AI-Integrated Materials Engineering
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 is shown below. Read more about the undergraduate AI core courses and the Master program.
Study plan
| Year 1, First semester | |||
|---|---|---|---|
| 01999111 | Wisdom of the Land | 2 (2-0-4) | |
| 01208111 | Engineering Drawing | 3 (2-3-6) | |
| 01417167 | Engineering Mathematics I | 3 (3-0-6) | |
| 01420111 | General Physics I | 3 (3-0-6) | |
| 01420113 | Physics Laboratory I | 1 (0-3-2) | |
| 01999xxx | Thai Language | 3 (3-0-6) | |
| 01355xxx | Foreign Language Course (1 Language) | 3 (- -) | |
| General Education - IT/Computing | 3 (- -) | ||
| Total | 21 (- -) | ||
| Year 1, Second semester | |||
| 01200101 | Innovative Thinking | 3 (3-0-6) | |
| 01204111 | Computer and Programming | 3 (2-3-6) | |
| 01213211 | Materials Science for Engineers | 3 (3-0-6) | |
| 01403114 | Laboratory in Fundamentals of General Chemistry | 1 (0-3-2) | |
| 01403117 | Fundamentals of General Chemistry | 3 (3-0-6) | |
| 01417168 | Engineering Mathematics II | 3 (3-0-6) | |
| 01420112 | General Physics II | 3 (3-0-6) | |
| 01420114 | Laboratory in Physics II | 1 (0-3-2) | |
| Total | 20 (- -) | ||
| Year 2, First semester | |||
| 01206221 | Applied Probability and Statistics for Engineers | 3 (3-0-6) | |
| 01208221 | Engineering Mechanics I | 3 (3-0-6) | |
| 01213212 | Fundamental of Inorganic Materials | 4 (4-0-8) | |
| 01213213 | Principle Chemistry for Organic Materials | 4 (4-0-8) | |
| 01213214 | Principle Chemistry Laboratory for Organic Materials | 1 (0-3-2) | |
| 01175xxx | Physical Education | 1 (0-2-1) | |
| 01355xxx | Foreign Language Course (1 Language) | 3 (- -) | |
| 01204262 | Programming Principles for Data Processing and Analysis for Applied AI | 3 (3-0-6) | AI Foundation Course |
| Total | 22 (- -) | ||
| Year 2, Second semester | |||
| 01205201 | Introduction to Electrical Engineering | 3 (3-0-6) | |
| 01208281 | Workshop Practice | 1 (0-3-2) | |
| 01213216 | Mechanical Behavior of Materials | 4 (4-0-8) | |
| 01213217 | Thermodynamics of Materials | 3 (3-0-6) | |
| 01213218 | Manufacturing Processes for Materials Engineers | 3 (3-0-6) | |
| 01213219 | Materials Processing Laboratory | 1 (0-3-2) | |
| 01417267 | Engineering Mathematics III | 3 (3-0-6) | |
| 01204162 | Applied AI for Engineering | 3 (3-0-6) | AI Foundation Course |
| Total | 21 (- -) | ||
| Year 3, First semester | |||
| 01205202 | Electrical Engineering Laboratory I | 1 (0-3-2) | |
| 01208381 | Mechanical Engineering Laboratory I | 1 (0-3-2) | |
| 01213311 | Principle of Characterization Techniques | 3 (3-0-6) | |
| 01213312 | Materials Characterization and Properties Analysis Laboratory | 1 (0-3-2) | |
| 01213313 | Kinetics and Transport Phenomena in Materials Engineering | 4 (4-0-8) | |
| 01213314 | Failure Analysis and Prevention | 3 (3-0-6) | |
| 01213395 | Research Proposal Preparation | 1 (0-3-2) | |
| Total | 14 (- -) | ||
| Year 3, Second semester | |||
| 01213316 | Materials Industry in Thailand | 1 (0-3-2) | |
| 01213497 | Seminar | 1 (- -) | |
| 01355xxx | Foreign Language Course (1 Language) | 3 (- -) | |
| 01204261 | Mathematical Foundations for Applied AI | 3 (3-0-6) | AI Foundation Course |
| General Education – Well-Being Studies | 3 (- -) | ||
| Free Elective | 3 (- -) | ||
| General Education – Aesthetic Studies | 3 (- -) | ||
| Total | 20 (- -) | ||
| Year 4, First semester | |||
| 01213399 | Intership | 1 | |
| Major Elective | 6 (- -) | ||
| Free Elective | 3 (- -) | ||
| Total | 10 (- -) | ||
| Graduate courses (enrolled on year 4, first semester) | |||
| 01204xxx | Research methodology | 1 | Graduate required course |
| 01204xxx | Data Acquisition | 1 | Graduate required course |
| 01204xxx | Data Preprocessing | 1 | Graduate required course |
| 01204xxx | Database and Data Warehouse | 1 | Graduate required course |
| Electives | 2 - 3 | Graduate elective course | |
| Year 4, Second semester | |||
| 01213411 | Materials Selection and Engineering Design | 3 (3-0-6) | |
| 01213412 | Production Management for Materials Industry | 3 (3-0-6) | |
| 01213499 | Materials Engineering Project | 3 (0-9-5) | |
| Total | 9 (- -) | ||
| Graduate courses (enrolled on year 4, second semester) | |||
| 01204xxx | Seminar | 1 | Graduate required course |
| 01204xxx | Advanced Machine Learning I | 1 | Graduate required course |
| 01204xxx | Advanced Machine Learning II | 1 | Graduate required course |
| 01204xxx | AI for data interpretation | 1 | Graduate required course |
| Electives | 2 - 3 | Graduate elective course | |
| Graduate year 1, First semester | |||
| 01204xxx | Seminar | 1 | Graduate required course |
| 01204xxx | Thesis | 6 | Graduate thesis |
| Electives | 6 - 7 | Graduate elective course | |
| Graduate year 2, Second semester | |||
| 01204xxx | Thesis | 6 | Graduate thesis |
| Electives | 3 - 4 | Graduate elective course | |
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