AI-Integrated Materials Engineering

The next-gen materials engineering program for smart manufacturing, semiconductors, and AI-powered innovation.

AI-Integrated Materials Engineering is a pioneering program co-developed by the Department of Materials Engineering and the Department of Computer Engineering at Kasetsart University.
It is designed to produce a new generation of engineers who understand that materials are the foundation of all engineering innovations — from smartphones and electric vehicles to spacecraft and next-generation batteries — and who can harness AI to unlock the full potential of these materials.

KU students will gain hands-on experience analyzing real production data, applying machine learning to predict component failures, and developing projects based on real-world challenges from industries such as energy, electronics, semiconductors, and smart manufacturing.

Throughout the program, students are mentored by expert faculty from both disciplines and collaborate with peers from other AIEP tracks — such as Mechanical, Industrial, and Environmental Engineering — fostering a truly interdisciplinary learning experience.

Program Overview

This track is part of the AI-Integrated Engineering Program (AIEP). It is offered as a special program under the Department of Materials Engineering.

Bachelor’s Program: Bachelor of Engineering (Materials Engineering)
Master’s Program: Master of Engineering (AI-Integrated Engineering)
First intake: Academic Year 2026
Number of Students: 20 (Special program)
Admission Channels: TCAS1 (Portfolio), TCAS2 (Quota)
Not available in regular or IUP tracks

Program Highlights

Integration of materials science, computer science, and AI for innovation in the digital transformation era
Strong foundation in engineering and scientific principles with hands-on research at both undergraduate and graduate levels
Capstone and Master’s projects based on real industry challenges
Courses co-taught by faculty in materials engineering and computer engineering
National and international research collaborations (e.g., NAIST, Japan)
Learning community shared with students from other AI+Engineering disciplines

What You’ll Learn

Students will gain comprehensive knowledge in core materials science, including the fundamentals of metals, polymers, ceramics, and composites. The program covers diverse applications across electronics, semiconductors, biomaterials, and energy sectors, providing a broad foundation in materials engineering.

Students will learn to apply AI techniques for materials design, simulation, failure analysis, and optimization, bridging traditional materials science with cutting-edge computational methods. The curriculum includes a capstone project that utilizes real industrial datasets, giving students hands-on experience with actual industry challenges.

Throughout the program, students will develop skills in cross-functional collaboration on AI-driven engineering problems, working alongside peers from other AIEP tracks to solve complex, interdisciplinary challenges that reflect real-world engineering practice.

Sample Capstone and Research Topics

Materials selection and design through simulation + ML
Predictive maintenance for production plants
Image recognition and ML for failure inspection
Data analytics for manufacturing optimization
Materials property prediction with machine learning

Industry & Research Partners

NAIST (Japan)
PTT Research & Innovation
PTTEP
PTTGC
WD (in collaboration with EE)
Delta (for MechE and IE)
AI research groups at Kasetsart University

4+1 Pathway

Students must meet academic requirements by their 3rd year by taking AI foundational courses.
Students start enrolling Master’s courses also in the 4th year. (See information for the M.Eng in AI-Integrated Engineering program.)
4th-year project linked to Master’s research in the 5th year.
Two degrees (B.Eng. + M.Eng.) in 5 years for future professionals.

The detailed study plan will be available soon. Read more about the undergraduate AI core courses and the Master program.

Career Opportunities

Materials informatics engineers in semiconductors, electronics, advanced materials
AI-enhanced reliability engineers in petrochemical & petroleum industries
Smart factory engineers for Manufacturing 5.0
Computational materials researchers
Technology developers in materials science + AI

Competitive Advantages

Dual-degree 4+1 track: Professional B.Eng. + AI-integrated M.Eng.
Deep interdisciplinary integration across departments
Strong partnerships for Capstone/Research with leading companies
Early access to advanced AI tools and research practices

Enrichment Activities

Capstone projects co-designed with leading industries
Undergraduate Research Opportunities (UROP) from early years
Internship programs in Thailand or abroad
Bootcamps, tech workshops, and international academic exchange
Guidance from academic and industry mentors

AI Foundation Courses (Undergraduate Level)

Students will take the following courses during their first 3 years as undergradute students.

Applied AI for Engineering (01204162)
Mathematical Foundations for AI Engineers (course under development)
Programming Concepts for Data Processing and Analysis (course under development)

See course descriptions.

Equivalent internal courses from Materials Engineering may be submitted for equivalency review.

Distinctive Graduate Outcomes

Graduates of the AI-Integrated Engineering Program will be able to:

Apply AI to improve efficiency and reliability in industrial processes
Leverage data and machine learning to accelerate materials development
Use predictive models to prevent failures in real-world production systems
Combine engineering fundamentals with AI fluency to drive future-ready innovation