Master of Science in AI in Engineering
Program Overview
The Master of Science in Artificial Intelligence in Engineering at NIT is developed using Arizona State University’s curriculum and offers a 1+1 pathway with the Ira A. Fulton Schools of Engineering at Arizona State University (ASU).
Course Curriculum
The Master of Science in Artificial Intelligence Engineering offers three different specializations:
1. Human-Centered Artificial Intelligence. Focuses on applying advanced AI methods to systems that prioritize human interaction, behavior, and usability. Students gain expertise in machine learning, natural language processing, computer vision, and other AI tools, tailored to solving engineering problems involving human factors. The program combines a strong foundation in AI systems, tools, ethics, and data analysis with specialized courses in human-centered design and systems engineering. Graduates are equipped to create AI solutions that are effective, responsible, and user-focused across a wide range of industries.
2. Mechanical Engineering. Combines advanced AI techniques with deep mechanical engineering knowledge. Students learn to apply tools like machine learning, robotics, and computer vision to solve engineering-specific problems. The program includes core courses in AI foundations, ethics, and data systems, alongside mechanical engineering electives. Graduates are equipped to innovate at the intersection of AI and engineering.
Two Master’s Degrees Instead of One – 1+1 Pathway
Students pursuing their Master of Science in AI in Engineering from NIT have the unique opportunity to participate in the 1+1 pathway at Arizona State University (ASU) to earn two master’s degrees – one from Pakistan and one from the U.S.
Degree Pathway with ASU – How It Works
Start your master’s degree in AI in Engineering at NIT and transfer a portion of your NIT credits toward an additional master’s degree from the Ira A. Fulton Schools of Engineering, graduating with two master’s degrees in just two years.*
Students choosing to study on campus at ASU may apply for OPT, allowing eligible graduates the opportunity to work legally in the United States for up to three years (depending on program eligibility).
Upon successful completion of the 1+1 pathway, you graduate with two master’s degrees:
- Master of Science in Artificial Intelligence in Engineering from National Institute of Technology, Pakistan
- Master of Science in Artificial Intelligence Engineering, with concentrations in Human-Centered Artificial Intelligence and Mechanical Engineering from Ira A. Fulton Schools of Engineering at Arizona State University, United States
*Program details may vary based on academic progress and successful credit transfer and approval by each university. Students participating in the 1+1 pathway must work with their program director at NIT to ensure that credits earned at ASU meet master’s degree requirements at NIT.
Program Duration
2 Years (1st year at NIT and 2nd year in ASU)
Careers
1. Human systems engineers with a background in AI can pursue opportunities in a variety of fields to develop, customize and apply AI systems and tools while taking ethical and societal considerations into account. Applicable roles:
- Human-robot teaming
- Learning design
- Product design
- Workplace and patient safety
2. Mechanical engineers with a background in AI can pursue opportunities in a variety of fields to develop, customize and apply AI systems and tools while taking ethical and societal considerations into account. Applicable roles:
- Autonomous vehicles
- Energy systems
- Manufacturing
- Product design and development
- Robotics
Master’s degrees available from ASU
- MS in Artificial Intelligence Engineering (Human-Centered Artificial Intelligence)
- MS in Artificial Intelligence Engineering (Mechanical Engineering)
Offered by Ira A. Fulton Schools of Engineering at Arizona State University (ASU)
Admissions to earn a Master's degree at NIT
Applicants must have completed sixteen (16) years of education, or a four-year bachelor’s degree comprising at least 130 credit hours after HSSC/F.A./F.Sc. or an equivalent Grade 12 qualification.
In addition, candidates are required to successfully pass the prescribed Admission Test and Interview as part of the selection process.
Admissions to earn the MS AI Engineering from Ira A. Fulton Schools of Engineering at Arizona State University (ASU)
NIT undergraduate program diploma + official transcripts from every college or institution attended, including NIT’s. Must submit original transcripts and English translated transcripts.
Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = “A”) in the last 60 hours of their first bachelor’s degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = “A”) in an applicable master’s degree program.
All applicants must demonstrate relevant coursework or experience in the following three areas:
- Undergraduate linear algebra (e.g., MAT 242 Elementary Linear Algebra) and Calculus 1, 2, and 3.
- 300-level courses relevant to the concentration you are applying to. For example, the EE concentration requires EEE 350 or equivalent.
- Familiarity with Matlab, Python, SQL, R, or other relevant programming skills (in the professional resume).
Proof of English proficiency: TOEFL>90 iBT, IELTS>7, Pearson Test of English>65, Duolingo>115, all taken within the last two years from start date.
Program Plan
Year One
| Semester 1 | Credits |
|---|---|
Analyzing Big Data | 3 Credits |
Probability and Random Processes | 3 Credits |
Artificial Intelligence | 3 Credits |
Research Methodology | 3 Credits |
| Total Credits | 12 (out of 12) |
| Semester 2 | Credits |
|---|---|
Technology Entrepreneurship | 3 Credits |
Strategic Enterprise Innovation | 3 Credits |
Statistical Machine Learning | 3 Credits |
Evolutionary Computation | 3 Credits |
| Total Credits | 09 (out of 12) |
Year Two
| Semester 3 | Credits |
|---|---|
Artificial Intelligence Ethics and Social Responsibility | 3 Credits |
Data Visualization - AI Systems and Tools | 3 Credits |
Generative AI | 3 Credits |
Statistical and Mathematical Methods for Data Science | 3 Credits |
Thesis I | 3 Credits |
| Total Credits | 9 (out of 15) |
| Semester 4 | Credits |
|---|---|
Explainable Artificial Intelligence | 3 Credits |
Natural Language Processing | 3 Credits |
Intelligent Distributed Systems | 3 Credits |
Robotics | 3 Credits |
Thesis II | 3 Credits |
| Total Credits | 6 (out of 15) |
Core Courses
| Core Courses: |
|---|
Analyzing Big Data |
Probability and Random Processes |
Artificial Intelligence |
Research Methodology |
Thesis I |
Electives
| Electives: |
|---|
Technology Entrepreneurship |
Strategic Enterprise Innovation |
Statistical Machine Learning |
Evolutionary Computation |
Artificial Intelligence Ethics and Social Responsibility |
Data Visualization - AI Systems and Tools |
Generative AI |
Statistical and Mathematical Methods for Data Science |
Explainable Artificial Intelligence |
Natural Language Processing |
Intelligent Distributed Systems |
Robotics |