Texas Tech professor awarded AI Mini Grant to bring textbook figures to life

Thanakorn Khamvilai, an assistant professor in the Department of Mechanical Engineering at the Edward E. Whitacre Jr. College of Engineering at Texas Tech University, has been awarded a Teaching, Learning, & Professional Development Center (TLPDC) AI Mini Grant to support a cutting-edge project aimed at revolutionizing how undergraduate engineering students learn complex dynamics concepts.

The project, titled Enhancing Conceptual Understanding in Undergraduate Engineering Students Through Generative AI Coding and Animation Tools, received $2,000 in funding, with an additional $1,000 bonus contingent upon future publication. Khamvilai’s work focuses on using generative AI and animation tools to convert static textbook figures into dynamic visualizations for Engineering Mechanics: Dynamics—a critical course taken by second- and third-year mechanical, civil and aerospace engineering students (ME 2302 and CE 3302 at Texas Tech).

“Our goal is to help students visualize static figures in the textbook by making these figures come to life using Gen-AI and engineering equations of motion,” Khamvilai said.

The project utilizes generative text-based AI models such as ChatGPT-4 and Gemini, as well as video-based models like Veo 3, in combination with Python’s Manim animation library and MATLAB’s animation tools. These technologies enable the creation of animations that illustrate particle and rigid body motion, velocity and acceleration vectors, curvilinear paths, and relative motion scenarios. The animations will be integrated into lectures, online modules, and self-paced learning materials using content from the textbook Engineering Mechanics: Dynamics (15th Edition) by R.C. Hibbeler.

A significant component of the initiative is a student-centered, AI-assisted coding workflow. Students will have the opportunity to input textbook problems or hand-drawn diagrams into AI tools to generate starter animation code. By modifying these scripts to change initial conditions or add visual elements like vectors and coordinate frames, students will engage in active problem-solving and visualization.

For example, in a scenario involving two aircraft in relative motion, a student could prompt ChatGPT to create an animation based on varying speeds and trajectories. They could then edit the code to observe how different reference frames affect the velocity vectors.

In alignment with the goals of the TLPDC AI Mini Grant, Khamvilai will disseminate the project’s findings through a TLPDC workshop, a blog post for the center’s “Small Bytes” series, or a presentation at a TLPDC conference during the 2025-2026 academic year. The grant also encourages recipients to publish their work as peer-reviewed Scholarship of Teaching and Learning (SoTL).

This initiative represents a growing trend in engineering education—integrating AI and animation to make theoretical learning more intuitive, interactive, and personalized.

For more on the grant, visit the Scholarship of Teaching and Learning  Mini-Grants section of the Teaching, Learning, & Professional Development Center website.