BS Artificial Intelligence
Program Overview
The BS in Artificial Intelligence at NIT is designed for students who want to build intelligent systems that learn, reason, and support decision-making at scale. Developed using Arizona State University’s (ASU) curriculum, the program prepares graduates to work at the forefront of AI-driven technologies, data-centric systems, and intelligent automation.
Course Curriculum
Built on a strong foundation in programming, mathematics, statistics, and core computer science, the curriculum emphasizes algorithmic thinking, computational modeling, and responsible AI design. Students progress from foundational computing concepts into advanced areas such as machine learning, deep learning, natural language processing, computer vision, and intelligent systems.
The program balances theory and application, enabling students to design, train, evaluate, and deploy AI models that address real-world problems across industries. Strong emphasis is placed on ethical awareness, governance, and principled innovation, ensuring graduates understand not only what AI can do, but how it should be applied responsibly.
Hands-on learning is central to the program. Through labs, applied projects, structured internships, and a final-year capstone, students gain experience building AI-powered solutions for practical use cases. This ensures graduates are both technically capable and industry-ready.
Graduates of the BS in Artificial Intelligence are equipped for roles across intelligent systems development, data science, automation, and AI research, as well as for postgraduate study or innovation-led entrepreneurial ventures.
Accelerated Master’s Degree (4+1 Pathway):
High-performing students in the BS Artificial Intelligence program may pursue a 4+1 Accelerated Master’s Pathway, earning both a bachelor’s and a master’s degree in five years. Students complete four years of undergraduate study at NIT, followed by one year of postgraduate education at Arizona State University (ASU).
Through this pathway, students may progress to the MS in Artificial Intelligence in Business offered by the W. P. Carey School of Business at Arizona State University. This graduate program integrates advanced AI capabilities with strategic, managerial, and ethical perspectives, preparing students to lead AI adoption in organizational and business contexts.
The master’s program emphasizes mindful AI implementation, governance, and principled innovation, alongside advanced technical competence. Students gain the skills needed to apply AI responsibly in complex, real-world environments where technology, policy, and business intersect.
The accelerated pathway enhances technical depth, professional readiness, and global exposure, positioning graduates for senior roles in AI engineering, intelligent automation, analytics leadership, and AI-driven innovation.
Students may choose from three different specializations within the MS in Artificial Intelligence Engineering:
Human-Centered Artificial Intelligence. Focuses on applying advanced AI methods to systems that prioritize human interaction, behavior, and usability
Robotics, combines advanced AI with specialized robotics engineering to prepare students for cutting-edge roles in automation and intelligent systems
Mechanical Engineering. Combines advanced AI techniques with deep mechanical engineering knowledge
Practical Learning Experience
With a strong emphasis on real-world applications, the program includes capstone projects, hands-on labs, and electives in machine learning, digital signal processing, human-computer interaction, and more—ensuring students graduate job-ready and innovation-driven.Â
Note: 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.
Career Pathways:
Graduates of the BS in Artificial Intelligence are prepared for roles at the intersection of data, computation, and intelligent automation. The program develops strong analytical reasoning, algorithmic thinking, and ethical awareness in AI system development.
- Artificial Intelligence Engineer
- Machine Learning Engineer
- Data Scientist
- Computer Vision Engineer
- Natural Language Processing Specialist
- AI Research Assistant
- Robotics & Intelligent Systems Engineer
- Intelligent Automation Engineer
- AI Product Analyst
- Decision Systems Analyst
Graduates may also pursue advanced degrees, research careers, or innovation-led startups in AI and emerging technologies.
Admissions to earn the MS Artificial Intelligence
Master of Artificial Intelligence:  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.
NIT Admission Criteria:
The National Institute of Technology (NIT) seeks to admit academically prepared, motivated, and intellectually curious students who demonstrate the potential to contribute positively to the university’s learning environment and to society. Meeting the minimum eligibility requirements qualifies an applicant for admission evaluation but does not guarantee admission. Applicants may apply if they meet any one of the minimum criteria outlined below:
- Matriculation/Intermediate Requirements:
- 12 years of formal education with a minimum of 60% marks (no specific subject requirements).  Â
- Cambridge International (O & A Levels):
- O Level: Eight subjects (English, Mathematics, Urdu, Islamiat, Pakistan Studies + 3 electives), with an average of grade C. (Additional Mathematics does not count as an elective)
- A Level: Three principal subjects with an average of grade C. (Further Mathematics and General Paper are excluded.)
- International Baccalaureate (IB):
- Minimum 30/45 points.  Â
- English is compulsory; CAS and TOK must be completed.  Â
- Students must also pass Urdu, Islamiat and Pakistan Studies (via O-Level/SSC/IB).
- High School Diploma (HSD):
- Minimum 60% overall.  Â
- English is required, along with four principal electives in grades 9–12. Â
- Students must also pass Urdu, Islamiat and Pakistan Studies (via O-Level/SSC/HSD)Â
Fee Structure For The Academic Year 2025-26 (PKR)
One-time Admission Fee: 145,000
One-time Security Fee: 50,000
Semester Registration Fee: 40,000 per semester
Tuition Fee:
Fall semester: 547,500
Spring semester: 657,000
Total tuition fee for the Academic year 2025-26: 1,479,500
Program Plan
Year One
| Spring Semester 1 | Credits |
|---|---|
CSE 110: Principles of Programming | 4 Credits |
MAT 265 / MAT 170: Calculus for Engineers I | 3 Credits |
PHI 105/SSC 101: Islamic Studies | 2 Credits |
SSC 102: Pakistan Studies | 2 Credits |
FSE 100: Introduction to Engineering | 2 Credits |
SSC 100: Fundamentals of Academic Writing | 3 Credits |
| Total Credits | 16 Credits |
| Summer Semester 2 (Summer Session IV) | Credits |
|---|---|
CSE 205: Object-Oriented Programming and Data Structures | 4 Credits |
Understanding of Quran 1 | 1 Credits |
MAT 266 / MAT 265: Calculus for Engineers II | 3 Credits |
Civics and Community Engagement | 2 Credits |
EEE 120: Digital Design Fundamentals | 4 Credits |
PHY 121: University Physics I: Mechanics | 3 Credits |
PHY 122: University Physics I Lab | 1 Credits |
| Total Credits | 18 Credits |
Year Two
| Spring Semester 1 | Credits |
|---|---|
COM 225: Public Speaking and Presentations | 3 Credits |
QUR 101: Understanding of Quran || | 1 Credits |
CSE 240: Introduction to Programming Languages | 4 Credits |
MAT 243: Discrete Mathematical Structures | 3 Credits |
DAT 250: Data Science and Society | 3 Credits |
PSY 101: Introduction to Psychology (SOBE) | 3 Credits |
| Total Credits | 17 Credits |
| Summer Semester 2 (Summer Session IV) | Credits |
|---|---|
FIS 201: Innovation in Society (HUAD) | 3 Credits |
CEE 181: Technological, Social, and Sustainable Systems | 3 Credits |
CSE 230: Computer Organization and Assembly Language Programming | 4 Credits |
CSE 310: Data Structures and Algorithms | 4 Credits |
IEE 380: Probability and Statistics for Engineering Problem Solving | 3 Credits |
| Total Credits | 17 Credits |
Year Three
| Spring Semester 1 | Credits |
|---|---|
Ideology and Constitution of Pakistan | 2 Credits |
CSE 340: Principles of Programming Languages | 3 Credits |
CSE 331: Computing Ethics | 1 Credits |
Programming for AI | 4 Credits |
Machine Learning | 4 Credits |
Knowledge Representation and Reasoning | 3 Credits |
| Total Credits | 17 Credits |
| Summer Semester 2 (Summer Session IV) | Credits |
|---|---|
Elective 1 | 3 Credits |
CSE 445: Distributed Software Development/Parallel & Distributed Computing | 4 Credits |
CSE 412: Database Management | 4 Credits |
MAT 343: Applied Linear Algebra | 3 Credits |
Computer Vision | 3 Credits |
| Total Credits | 17 Credits |
Year Four
| Spring Semester 1 | Credits |
|---|---|
Artificial Intelligence Capstone Project I | 3 Credits |
Elective 2 | 3 Credits |
CSE 471: Introduction to Artificial Intelligence | 3 Credits |
IFT 511: Analyzing Big Data / Elective 3 | 3 Credits |
EEE 554: Probability and Radom Process / Elective 4 | 3 Credits |
Deep Learning | 3 Credits |
| Total Credits | 18 Credits |
| Summer Semester 2 (Summer Session IV) | Credits |
|---|---|
Artificial Intelligence Capstone Project II | 3 Credits |
FSE 501: Technology Entrepreneurship/Entrepreneurship | 3 Credits |
CSE 463: Introduction to Human Computer Interaction / Elective 5 | 4 Credits |
CSE 4**: Elective 6 | 3 Credits |
FSE502: Strategic Enterprise Innovation / Elective 7 | 3 Credits |
| Total Credits | 16 Credits |
List Of Electives
| Electives | Credits |
|---|---|
IFT 511: Analyzing Big Data / Elective 3 | 3 Credits |
EEE 554: Probability and Radom Process / Elective 4 | 3 Credits |
FSE502: Strategic Enterprise Innovation / Elective 7 | 3 Credits |
CSE 463: Introduction to Human Computer Interaction / Elective 5 | 4 Credits |
Natural Language Processing | 3 Credits |
Speech Processing | 3 Credits |
Data Mining | 3 Credits |
Advanced Statistics | 3 Credits |
Reinforcement Learning | 3 Credits |
Fuzzy Systems | 3 Credits |
Swarm Intelligence | 3 Credits |
Agent Based Modeling | 3 Credits |
| Electives for this program should include bridge courses for MS AI in Engineering at ASU: | |
|---|---|
IFT 511: Analyzing Big Data | 3 Credits |
CSE 575: Statistical Machine Learning | 3 Credits |
FSE 501: Technology Entrepreneurship | 3 Credits |
FSE 502: Strategic Enterprise Innovation | 3 Credits |
| Total Credits | 12 Credits |