M.Sc. in Artificial Intelligence (AI)
AUK is launching M.Sc. in Artificial Intelligence (AI). The program aims to provide students with the knowledge, skills, and ethical understanding necessary to lead in the rapidly evolving field of AI. The program prepares students to address complex challenges and develop solutions that positively impact society by promoting research, innovation, and collaboration with industry. The M.Sc. in AI will be supported by state-of-the-art facilities, including a newly established AI lab at AUK.
For admission, students must have a bachelor’s diploma in a related field or demonstrate proficiency in the following before beginning the program: Programming, Mathematics, Data Structures and Algorithms, and Introductory AI/Machine Learning. The attainment of proficiency may have been through previous coursework or industry/professional experience.
Also, all candidates will need to go through the assessment process. During the assessment of candidates for admission, the Department Chair and Dean will review the bachelor’s transcripts to ensure the expected competencies and disciplinary knowledge needed to begin the program’s coursework (prerequisites) are satisfied.
emphasizing transformative knowledge, innovative research, and ethical community service. The University prioritizes inclusion, diversity, global connectivity, and lifelong learning to foster economic, environmental, social, and political progress at regional, national, and global levels. Following its success in offering undergraduate programs across the four colleges of Business, Arts and Sciences, Nursing, and Engineering, AUK has continued to identify opportunities to meet the needs of its constituents in advancing its offerings in higher education, meeting the needs of the regional workforce, and fulfilling its institutional mission. In response to local and regional challenges, AUK’s program will build upon the institution’s current expertise in both Computer Science and Engineering. A recent market study conducted in the Kurdistan Region by Business Insight, Erbil, highlighted a pressing need for experts with advanced technical knowledge and innovative problem-solving capabilities. “Skilled workers capable of utilizing advanced technology and software such as predictive analytics, artificial intelligence, data visualization tools, and digital marketing techniques are in high demand.” The Information and Communications Technology (ICT) sector is quickly becoming a vibrant and promising industry. The KRG’s dedication to promoting the ICT sector as the main driver of economic expansion is enabling the region to evolve into a center for technology entrepreneurship. The technology sector, including areas like artificial intelligence, data analysis, cybersecurity, and digital marketing, is identified as a significant driver of demand for skilled workers.The shift towards digitization and online platforms has intensified the need for tech-savvy professionals who can navigate the evolving digital landscape.”
In response to this demand, this proposal will outline establishing a Master of Science (M.Sc.) program in AI at AUK. This program aims to equip students with the essential skills and knowledge to excel in the evolving digital economy.
- 1.Provide students with a solid theoretical and practical foundation in AI, including machine learning, deep learning, natural language processing, and computer vision.
- 2.Foster innovative thinking and the ability to apply AI techniques to real-world problems.
- 3.Develop students’ abilities to conduct original research in AI, contribute to academic and industry advancements, and publish their findings.
- 4.Prepare graduates for AI-driven industries, research institutions, and academic leadership roles.
- 5.Promote ethical AI development and ensure students understand the societal implications of AI technologies.
- Certified copies of the bachelor’s degree diploma, transcript, and certified high school grades. For students who have graduated from international institutions, a bachelor’s degree equivalency is mandatory.
- A minimum GPA of 2.50 out of 4.00 or 60% in the bachelor’s degree.
- Proficiency in English language evidenced by a valid TOEFL or IELTS score or a bachelor’s degree from a university whose instruction is exclusively in English. The AUK requires a minimum TOEFL ITP score of 537+, TOEFL iBT score of 75+ or IELTS 6 (valid for two years).
- Successful completion of an interview.
- Preferred: Relevant professional experience and evidence of research/ product development.
- Required Knowledge: Proficiency in programming languages commonly used in AI, such as Python, C++, or Java. Courses in Python Programming or Object-Oriented Programming are recommended.
- AUK Courses:Introduction to Programming or Object-Oriented Programming.
- Required Knowledge: Knowledge in Linear Algebra, Calculus, Probability, and Statistics. Courses equivalent to Linear Algebra, Calculus I & II, and Statistics are required to ensure mathematical readiness for AI and machine learning concepts.
- AUK Courses: Linear Algebra, Calculus I, and Probability and Statistics.
- Required Knowledge:Understanding of fundamental data structures (such as arrays, stacks, queues, and trees) and algorithm design. Prior coursework in Data Structures and Algorithms is essential.
- AUK Courses:Data Structure and Algorithm
- Although not mandatory, prior exposure to AI or Machine Learning basics is beneficial for incoming students. This could include coursework or hands-on experience in
Introduction to AI or Machine Learning Fundamentals.
Mission The M.Sc. in AI aims to provide students with the knowledge, skills, and ethical understanding necessary to lead in the rapidly evolving field of AI. The program prepares students to address complex challenges and develop solutions that positively impact society by promoting research, innovation, and collaboration with industry.
Program Goals The primary goal of the master’s program is to produce highly skilled professionals capable of addressing current and future challenges in the field through an interdisciplinary approach that combines technical knowledge, research experience, and ethical considerations.
Vision In keeping with AUK’s vision to be recognized as a premier institution in the Middle East, the M.Sc. in AI strives to be a regional and global leader in technology-related education.
Program Goals The primary goal of the master’s program is to produce highly skilled professionals capable of addressing current and future challenges in the field through an interdisciplinary approach that combines technical knowledge, research experience, and ethical considerations.
Program Objectives
Facilities and Resources The M.Sc. in AI will be supported by state-of-the-art facilities, including a newly established AI lab. This lab will provide students with direct access to high-performance computing resources, cutting-edge software, and specialized AI hardware. These resources are essential for advanced experimentation and research in AI, equipping students to engage in real-world applications and innovative projects. The AI lab is designed to foster collaboration on applied research, enabling students to develop, test, and refine AI models in a hands-on environment that aligns with industry standards.
Admission and Graduation requirements For admission, students must have a bachelor’s diploma in a related field or demonstrate proficiency in the following before beginning the program: Programming, Mathematics, Data Structures and Algorithms, and Introductory AI/Machine Learning. The attainment of proficiency may have been through previous coursework or industry/professional experience. Admission requirements include:
Meeting Program Prerequisites During the assessment of candidates for admission, the Department Chair and Dean will review the bachelor’s transcripts to ensure the expected competencies and disciplinary knowledge needed to begin the program’s coursework (prerequisites) are satisfied. If from industry, candidates will be obliged to provide a letter of reference from their employer as proof of experience. Candidates may be required to take an assessment to evaluate their prior learning. If a candidate is determined to be deficient in any of the requisite skills/knowledge, then s/he will be required to complete specified AUK undergraduate courses to attain such skills/knowledge. These students will be accepted conditionally; a contract will specify the courses and minimum grade thresholds. Students not satisfying these conditions will not move onto the master’s courses. Undergraduate courses taken under a conditional contract will not count toward the master’s degree requirements and credit count.
Prerequisite Courses or Knowledge Areas Candidates must demonstrate proficiency in the following areas prior to registering for their
master’s courses. Students who lack any of these prerequisites will be required to complete relevant undergraduate courses at AUK.
Programming Skills:
Mathematics:
Data Structures and Algorithms:
Introduction to Artificial Intelligence or Machine Learning (Recommended, not Required):
Course Waiver Students will be registered for courses during the designated registration period. Due to the cohort structure of the program, they are not permitted to add, drop, or change courses unless approved by the Department Chair. Any change shall result in the design of a new individualized study plan. This may involve a change in date of degree completion. Students must enroll in 9 credits during the fall and spring semesters, with a maximum of 6 credits during the summer term and second fall semester. The exact number of credits must follow the requirements outlined in the student’s approved study plan. Students with a CGPA below 3.0 are limited to enrolling in 6 credits during the next semester; exceptions can only be approved by the Department Chair.
Full-time load for a master’s student is 6 credits or above. Part-time load for a master’s student
is fewer than 6 credits per semester/term.
Course Registration Students who have mastered the knowledge/skills in an above area through previous coursework (graduate or undergraduate) and those who have industry experience may petition to have the requirement waived. Unlike transfer credit, waivers do not reduce the total number of courses required for the degree and must be replaced.
Course Withdrawal Students are expected to complete the courses for which they are registered unless exceptional circumstances necessitate withdrawal. A student seeking to withdraw from a graduate course must obtain approval from both the course instructor and the Department Chair, providing the rationale for the withdrawal. The Department Chair will discuss with the student all implications of the withdrawal on degree completion and re-entry to the University.
Course withdrawals are permitted until the end of the twelfth week of the semester. A grade of “W” will be recorded on the student’s transcript and will not affect the CGPA. Students are
responsible for any additional costs associated with repeating a course due to withdrawal or failure. Additionally, no tuition refund will be provided for withdrawn courses.
Attendance Since courses only meet once per week, students are expected to attend classes in order to keep
pace with the learning process. As weekday classes will be conducted in a hybrid modality,
students who cannot physically travel to campus will be able to attend online. Recordings of the
class sessions will also be archived for later viewing.
Academic Standing / Probation and Dismissal Students are required to maintain a minimum CGPA of 3.0/4.0 to remain in good academic standing. Graduate students who receive an “F” in any semester or term will be subject to academic dismissal from the University. If a graduate student’s CGPA falls below 3.0 in a semester, they will be placed on probation and remain on probation until the CGPA reaches/exceeds the 3.00 threshold. A student only has two semesters to reach the 3.00 threshold. Failure to achieve a CGPA of 3.0 or higher by the end of the probationary semester will result in academic dismissal. While on probation, students are limited to a maximum of 6 credit hours in the regular semester and 6 credits in the summer term.
Leave of Absence In certain cases, students may decide to take a leave of absence from the University. A leave of absence can be granted for one semester with the approval of the Dean. If a student requires more than one semester leave (12 months or longer), s/he must apply for readmission. It is important to note that course availability upon reentry is not guaranteed. As the program follows a cohort system, the student will join a later cohort.
Transfer Credit for Graduate Programs Applicants may request a review of transfer credits at the time of application. A maximum of 6 credits, with a grade of “B” or higher, may be transferred from accredited/licensed universities recognized by the Ministry of Higher Education & Scientific Research of the KRI, and is subject to the approval of the Department Chair and Dean. Credits for graduation projects or thesis courses cannot be transferred. The content of the transferred courses must match at least 70% of the corresponding courses in the AUK master’s program. While grades from transferred courses do not impact the CGPA, the credits earned will count toward cumulative credits and
graduation requirements.
Course Repeat Graduate students may repeat up to two courses in which they received a grade lower than “B,” with approval from the Department Chair. The higher grade from the repeated course will be used for CGPA calculation, while the lower grade will remain on the student’s transcript but will no longer be calculated within the CGPA. A student may repeat each course only once. Courses for which grades of “C-,” “D,” or “F” are received, must be repeated. Courses for which grades below “B” are received may be repeated, but only with the approval of the Department Chair
and Dean.
Incomplete Grade Removal Students must complete all incomplete coursework by the end of the following semester, excluding the summer term. The instructor must submit the final grade for the incomplete course by the grading deadline of the following semester. If no grade is submitted and the instructor provides no written request for an extension, then the “I” grade will automatically be changed to an “F”.
GRADUATE GRADING SYSTEM
Letter Grade | Numerical Grade | Grade Points |
---|---|---|
A | 90-100 | 4.00 |
A- | 87-89 | 3.67 |
B+ | 84.86 | 3.33 |
B | 80-83 | 3.00 |
B- | 77-79 | 2.67 |
C+ | 74-76 | 2.33 |
C | 70-73 | 2.00 |
C- | 67-69 | 1.67 |
D | 60-66 | 1.00 |
F | 0-59 | 0 |
I | Incomplete – No effect on GPA | |
IP | Incomplete – No effect on GPA | |
W | Incomplete – No effect on GPA | |
TR | Incomplete – No effect on GPA | |
P | Incomplete – No effect on GPA |
- Advanced Database Systems:Explores advanced database topics, including distributed systems, NoSQL, and data warehousing. Students will focus on query optimization, transaction management, and the architecture of modern databases in large-scale applications. This course will feature a combination of lectures, case studies, and hands on labs emphasizing database design, management, and optimization.
- Advanced Human-Computer Interaction (HCI):Focus on advanced interface design, usability testing, and the cognitive aspects of user experience. This course will combine
lectures with project-based learning, covering key HCI concepts like user-centered design, usability testing, and interaction techniques. Through hands-on projects,
students will apply these principles to design and evaluate interfaces, gaining practical skills to create intuitive, user-friendly systems. - Algorithm Analysis and Design: Covering essential algorithms and their analysis, including dynamic programming, graph algorithms, and NP-completeness. This course
will combine lectures and assignments to build a solid foundation in designing efficient algorithms. Lectures will cover essential topics such as algorithm complexity, dynamic programming, and graph algorithms, while assignments will allow students to apply these concepts by solving challenging problems and analyzing algorithm performance. - Artificial Intelligence: Introduction to AI’s fundamental concepts and techniques. Topics include intelligent agents, problem-solving, search algorithms, and knowledge representation. Emphasis is placed on building AI systems and understanding their underlying principles. This course will offer interactive lectures with practical sessions covering fundamental concepts and applications of AI technologies.
- Blockchain Technology & Cryptocurrencies: Introduction to blockchain technology, cryptographic foundations, and cryptocurrencies like Bitcoin. Students will explore
consensus algorithms, smart contracts, and real-world blockchain applications across various industries. This course will combine lectures and hands-on assignments to give students a thorough understanding of blockchain fundamentals and cryptocurrency applications. - Computer Vision: Fundamental techniques in computer vision, such as image processing, object recognition, and 3D reconstruction. Students will explore applications in fields like facial recognition and autonomous systems, with practical implementation using contemporary vision libraries. The Computer Vision course will combine lectures and lab sessions to provide students with a well-rounded learning experience.
- Data Analytics and Visualization: Focus on the methods and tools used to analyze complex data and communicate insights visually. Students will learn key data analytics techniques, including data cleaning, transformation, statistical analysis, and visualization principles that enhance data interpretation. Discussion of data analytics concepts and conduct practical data visualization projects during lab sessions.
- Data Mining: Introduction to key concepts in data mining, including classification, clustering, association rules, and anomaly detection. Students will learn to apply data
mining techniques to large datasets, focusing on real-world applications and hands-on practice with modern tools. This course will include lectures and hands-on lab sessions for data analysis and tool usage. - Deep Learning and Neural Networks: Focus on modern deep learning architectures, the design and training of deep neural networks. Topics include backpropagation, convolutional neural networks (CNNs), and sequence models like LSTMs and transformers. Along with theoretical lectures, this course will feature in-person coding sessions, enabling students to implement and experiment with deep learning models.
- Ethics and AI Policy: Exploration of the ethical challenges presented by AI technologies, such as algorithmic bias, data privacy, job displacement, and decision-making transparency. Students will study case studies and develop strategies for responsible AI development with the aim of exploring the ethical, legal, and societal implications of AI.
- Data Mining: Introduction to key concepts in data mining, including classification, clustering, association rules, and anomaly detection. Students will learn to apply data
mining techniques to large datasets, focusing on real-world applications and hands-on practice with modern tools. This course will include lectures and hands-on lab sessions for data analysis and tool usage. - Machine Learning: An in-depth exploration of machine learning algorithms, covering linear regression, decision trees, neural networks, support vector machines, and clustering. Students will learn to apply these techniques to real-world datasets and evaluate their performance. The teaching modality for this course will include lectures with in-person labs to practically implement machine learning algorithms and techniques.
- Natural Language Processing: Techniques for processing and analyzing natural language data, with applications in speech recognition, machine translation, and sentiment analysis. Topics include language models, tokenization, parsing, and word embeddings. course will blend lectures and lab sessions to equip students with both theoretical understanding and practical skills.
- Python Programming for Artificial Intelligence: Focus on using Python to implement AI techniques like machine learning and natural language processing. Through lectures
and practical AI projects, students will gain hands-on experience with libraries such as TensorFlow and Scikit-learn. -
AI for Healthcare and Bioinformatics: Applying AI techniques to healthcare and medical data. Projects could involve disease prediction models, diagnostic tools, and data analytics for healthcare data.
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Natural Language Processing Projects: Hands-on projects in NLP, including tasks like sentiment analysis, machine translation, chatbots, and language generation.
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AI in Finance and Business Analytics: Application of AI in financial modeling, stock prediction, credit scoring, and data-driven decision-making processes.
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Ethics and Responsible AI Development: Projects that explore the ethical implications of AI applications, addressing fairness, accountability, transparency, and privacy.
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Human-Computer Interaction and User Experience Design: Hands-on projects that explore the principles and methodologies of Human-Computer Interaction, focusing on designing intuitive and user-friendly interfaces for AI applications.
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Visual Analytics Systems with AI Integration: Design of visual analytics systems that use AI to enhance data exploration and decision-making. Students will learn to integrate
AI techniques, like machine learning and natural language processing, with advanced visualization methods to create interactive tools for complex data analysis. - Identify a relevant AI-related research question, challenge, or problem to address.
- Conduct a comprehensive review of existing literature and methodologies.
- Utilize AI methodologies to design, develop, and evaluate solutions or models.
- Document findings in a formal thesis or capstone report that meets academic standards in AI research.
Program Structure
Fall Semester
Course Name | Number of Credits |
---|---|
Machine Learning | 3 |
Data Mining | 3 |
Elective 1 | 3 |
Spring Semester
Course Name | Number of Credits |
---|---|
Artificial Intelligence | 3 |
Deep Learning and Neural Networks | 3 |
Elective 2 | 3 |
Summer Term
Summer Seminar | Number of Credits |
---|---|
Project-Based Seminar | 6 |
Fall Semester
Course Name | Number of Credits |
---|---|
Thesis/Capstone | 6 |
Electives
Course Name | Number of Credits |
---|---|
Advanced Human-Computer Interaction | 3 |
Algorithm Analysis and Design | 3 |
Data Analytics and Visualization | 3 |
Advanced Database Systems | 3 |
Blockchain Technology & Cryptocurrencies | 3 |
Python Programming for Artificial Intelligence | 3 |
Ethics and AI Policy | 3 |
Computer Vision | 3 |
Natural Language Processing | 3 |
Course Descriptions
Project-Based Courses
In the summer term, students will engage in project-based learning emphasizing real-world applications; they will work collaboratively in groups on industry-oriented projects in areas such as healthcare, education, banking, and technology. Through group presentations and feedback sessions, they will refine their projects and learn to effectively communicate (and pitch) their solutions to a jury. Possible topics include:
Thesis/Capstone Project
The thesis/capstone project is the culminating academic experience in the master’s program. It allows students to apply their knowledge to solve real-world problems, contribute original
insights to the field, or engage in research that aligns with industry or societal needs. Students will be expected to:
Project Map
1. Topic Selection: Students will select a project topic in collaboration with faculty before the final fall semester begins. Projects may be aligned with industry needs, ongoing faculty research, or societal challenges, leveraging the resources of the AI lab where applicable.
2. Proposal Submission and Approval: A formal project proposal must be submitted, outlining objectives, methodology, expected outcomes, and any anticipated challenges. A faculty committee will review and approve each proposal, ensuring alignment with program goals and resource feasibility.
3. Supervision and Mentorship: Students will be assigned a faculty advisor who will provide guidance throughout the research and project phases, offering feedback on methodology, design, and analysis. Regular check-ins will be required, with milestones set for progress tracking.
4. Project Execution and AI Lab Utilization: Students will conduct their research and project development in the AI lab, using its model-building, testing, and data analysis resources. Students will follow rigorous research protocols for thesis projects, including data collection, experimentation, and result evaluation.
5. Thesis/Capstone Report and Defense: Students will submit a final report or thesis upon project completion, presenting their findings, methodology, analysis, and conclusions. Each student must then defend their work before a thesis defense committee consisting of their project supervisor, two additional faculty members, and an external expert in the field of AI.
6. Assessment Criteria: The project will be evaluated based on originality, technical rigor, application of AI concepts, analytical depth, and the final presentation and report quality. Successful completion of the thesis/capstone project is required for graduation, fulfilling a core program outcome.
Design Models
The MSc. in AI was designed with a comprehensive approach that combines global best practices with regional market needs. The Department of CSIT analyzed AI programs from three universities in the U.S. focusing on their curriculum structures, core and elective courses, and alignment with industry demands. Research identified the M.Sc. in AI offered by the University of Houston Downtown, which has been recognized by AI Degree Guide as a “Top Pick of the Very Best Master’s Programs in Artificial Intelligence.” To ensure that the program meets the unique requirements of the local market, we collaborated with industry leaders, academic experts, and governmental representatives. This collaboration highlighted the growing demand for AI professionals to drive digital transformation in the Kurdistan Region and Iraq. As a result, the program was tailored to emphasize applied research and domain-specific AI applications, equipping graduates with the skills needed to address regional challenges while contributing to the global AI landscape. The average duration to complete a M.Sc. in Artificial Intelligence in the United States is typically 1.5 to 2 years of full-time study. Programs with a thesis requirement may take slightly longer due to the time needed for research and writing. Non-thesis or project-based programs can often be completed more quickly.
Course Category | AUK | Kent State | Michigan | Houston |
---|---|---|---|---|
Foundation Courses 12-18 credits |
• Machine Learning • Data Mining • Artificial Intelligence • Deep Learning and Neural Networks |
• Information Visualization • Artificial Intelligence • Machine Learning and Deep Learning • Advanced Database Systems Design • Advanced Artificial Intelligence |
• Artificial Intelligence • Intelligent Systems • Algorithm Analysis and Design • Software Engineering |
• Data Mining • Introduction to Artificial Intelligence • Deep Learning • Natural Language Processing • Computer Vision • Advanced Artificial Intelligence |
Elective, Concentration, Project-Based Courses 9-12 credits |
Students take 4 • Advanced HumanComputer Interaction • Algorithm Analysis and Design • Data Analytics and Visualization • Advanced Database Systems • Blockchain Technology & Cryptocurrencies • Python Programming for Artificial Intelligence • Ethics and AI Policy • Computer Vision • Natural language processing |
Students take 3 • Big Data Analytics • Data Mining • Big Data Management • Software Development for Robotics • Algorithmic Robotics • Internet of Things • Advanced Digital Design • Human-Robot Interaction • Probabilistic Data Management • Computational Health Informatics • Embedded Computing • Image Processing • Multimedia Systems and Biometrics • Wireless and Mobile Communication Networks • Scientific Visualization |
Students take 3 or 4 based on Concentration Computer Vision • Advanced Computer Graphics • Information Visualization • Pattern Recognition • Digital Image Processing Machine Learning • Introduction to Natural Language Processing • Deep Learning • Artificial Neural Networks Knowledge Management and Reasoning • Data Mining • Advanced Data Management • Foundation of Information Security Intelligent Interaction • Human-Computer Interaction • Wireless Sensor Networks and IoT • Computer Game Design and Implementation |
Students take 3 • Cognitive Systems • AI in Biomedical • AI in Business Intelligence • Intelligent Interactive Systems • Robotics Process Automation • Ethics in Artificial Intelligence |
Capstone 3-6 credits |
Project or Thesis | Project or Thesis | Project or Thesis | Project |
Credit Totals | 30 | 30 | 30 | 30 |
The proposed M.Sc. will allow students to delve deeply into cutting-edge fields, integrating theoretical knowledge with practical experience. This advanced education will prepare
graduates for senior roles in industry and establish a solid foundation for those interested in pursuing doctoral studies. This focus on innovation and scholarship will benefit faculty and students alike, while also contributing to advancements in the broader scientific community and industry.
A recent market study highlighted opportunities in sectors such as information technology, renewable energy, and engineering projects. It emphasized the importance of addressing education and skills gaps, promoting entrepreneurship, and fostering a supportive business environment. International collaborations, technological advancements, and sustainable development initiatives are vital for creating a more robust and diversified labor market, which will, in turn, support Kurdistan and Iraq’s economic growth and stability. Ongoing efforts in these areas will be crucial for developing a resilient and dynamic labor market in the future.
Tabulated responses from key informant interviews and surveys indicate a strong demand for essential skills in digital literacy and technology integration. Participants recognize the growing
importance of these skills over the next five years. To address the needs of emerging trends in artificial intelligence, technology, and financial technology, universities should introduce
relevant courses and programs.
By incorporating practical projects, industry partnerships, and experiential learning opportunities into the curriculum, universities can provide students with valuable hands-on experience and real-world applications of their studies. It is crucial for universities to stay current with industry advancements and evolving skill requirements to ensure their programs remain relevant. By equipping students with digital skills training, soft skills development, and practical experience, universities can significantly contribute to meeting the increasing demand
for talent in these dynamic sectors. Updating curricula to align with emerging market trends and bridging skills gaps will better prepare students for success in the evolving job market.
The M.Sc. in AI specifically addresses these concerns. The technology sector, which includes artificial intelligence, data analysis, cybersecurity, and digital marketing, is a major driver of demand for skilled graduates. The shift towards digitization and online platforms has heightened the need for tech-savvy professionals who can effectively navigate the changing
digital landscape.
A study commissioned by AUK, titled “A Strategic Analysis of Public Policy, Labor Market Trends, and High School Students’ Interest for Enhancing Academic Offerings,” published by Business Insight in June 2024, highlights the need for academic programs in the Kurdistan Region of Iraq for IT professionals as businesses across various sectors undergo digital transformation. This transformation necessitates expertise in cybersecurity, cloud computing, and data analytics. Skilled graduates are particularly sought after in fields such as financial technology, data analytics, cybersecurity, and regulatory compliance. Notably, 76% of student respondents expressed an interest in scientific disciplines, including Engineering, Computer Science, and Math/Data Science.
Key findings include:
- Technology and IT Demand: The technology sector—encompassing artificial intelligence, data analysis, cybersecurity, and digital marketing—emerges as a major driver of demand for skilled professionals. The ongoing shift toward digitization has heightened the need for professionals adept in navigating the evolving digital landscape.
- Advanced Technology Skills: There is a notable demand for advanced skills in areas such as AI and Machine Learning, with 421 respondents expressing interest in these fields, along with advanced IT skills like cloud computing and cybersecurity.
For universities designing their academic programs, this data underscores the importance of prioritizing curriculum enhancements and skill development initiatives focused on emerging technologies, digital literacy, and entrepreneurship. Integrating hands-on projects, industry collaborations, and specialized courses in areas such as AI, cybersecurity, and digital marketing will better equip students for the demands of the modern workforce, enhancing their career prospects and job security.
The study recommends the introduction of new programs in Artificial Intelligence, Data Science, and Cybersecurity. The authors conclude by advocating for the establishment of a Center for Artificial Intelligence at AUK, emphasizing its potential to support diverse solutions across scientific, business, and societal challenges.
The uniqueness or distinctiveness of the program is clear. This will be the first master’s degree in AI in the Duhok Governorate. The University of Kurdistan Hewler, in Erbil, currently offers a master’s degree in AI, which is designed from the engineering perspective. AUK’s proposed master’s is designed from the IT perspective. To meet the needs of those recently graduated
from bachelor’s programs or industry professionals looking to upskill their talents, AUK will offer the program with access in mind, meaning it will be delivered two evenings per week(hybrid model) and all-day Saturday. The hybrid mode will accommodate working professionals, those with families who cannot travel to campus. Travel to campus will only be required on Saturdays.
The College of Arts and Sciences, specifically the Computer Science and Information Technology Department, is currently equipped with a faculty whose expertise and qualifications are adequate to support the launch of the new master’s program. While the existing faculty are wellprepared to meet the educational and research needs of this program, there will be a need to hire an additional full-time faculty member. The qualifications of the new position will be tailored to bring knowledge that leans more towards Artificial Intelligence and related fields, and will share FTE with the Bachelor’s in Computer Software and Security with his/her teaching load being equally distributed between the Master’s and Bachelor’s program in the first year. This arrangement ensures efficient utilization of departmental resources. Additionally, adjunct faculty will be used to complement the efforts of full-time faculty and maintain the program’s industry relevance.
Faculty Profile
- Shamal Taha, Ph.D. in Computer Science/Data Visualization, Kent State University. Chair/Assistant Professor, College of Arts and Sciences, Computer Science and Information Technology Department.
- Omar Abdulghafoor, Ph.D. in Electrical, Electronic and System Engineering, University of Kebangsaan. Chair/Associate Professor, College of Engineering, Electronic and Telecommunication Engineering Department.
- Ali Shinwari, Lecturer, M.CS in Information Technology at Kabul University. College of Arts and Sciences, Computer Science and Information Technology Department.
- Dara Sherwani, Ph.D. In Human-Computer Interaction, University of London. Assistant Professor, College of Arts and Sciences, Computer Science and Information Technology Department.
- Prathap Mani, Ph.D. in Computer Science, Bharathiar University. Assistant Professor College of Arts and Sciences, Computer Science and Information Technology Department.
The existing administrative structure is adequately prepared to support the introduction of the M.Sc. in AI. The College’s administrative staff who manage student records, facilitate enrollment, and provide general support, are well positioned to meet the program’s needs without additional hires. Furthermore, the Admissions and Registration Department includes two Student Recruiters whose efforts will include supporting the new program’s student recruitment initiatives.
At the launch of the new M.SC. in AI, there will be a need to hire a teaching assistant to provide support in the labs and to be present in the classroom during hybrid instruction and in the lab to monitor usage of the hardware and software. The current support team can manage the additional responsibilities associated with the new program, covering areas such as instructional design, helpdesk support, tutoring services, and library resource access. If there is a significant increase in student enrolment in the future, support staffing will be addressed, ensuring that support capabilities scale appropriately with program growth.
The AUK Library provides comprehensive resources for students, faculty, alumni, and associate members. Since AUK’s Library became a member of AMICAL in 2022—a consortium supporting American international liberal arts institutions—the Library has enhanced its resources to support learning, teaching, and research through collaborative library and information services. The Library’s collection comprises over 40,000 physical items and more than 500,000 electronic resources in fields such as Science, Technology, Finance, Business, and Management. It includes a diverse range of materials such as eBooks, audiobooks, journals, magazines, reports, and 3D models.
Key electronic subscriptions include ProQuest eBook Central, Elsevier (Clinical Key Student and Complete Anatomy for nursing students), Research4Life, IFLA Library and Sage Journal. These resources are accessible both on and off-campus, ensuring access for all users.
The recent acquisition of OCLC WorldShare Management System (WMS) resources will significantly enhance AUK’s academic and research capabilities by providing access to a global network of information, fostering innovation, and enriching the learning environment. This acquisition will improve learning and research opportunities, streamline library operations through data-driven decision-making, and enhance the user experience. Through WorldCat, will be granted access to Collective Catalogue (550 million records, 3.3 billion holdings) Knowledge Base (+24,7K content collections) 108M open access items.
Additionally, the Library provides a Reservation System and Library Management System, allowing patrons to book private study rooms and schedule tutoring sessions, as well as borrow, locate, and return books electronically. All systems are accessible anywhere, anytime, facilitating a more dynamic and supportive learning environment. These resources are complemented by ongoing library renovations, integrating technology to enhance the user experience.
AUK has state of the art facilities and infrastructure to support a master’s program. Given that the proposed delivery is during non-traditional hours, classrooms and access to computer labs is facilitated. Minimum staffing additions would need to be addressed in security, food service, and IT support for these non-traditional hours.
AUK will provide master’s students with comprehensive technological resources to support their academic careers. Through the online self-service account, students can view their course schedules, transcripts and degree audits, and class absence percentages. Faculty will use the online self-service account to view class rosters, record and track absences, and access student transcripts and audit sheets. Additionally, faculty will utilize Blackboard for course management and communication, as well as the Beacon system to identify and support students with personal or academic challenges.
Instructional support for hybrid courses will continue to be provided with the IVN classrooms, which are specially designed with advanced video conferencing capabilities to facilitate real time, interactive learning across multiple locations. These classrooms enable instructors and students to engage as if they were in the same physical space, with live audio, video, and digital content sharing.
IVN Classrooms offer:
- High-definition video lectures for clear visuals and audio quality.
- Dual or multiple screens to simultaneously display remote participants and
instructional materials. - Strategically placed microphones and speakers to ensure clear communication across
locations. - Interactive tools like digital whiteboards and screen sharing, which enable collaborative
activities. - Recording capabilities to capture sessions for later review or asynchronous learning
through Microsoft Teams.