis.

Materials Science

The course demonstrated how AI is being applied in advanced materials research:

• Using algorithms to optimize imaging performance in electron microscopy technologies, including Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM);

• Applying machine learning to assist material classification and design, enabling the exploration of new nanomaterials with specific properties.

Thermal & Fluid Science

In research areas involving porous media, batteries, and electrolytes, machine learning is being used to support the development of complex fluid models.

Through AI-assisted modeling, researchers can improve the computational efficiency of traditional physics-based simulations and accelerate scientific discovery.

Aerospace

The course introduced applications of artificial intelligence in aerospace engineering design.

For example, Physics-Informed Neural Networks (PINNs) allow researchers to combine physical principles with AI models to improve the efficiency and accuracy of aerodynamic predictions.

Course 3: Robotics Design — From Theoretical Learning to Real-World Engineering Practice

The third course focused on robotics design education at McKelvey Engineering.

Through project-based learning, students are required to:

• Select components based on practical budget constraints;

• Complete robotic system design and assembly;

• Develop control algorithms;

• Optimize system performance.

Ultimately, students are expected to build robotic systems capable of autonomous operation while meeting engineering performance requirements.

The course emphasizes the integration of engineering theory, hardware design, and artificial intelligence algorithms to develop students’ ability to solve real-world engineering challenges.

Faculty Q&A: How Can Engineering Students Build Competitiveness in the AI Era?

Q1: How does McKelvey Engineering evaluate applicants’ AI capabilities?

The faculty explained that AI programming skills or large language model development experience are not the only criteria used in the admission process.

As long as students meet the fundamental admission requirements, McKelvey Engineering’s curriculum is designed to help students gradually develop their AI-related knowledge and capabilities.

Q2: Without a technical background, how can students succeed in the AI era?

The professor shared that even with extensive CTO experience, he believes that strategic thinking and the ability to continuously learn are essential.

In the AI era, success is not only about mastering coding skills, but also about understanding how to use AI tools to solve meaningful real-world problems.

Q3: Is interdisciplinary collaboration accessible within McKelvey Engineering?

The professor emphasized that interdisciplinary collaboration is highly encouraged within the school.

Students can collaborate across fields including:

• Electrical & Systems Engineering (ESE);

• Biomedical Engineering (BME);

• Computer Science (CS).

Additionally, after completing prerequisite requirements, students can select courses across different disciplines through the university’s academic system.

Q4: How does McKelvey Engineering help international students build career competitiveness in the U.S.?

The professor explained that the university supports students’ career development through activities such as career fairs and industry networking opportunities.

In addition, engineers from industry often participate in classroom discussions and course projects, allowing students to establish connections with professionals and gain exposure to real-world engineering practices.

Q5: What skills will engineering students need most over the next 5–10 years?

The professor believes that future engineering professionals need to combine:

• Strong foundations in traditional engineering disciplines;

• The ability to effectively apply AI tools;

• Critical thinking skills.

While AI can significantly improve computational and analytical efficiency, engineers must still understand fundamental physical principles and evaluate whether AI-generated results are accurate, safe, and practically applicable.