1. Program Overview

The Artificial Intelligence (AI) program is designed to equip students with a strong foundation in mathematics, computer science, data processing, knowledge representation, and the development of intelligent systems. In addition to professional knowledge, students are provided with essential understanding of social sciences, law, and professional ethics.

The program follows an application-oriented and career-focused approach, enabling students to develop competencies in designing, developing, and deploying AI systems that address the needs of businesses and society. Upon graduation, students will be capable of analyzing, processing, and evaluating data; developing machine learning and deep learning models; building modern AI applications; working independently or collaboratively in teams; and continuously learning and adapting to the rapid advancement of digital technologies.

2. Curriculum Structure

The program requires the completion of 153 credits (excluding Physical Education and National Defense & Security Education) and is organized into four knowledge blocks:

The program is structured into two educational stages:

• Stage 1: Undergraduate education leading to the degree of Bachelor of Artificial Intelligence.
• Stage 2: Advanced professional education leading to the degree of Engineer in Artificial Intelligence.

3. Course List

3.1. General Education Courses

Compulsory Courses

• Marxist-Leninist Philosophy
• Marxist-Leninist Political Economy
• Scientific Socialism
• Ho Chi Minh Thought
• History of the Communist Party of Vietnam
• Linear Algebra
• Calculus and Engineering Applications
• Probability and Statistics for Data Science
• English 1
• English 2
• English 3
• Law and Human Rights
• Physical Education 1, 2, 3
• National Defense and Security Education 1, 2, 3, 4

Elective Courses

• Critical Thinking
• Logic
• Corporate Culture
• Innovation and Entrepreneurship

3.2. Fundamental Discipline Courses

Compulsory Courses

• Programming Fundamentals for Artificial Intelligence
• Programming Fundamentals for Artificial Intelligence Laboratory
• Programming Techniques for Artificial Intelligence
• Programming Techniques for Artificial Intelligence Laboratory
• Data Structures and Algorithms with Python
• Data Structures and Algorithms with Python Laboratory
• Introduction to Artificial Intelligence
• Introduction to Artificial Intelligence Laboratory
• Database Systems
• Database Systems Laboratory
• Mathematics for Machine Learning
• Mathematics for Machine Learning Laboratory
• Computer Networks
• Computer Networks Laboratory
• Discrete Structures
• Operating Systems

Elective Courses

• Numerical Methods
• Bayesian Statistics
• Foundations of Data Analytics
• Foundations of Data Analytics Laboratory
• Data Mining
• Data Mining Laboratory
• Data Warehousing
• Data Warehousing Laboratory

3.3. Specialized Courses

Compulsory Courses

• Information Technology Project Management
• Machine Learning
• Machine Learning Laboratory
• Deep Learning
• Deep Learning Laboratory
• Introduction to Natural Language Processing
• Introduction to Natural Language Processing Laboratory
• Computer Vision
• Computer Vision Laboratory
• Multi-Agent Systems
• Big Data
• Big Data Laboratory
• Multimodal Data Mining
• Multimodal Data Mining Laboratory
• AI System Operations
• Ethics and Law in Artificial Intelligence
• English for Artificial Intelligence
• Internship
• Graduation Thesis

Elective Courses

• Large Language Models
• Intelligent Systems
• Modeling and Optimization for Machine Learning
• Modeling and Optimization for Machine Learning Laboratory
• Generative Artificial Intelligence
• Artificial Intelligence Applications in Healthcare
• Artificial Intelligence Applications in Healthcare Laboratory
• Artificial Intelligence Applications in E-commerce and Digital Marketing
• Artificial Intelligence Applications in E-commerce and Digital Marketing Laboratory
• Artificial Intelligence Applications in Food Technology
• Artificial Intelligence Applications in Food Technology Laboratory
• Artificial Intelligence Applications in Biotechnology
• Artificial Intelligence Applications in Biotechnology Laboratory
• Technical Report Writing and Presentation Skills
• Research Methodology

3.4. Advanced Professional Courses (Engineer Program)

Compulsory Courses

• Advanced Machine Learning
• Advanced Natural Language Processing
• Engineering Internship
• Engineering Capstone Project

Elective Courses

• Blockchain Technology
• Decision Support Systems
• IoT Application Development
• Social Network Analysis
• Graph Machine Learning
• Deep Learning-Based Object Recognition

 4. Admission Information

Applicants must possess a high school diploma (or equivalent qualification) and satisfy the admission requirements established by the University.

The program also recognizes prior learning outcomes for graduates of relevant college programs, students pursuing a second major, and holders of a previous bachelor's degree, in accordance with the University's academic regulations.

5. Program Duration and Credit Requirements

The standard duration of the program is 3.5 years.

Students are required to accumulate 153 credits (excluding Physical Education and National Defense & Security Education) to complete the curriculum.

Students who complete Stage 1 will be awarded the Bachelor's Degree in Artificial Intelligence. Those who successfully complete the advanced professional stage will be awarded the Engineer Degree in Artificial Intelligence.

6. Career Opportunities

Graduates of the Artificial Intelligence program may pursue a wide range of professional careers, including:

• AI Application Engineer, developing software solutions, digital platforms, and intelligent automation systems.
• Data Analyst or Data Processing Specialist, responsible for designing data processing workflows and supporting decision-making processes.
• AI Solution Specialist, developing and deploying intelligent systems such as chatbots, recommender systems, computer vision applications, natural language processing systems, and other AI-powered products.
• Research and Development (R&D) Engineer, conducting research, experimentation, and innovation in AI algorithms, models, and technologies.
• Lecturer or Researcher in the fields of Information Technology, Computer Science, and Artificial Intelligence at universities, research institutes, and technology centers.

The program also provides a solid foundation for pursuing postgraduate studies in Artificial Intelligence, Computer Science, Data Science, Information Technology, and related disciplines.

7. Program Learning Outcomes (PLOs)

PLO1 (C3 – Bachelor; C3 – Engineer)

Apply fundamental knowledge of mathematics, natural sciences, social sciences, and information technology to the field of Artificial Intelligence.

PLO2 (C4 – Bachelor; C5 – Engineer)

Analyze and integrate disciplinary, specialized, and practical knowledge to identify appropriate solutions for Artificial Intelligence problems.

PLO3 (P3 – Bachelor; P4 – Engineer)

Demonstrate professional skills, including systems thinking, in solving Artificial Intelligence-related problems.

PLO4 (P3 – Bachelor; P3 – Engineer)

Demonstrate self-learning, research, and knowledge discovery skills in the field of Artificial Intelligence.

PLO5 (A3 – Bachelor; A3 – Engineer)

Adhere to professional ethics and social responsibility in the development and application of Artificial Intelligence systems.

PLO6 (P3 – Bachelor; P3 – Engineer)

Demonstrate effective teamwork, collaboration, and organizational skills.

PLO7 (P4 – Bachelor; P4 – Engineer)

Communicate effectively and utilize foreign language skills in professional environments.

PLO8 (R4 – Bachelor; R4 – Engineer)

Analyze societal contexts and industry needs to develop Artificial Intelligence solutions that address real-world challenges.

PLO9

• P3 – Bachelor: Plan, coordinate, evaluate, and improve Artificial Intelligence projects and activities.
• P4 – Engineer: Organize, manage, and continuously improve professional activities in the field of Artificial Intelligence.

Competency Scale Legend

• C (Cognitive Domain): Knowledge competency based on Bloom’s Taxonomy.
• P (Psychomotor Domain): Professional and practical skills competency.
• A (Affective Domain): Attitudes, ethics, and professional values.
• R (Crawley Proficiency Rating Scale): Professional autonomy, responsibility, and occupational competence.