Artificial Intelligence and Frontier Systems: AI + X
What is the Dual Degree Programme?
Seoul Cyber University (SCU, Republic of Korea) and Maqsut Narikbayev University (MNU, Astana, Kazakhstan) jointly offer a four-year dual-degree bachelor’s programme in Artificial Intelligence (AI) and Frontier Systems (AI + X). Built on a Flexible 1+2+1 structure, students study Year 1 and Year 4 at MNU and Years 2–3 at SCU (fully online); because SCU teaches its courses fully online, selected SCU-taught courses may also be taken during Years 1 and 4. Upon fulfilling the graduation requirements of both institutions, students are awarded bachelor’s degrees from MNU and from SCU. The programme is delivered in English and is implemented in collaboration with MNU.
The AI + X Model
- The programme combines a rigorous AI-engineering core — programming, data analysis, machine and deep learning, and the design, deployment, and operation of AI systems — with a chosen professional domain (“X”). Students select a concentration and build domain expertise alongside advanced AI competencies, enabling them to apply artificial intelligence to real problems in the digital economy, industry, and infrastructure while considering engineering, ethical, and social dimensions. AI + X concentrations offered through this dual track: Finance, Economics, Business Management, Marketing, Psychology, Tourism Management, Law, and Digital Linguistics.
Programme Objective
- The programme prepares specialists in artificial intelligence and intelligent-systems engineering, providing fundamental knowledge and practical skills in programming, data analysis, machine and deep learning, and the design, deployment, and maintenance of AI systems. Graduates apply AI technologies to solve applied problems in the digital economy, industry, and infrastructure, taking engineering, ethical, and social aspects into account. Through the AI + X model, students choose a concentration to gain in-depth domain knowledge together with the ability to integrate artificial intelligence.
Programme at a Glance
| Programme | Artificial Intelligence and Frontier Systems (AI + X) |
|---|---|
| Partner institutions | MNU (Astana, Kazakhstan) × SCU (Seoul, Republic of Korea) |
| Degree | Dual Bachelor’s degree — one from each institution |
| Duration | 4 years — Flexible 1+2+1 |
| Total credits | 240 ECTS |
| Language of instruction | English |
| Study mode | Full-time; Blended (online + on-campus) |
| Host by Year | Year 1 & Year 4 at MNU · Year 2 & Year 3 at SCU (online) |
| AI + X Tracks | AI + Finance · AI + Economics · AI + Business Management · AI + Marketing · AI + Psychology · AI + Tourism Management · AI + Law · AI + Digital Linguistics |
Academic Structure — Flexible 1+2+1
- Years 1 and 4 are based at MNU; Years 2 and 3 are completed online through SCU - no travel to Korea required.
- Flexible scheduling: reflecting SCU’s fully online delivery, selected SCU-taught courses may also be taken during Years 1 and 4, spreading this coursework across the programme.
- Progression: students advance to the next stage upon completing the requirements of each stage.
- Academic administration at each stage follows the regulations of the hosting institution.
- The programme's AI-core and mathematics component — 20 courses, equivalent to 120 ECTS — is taught online by SCU; MNU delivers general education and the chosen AI + X (domain) concentration.
Eligibility & Admission
- Applicants must meet the admission requirements of both MNU and SCU. Students begin the programme at MNU in Year 1, including SCU-taught courses offered through inter-university exchange; formal enrollment at SCU begins in Year 2, based on completion of the required Year 1 coursework at MNU. SCU enrollment procedures are announced separately. Each institution makes its own final admission decision under its own regulations.
Requirements for Obtaining the Dual Degree
- Complete the graduation requirements of both MNU and SCU, including all required credits at each institution.
- SCU credits are converted at a ratio of 1 SCU credit = 2 ECTS.
- Achieve English proficiency of IELTS 5.5 or higher for SCU graduation.
- A student who completes the requirements of only one institution is awarded the degree of that institution only.
Course Information
| 1st YEAR | ||
|---|---|---|
| No. | Course Title | Credits |
| 1 | AI Programming Fundamentals | 3 |
| 2 | Introduction to Cybersecurity | 3 |
| Total credits for the 1st year | 6 (KOREA) | |
| 2nd YEAR | ||
|---|---|---|
| No. | Course Title | Credits |
| 3 | Introduction to GPT and Generative AI | 3 |
| 4 | Calculus | 3 |
| 5 | Python Data Analysis | 3 |
| 6 | Cloud Computing (Cloud Service Programming) | 3 |
| 7 | Bigdata Analysis and AI Modeling | 3 |
| 8 | Programming Methodology (C++) | 3 |
| 9 | Linear Algebra | 3 |
| 10 | Operating Systems | 3 |
| Total credits for the 2nd year | 24 (KOREA) | |
| 3rd YEAR | ||
|---|---|---|
| No. | Course Title | Credits |
| 11 | Databases | 3 |
| 12 | Introduction to Artificial Intelligence | 3 |
| 13 | Machine Learning | 3 |
| 14 | Probability and Statistics | 3 |
| 15 | Deep Learning | 3 |
| 16 | Intelligent Agent Systems | 3 |
| 17 | CAD | 3 |
| 18 | AI & Product Management | 3 |
| Total credits for the 3rd year | 24 (KOREA) | |
| 4th YEAR | ||
|---|---|---|
| No. | Course Title | Credits |
| 19 | Data & Business Decision | 3 |
| 20 | Optimization Methods | 3 |
| Total credits for the 4th year | 6 (KOREA) | |
Total: 20 courses / 60 credits = 120 ECTS
First Year in SCU
- 1. AI Programming Fundamentals
| Year | 1 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 1 / Fall Semester |
| Course Description | The course aims to equip students with solid programming foundations in Python, object-oriented design principles, and algorithmic thinking. Students will master core programming constructs, work with fundamental data structures, and implement basic algorithms. As a result of the course, students will develop the problem-solving skills essential for AI development and further study in the field. |
- 2. Introduction to Cybersecurity
| Year | 1 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 1 / Spring Semester |
| Course Description | The course aims to provide students with the security knowledge needed to protect information and AI systems. Students will learn the fundamentals of cryptography, network and system security, authentication, and access control, together with the wider landscape of cyber-crime and digital investigation. As a result of the course, students will be able to design and operate trustworthy systems and safeguard data throughout its lifecycle. |
Second Year in SCU
- 3. Introduction to GPT and Generative AI
| Year | 2 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 2 / Fall Semester |
| Course Description | This course introduces the core concepts and practical applications of generative AI. Students examine how large language models and multimodal generative systems are reshaping content creation, develop skills in prompt design and AI-assisted content production, and build working prototypes of AI-powered applications. The course also addresses the ethical, legal, and social implications of the technology, enabling students to evaluate generative AI systems critically and deploy them responsibly. |
- 4. Calculus
| Year | 2 |
|---|---|
| Course Classification | Mathematics Required Courses |
| Recommended Year / Semester | Year 2 / Fall Semester |
| Course Description | The course aims to develop students' mathematical foundation in differential and integral calculus essential for machine learning and AI. Students will master limits, derivatives, integrals, and their applications. As a result of the course, students will understand optimization algorithms, neural-network training, and mathematical modeling in AI systems. |
- 5. Python Data Analysis
| Year | 2 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 2 / Fall Semester |
| Course Description | The course aims to develop practical skills in data analysis using Python. Students will work with core libraries such as NumPy and Pandas together with visualization tools to process, analyze, and present data. As a result of the course, students will be able to carry out exploratory data analysis and prepare data for machine-learning and AI applications. |
- 6. Cloud Computing (Cloud Service Programming)
| Year | 2 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 2 / Fall Semester |
| Course Description | The course aims to provide a practical understanding of cloud computing and modern web-service architecture. Students will master core concepts — IaaS, PaaS, serverless computing, networking, databases, and storage — and use current cloud platforms to build and deploy real web applications, practicing Git-based collaboration, serverless API development, authentication, caching, storage, and monitoring. As a result of the course, students will build a URL-shortener SaaS project step by step and gain hands-on competence in developing and operating modern cloud-based services. |
- 7. Bigdata Analysis and AI Modeling
| Year | 2 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 2 / Spring Semester |
| Course Description | The course aims to develop skills in analyzing large-scale data and building AI models on the results. Building on Python together with NumPy, Pandas, and visualization, students will learn big-data analysis techniques and introductory AI modeling. As a result of the course, students will be able to derive insights from big data and apply them to prediction and decision-making tasks. |
- 8. Programming Methodology (C++)
| Year | 2 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 2 / Spring Semester |
| Course Description | Students will be able to understand and apply the basic syntax of C++ and the concepts of object-oriented programming. Through the step-by-step development of a simulation project, they will also develop practical problem-solving skills focused on program structure and system design, including state management, data structures, functional decomposition, and class design. |
- 9. Linear Algebra
| Year | 2 |
|---|---|
| Course Classification | Mathematics Required Courses |
| Recommended Year / Semester | Year 2 / Spring Semester |
| Course Description | The course aims to develop the linear-algebra foundation underlying machine learning and AI. Students will study matrices, systems of linear equations, vector spaces, linear transformations, eigenvalues, and applied techniques such as orthogonal transformations. As a result of the course, students will be able to apply linear-algebraic methods to model and solve computational problems. |
- 10. Operating Systems
| Year | 2 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 2 / Spring Semester |
| Course Description | The course aims to provide an understanding of operating-system principles essential for building and running AI systems. Students will master processes and threads, concurrency and mutual exclusion, memory management, file systems, and input/output. As a result of the course, students will understand how computer systems operate and how to use OS services efficiently in application development. |
Third Year in SCU
- 11. Databases
| Year | 3 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 3 / Fall Semester |
| Course Description | The course aims to provide comprehensive database knowledge for AI application development. Students will master relational and NoSQL database design, SQL querying, indexing and optimization, transactions, and data modeling. As a result of the course, students will be able to store, retrieve, and manage the large-scale datasets required for training and serving AI models. |
- 12. Introduction to Artificial Intelligence
| Year | 3 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 3 / Fall Semester |
| Course Description | The course aims to provide students with a comprehensive understanding of AI fundamentals, including intelligent agents, search algorithms, knowledge representation, and ethical implications. Students will gain the ability to analyze AI systems critically and understand their societal impact. As a result of the course, students will be able to apply basic AI concepts to solve real-world problems across various domains. |
- 13. Machine Learning
| Year | 3 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 3 / Fall Semester |
| Course Description | The course aims to lay a solid foundation in the theory and practice of machine learning. Students will learn supervised and unsupervised learning algorithms, model evaluation methods, feature engineering, and ML pipeline implementation. As a result of the course, students will gain practical experience in building models and using standard industry libraries for real-world applications. |
- 14. Probability and Statistics
| Year | 3 |
|---|---|
| Course Classification | Mathematics Required Courses |
| Recommended Year / Semester | Year 3 / Fall Semester |
| Course Description | The course aims to build a solid statistical foundation for understanding uncertainty in AI systems. Students will learn probability distributions, hypothesis testing, statistical inference, and Bayesian methods. As a result of the course, students will be able to apply statistical methods to data analysis, model evaluation, and data-driven decision-making in AI applications. |
- 15. Deep Learning
| Year | 3 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 3 / Spring Semester |
| Course Description | The course aims to provide a comprehensive understanding of neural-network architectures and deep-learning techniques. Students will master CNNs, RNNs, transformers, attention mechanisms, backpropagation, and optimization methods, implementing models with modern frameworks such as PyTorch or TensorFlow. As a result of the course, students will be able to apply deep learning to computer vision, NLP, and other domains. |
- 16. Intelligent Agent Systems
| Year | 3 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 3 / Spring Semester |
| Course Description | This course introduces the design, implementation, evaluation, and deployment of LLM-based AI agent systems. Students will learn core concepts such as tool use, agent loops, planning, state and memory management, MCP, multi-agent orchestration, and agent-to-agent communication, while also addressing practical issues including reliability, security, observability, and deployment. Through a semester-long project, students will build an AI agent system capable of using external tools, maintaining state, planning and executing tasks, and evaluating its own results. |
- 17. CAD
| Year | 3 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 3 / Spring Semester |
| Course Description | The course aims to develop computer-aided design (CAD) skills for engineering product design. Students will learn 2D and 3D modeling using CAD software and apply it to system design and practical problem solving. As a result of the course, students will be able to produce and validate design drawings and models for real-world engineering tasks. |
- 18. AI & Product Management
| Year | 3 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 3 / Spring Semester |
| Course Description | The course aims to develop strategic product-management skills for AI solutions. Students will master the product lifecycle, market and user research, roadmap planning, and stakeholder communication. As a result of the course, students will be able to identify AI product opportunities, define requirements, prioritize features, and bring AI products to market. |
Fourth Year in SCU
- 19. Data & Business Decision
| Year | 4 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 4 / Fall Semester |
| Course Description | The course aims to develop skills in using data and analytics to inform business decisions. Students will learn to frame business problems, work with relevant data, apply analytical and statistical reasoning, and communicate actionable insights. As a result of the course, students will be able to support strategy and operational decisions under uncertainty with data-driven recommendations. |
- 20. Optimization Methods
| Year | 4 |
|---|---|
| Course Classification | AI Track Courses |
| Recommended Year / Semester | Year 4 / Spring Semester |
| Course Description | The course aims to teach optimization methods that are critical for training and deploying AI models. Students will learn gradient-descent variants, convex optimization, evolutionary algorithms, and metaheuristics. As a result of the course, students will be able to apply these methods to hyperparameter tuning, model selection, and complex optimization problems in AI systems. |
Faculty Information
| No. | Name | Major | Contact |
|---|---|---|---|
| 1 |
Kim, Sanggyun (김상균) Dept. Chair · Deputy Director, Bigdata·AI Center |
Ph.D. in Electrical and Electronics Engineering, KAIST | +82-2-944-5548 sgkim@iscu.ac.kr |
| 2 |
Chun, Ji Young (천지영) Vice President |
Ph.D. in Information Management Engineering, Korea University | +82-2-944-5543 jychun@iscu.ac.kr |
| 3 |
Noh, Geontae (노건태) |
Ph.D. in Information Security, Korea University | +82-2-944-5542 gnoh@iscu.ac.kr |
| 4 |
Lee, Seung-Man (이성만) Director, Bigdata·AI Center |
M.S. in AI & Bigdata, Kookmin University | +82-2-944-5547 trex99@iscu.ac.kr |
| 5 |
Kim, Howard (김환) |
Ph.D. in Information & Communication Media Engineering, SeoulTech | +82-2-944-5742 howardkim@iscu.ac.kr |
| 6 |
Shin, Young Ah (신영아) |
Ph.D. in Information Security, Korea University | +82-2-944-5544 youngah2026@iscu.ac.kr |
| 7 |
Lee, Jongwon (이종원) |
Ph.D. in Electrical and Computer Engineering, Purdue University | +82-2-944-5112 jlee@iscu.ac.kr |
| 8 |
Won, Jong-seo (원종서) |
Ph.D. in Business Administration, Yonsei University | +82-2-944-5767 jongseo.won@iscu.ac.kr |
| 9 |
Yoon, Jaehyun (윤재현) |
M.S. in Mechanical Engineering, Gyeongsang National University | +82-2-944-5744 yoonjh@iscu.ac.kr |
*KAIST = Korea Advanced Institute of Science and Technology
