MS Artificial Intelligence in Engineering
Program Overview
The Master in Artificial Intelligence Engineering at NIT is developed using Arizona State University’s (ASU) curriculum and offers a dual degree pathway (1+1) with both NIT and Ira A. Fulton Schools of Engineering at Arizona State University (ASU).
Course Curriculum
The MS 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. Robotics.
Combines advanced AI with specialized robotics engineering to prepare students for cutting-edge roles in automation and intelligent systems. The program emphasizes core topics such as articulated and aerial robots, human-robot teaming, robot learning, and the integration of generative AI and large language models in robotics. Students build strong foundations in AI systems, tools, and ethics while developing key data science skills in data processing, visualization, mining, and machine learning. With interdisciplinary coursework and robotics-specific training, graduates are equipped to lead innovation at the intersection of AI, robotics, and industrial automation.
3.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.
Dual Degree Pathway (1+1 Program)*
Students have the possibility to complete their Master’s degree at NIT or opt to study one year at NIT and their final year at ASU, either online or on-campus, graduating with two Master’s degrees: one from NIT and one from Ira A. Fulton Schools of Engineering at Arizona State University (ASU)—gaining a competitive edge in global tech sectors.
* Final year at ASU and dual degree eligibility depend on successful credit transfer and approval by Arizona State University. Program details may vary based on academic progress
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. Robotics 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
- Intelligent manufacturing robots
- Mobile drone swarms
- Surgical robots
3. 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 (Robotics)
- 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 MBA degree by W. P. Carey School of Business 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 |