Bachelor of Science in Artificial Intelligence

BScUndergraduateComputer Science

About This Programme

The Bachelor of Science in Artificial Intelligence (BSc in AI) is a four-year undergraduate program designed to provide students with a strong and coherent foundation in artificial intelligence, computing, and data-driven technologies. The program integrates core knowledge in computer science, mathematics, probability, and statistics with specialized AI areas such as machine learning, deep learning, natural language processing, computer vision, intelligent systems, and ethical AI. The curriculum is structured to progressively develop students’ competencies from foundational programming and mathematical skills in the first two years to advanced AI methodologies and applications in the third and fourth years. The program includes practical laboratory components, project-based learning, CO-OP training, and junior and senior AI projects to ensure that students gain hands-on experience in designing, implementing, and evaluating AI-driven solutions. Emphasis is placed not only on technical proficiency but also on ethical responsibility, professional conduct, teamwork, and effective communication. Students are trained to analyze complex problems, work with real-world datasets, and apply appropriate AI models and tools to generate meaningful insights and intelligent applications. The inclusion of AI Ethics, Information Security, and responsible AI practices ensures alignment with national and international expectations for trustworthy and human-centered AI systems. The program is delivered primarily through face-to-face instruction supported by laboratories, collaborative learning, case studies, and applied projects. Modern software tools, programming environments, and computing platforms are integrated into coursework to reflect current industry practices. Graduates of the program are prepared for entry-level roles such as AI engineer, machine learning practitioner, data analyst, intelligent systems developer, and AI applications specialist across sectors including healthcare, smart cities, finance, cybersecurity, logistics, robotics, and government services. The program also provides a solid foundation for postgraduate studies and research in artificial intelligence and related computing disciplines. Overall, the BSc in Artificial Intelligence strengthens the academic portfolio of the College of Computing and Informatics and supports the University’s strategic commitment to innovation, digital transformation, and the development of highly skilled graduates capable of contributing to the UAE’s knowledge-based economy.

Course Highlights

123 credit hours
Taught in English
College of Computing and Informatics
CO-OP practical training
Study system: Courses
Full-time study

What You'll Study

Degree requirements (programme page): • Program requirements: compulsory courses (66 credit hours) and elective courses (15 credit hours) • Support requirements: compulsory courses (15 credit hours) and elective courses (3 credit hours) • University Requirements: 24 credit hours These add up to 123 credit hours, the total of the study plan; the page’s summary gives 120 credit hours. University Requirements (programme page): • All students in all programs offered by the University must study 24 credit hours. • The Compulsory and Electives courses are specified as follows. General Education Areas The program requires students to take 24 credit hours, 15 of which are compulsory and 9 are electives. Eight domains are covered: First: Compulsory Domains A. Islamic Studies B. Arabic Language C. English Language D. UAE Studies E. Information Technology Second: Elective Domains F. Humanities, Social and Arts G. Sciences and Technology H. Life Skills and Personal Development In the study plan the compulsory university courses are 0201102 Arabic Language, 0202112 English for Academic Purposes, 0204102 UAE Society, 0104100 Islamic Culture and 1501100 AI and Digital Technologies, with three University Requirement electives. Study plan (study plan document): eight semesters and a summer session (Practical Training II in AI), 123 credit hours.

Program requirements: compulsory courses (66 credit hours)

Twenty-three courses.

1501116Programming I4 credits (3-2:4)
This course covers introductory concepts in computer programming using C++. We assume that students have no programming experience. There is an emphasis on both the concepts and practice of problem solving and coding. This course covers variables, assignment statement, input / output statements, selection, repetition, functions, arrays, strings, and pointers. The course includes a number of labs, quizzes, and assignments. Students are expected to spend at least 10 hours on average per week on this course. Prerequisite: --
1501211Programming II3 credits (2-2:3)
Prerequisite: 1501116 Programming 1
This course introduces fundamental conceptual tools and their implementation of object-oriented design and programming such as: object, type, class, implementation hiding, inheritance, parametric typing, function overloading, polymorphism, source code reusability, and object code reusability. Object-Oriented Analysis/Design for problem solving. Implementation of the Object-Oriented programming paradigm is illustrated by program development in an OO language (C++). Prerequisite: 1501116 Programming I
1501215Data Structures3 credits (3-0:3)
Prerequisite: 1501211 Programming 2
Basics of algorithm design. Linear Structures: Multidimensional arrays and their storage organization, Lists, Stacks and Queues and introduction to the C++ STL library. Introduction to recursion. Nonlinear structures: trees (binary trees, tree traversal algorithms) and Graphs (graph representation, graph algorithms). Elementary sorting and searching methods. Prerequisite: 1501211 Programming II
1501220Python Programming3 credits (3-0:3)
Prerequisite: 1501100 AI and Digital Technologies
This course covers advanced features of the Python programming language, with an emphasis on programming practice. It aims to help learners understand the semantics of Python and the process of structuring data using collections such as lists, dictionaries, tuples, strings, and sets. Learners will be guided to develop important skills, including working with classes, data structures, and sorting algorithms, as well as implementing threads and exception handling. The course involves substantial Python programming practice through projects and assignments. Prerequisite: 1501100 Introduction to IT
1501263Introduction to Database Management Systems3 credits (3-0:3)
Prerequisite: 1501215 Data Structures
This course explores how databases are designed, implemented and used. The course emphasizes the basic concepts/terminology of the relational model and applications. The students will learn database design concepts, data models (Entity-Relationship and Relational Model), SQL, functional dependencies and normal forms. The students will gain experience working with a database management system. Prerequisite: 1501215 Data Structures
1501279Discrete Structures3 credits (3-0:3)
Prerequisite: 1440131 Calculus 1
This course emphasizes the representations of numbers, arithmetic modulo, radix representation of integers, change of radix. Negative and rational numbers. Sets, one-to-one correspondence, properties of union, intersection, and complement, countable and uncountable sets. Representing Relations, and Equivalence relations. Recurrence relations. Functions: Injective, surjective, and bijective functions. Mathematical Induction, proof by contradiction. Combinatorics and recurrence relations. Fundamentals of logic, truth tables, conjunction, disjunction, and negation, Boolean functions and disjunctive normal form. Numbers theory. Graphs Theory: Introduction, Paths and connectedness, Eulerian and Hamiltonian Graphs, Graph Isomorphisms. Trees. Prerequisite: 1440131 - Calculus I OR 1440133 - Calculus I for Engineering
1501315Practical Data Science3 credits (3-0:3)
Prerequisite: 1501220 Python Programming
Data science is an interdisciplinary field that uses statistics, mathematics, programming, and domain knowledge to extract insights from data. This course introduces students to key concepts such as data collection and Wrangling, data cleaning and normalization, data visualization and exploration. Students will learn to use Python tool and will gain hands-on experience in working with real-world datasets to solve complex problems and make data-driven decisions. Prerequisite: 1501220 Python Programming
1501329AI Ethics2 credits (2-0:2)
Prerequisite: 1501330 Intro. to Artificial Intelligence
This course provides an applied and critical introduction to ethical, legal, and societal issues in AI. Students study moral reasoning and professional responsibility; algorithmic bias and fairness; privacy and data protection; transparency and explainability; safety, robustness, and misuse risks (including generative AI); and organizational governance approaches for responsible AI. Using local and global case studies, students conduct audits and produce governance artefacts (e.g., responsible AI impact assessments, documentation, and policy briefs) aligned with major frameworks and relevant UAE/regional expectations. Prerequisite: 1501330 - Introduction to Artificial Intelligence
1501330Introduction to Artificial Intelligence3 credits (3-0:3)
Prerequisite: 1501215/1501214 + 1440131 (Data Structures/Prog with Data Structures + Calculus 1)
This course will provide an introduction to the fundamental concepts and techniques in the field of artificial intelligence. Topics covered in the course include basic search, heuristic search, game search, constraint satisfaction, knowledge representation, and machine learning algorithms, Robotics, and Natural Language Processing. Prerequisite: 1411215 Data Structures/ 1501214 Programming with Data Structures + 1440131 Calculus I
1501331Introduction to Machine Learning3 credits (3-0:3)
Prerequisite: 1501220 Python Programming
This course offers a foundational and practical exploration of machine learning, covering essential models and techniques for building and evaluating predictive systems. Students begin with core concepts, including feature types, model selection, and the bias-variance tradeoff. Key models explored include classification (k-nearest neighbors, logistic regression, and Gaussian Naive Bayes), regression (linear and elastic net), support vector machines, decision trees, and ensemble methods. Advanced topics introduce neural networks, deep learning architectures, and clustering techniques for unsupervised learning. The course emphasizes hands-on applications, preparing students to effectively design, assess, and improve machine learning models for practical applications. Prerequisite: 1501220-Python Programming
1501352Operating Systems3 credits (3-0:3)
Prerequisite: 1501215 Data Structures
This course covers the fundamentals of operating systems, including system structures, processes and threads, CPU scheduling, synchronization, memory management, virtual memory, storage systems, and disk scheduling algorithms. Emphasis is placed on understanding system-level design, resource management, and performance evaluation of modern operating systems. Prerequisite: 1501215 Data Structures
1501364Big Data Analytics3 credits (3-0:3)
Prerequisite: 1501220 + 1501263 (Python Programming + Intro to Data Base Systems)
This course introduces key concepts, technologies, and tools used in big data analytics. Topics include the data analytics lifecycle, clustering, classification, regression, association rules, time series, text analytics, MapReduce, Hadoop, in-database analytics, and data visualization. Students apply analytical techniques to real-world datasets and complete a team-based analytics project. Prerequisite: 1501263 Introduction to Database; 1440281 Introduction to Probability and Statistics
1501366Software Engineering3 credits (3-0:3)
Prerequisite: 1501215 Data Structures
This course follows the formal software development lifecycle from requirements and specification through design, implementation, testing, and maintenance. Topics include software process models, project management, UML modeling, verification and validation, quality assurance, and software maintenance. Prerequisite: 1501215 Data Structures
1501371Design & Analysis of Algorithms3 credits (3-0:3)
Prerequisite: 1501215, 1501279 (Data Structures, Discrete Structures)
This course emphasizes fundamental concepts in algorithm design and analysis, including divide and conquer, greedy methods, dynamic programming, backtracking, and randomized algorithms. It covers sorting, searching, graph algorithms, maximum flow, string matching, and computational complexity including NP-completeness. Prerequisite: 1501279 Discrete Structures; 1501215 Data Structures
1501387Practical Training I in AI1 credit (0-2:1)
Prerequisite: Junior/Senior Standing
The course will be designed to equip students with an understanding of their target industries and the professional skills required to excel in them. It will focus on imparting practical technical skills that are in high demand, alongside project management and execution strategies using industry-standard methodologies. Furthermore, the course will emphasize the development of essential soft skills, such as communication and teamwork, while also preparing students for the job market with robust career readiness training, including resume writing, interview techniques, and networking. Prerequisite: Junior Standing
1501388Practical Training II in AI2 credits
Prerequisite: 1501387 Practical Training I in AI
This course serves as a bridge between theoretical knowledge gained in introductory courses and its real-world application in professional settings. Through hands-on experiences and guided practice, students will develop essential skills and competencies necessary for success in the computing field. The students will engage in practical tasks and projects that simulate real-world computing scenarios. They will work individually and in teams to tackle challenges, develop solutions, and present their findings. Regular feedback sessions and self-reflection exercises will help students track their progress and identify areas for improvement. By the end of the course, students will have gained valuable hands-on experience, enhanced their technical proficiency, and developed the professional skills necessary to thrive in diverse computing environments. This course prepares students to function effectively in the workforce, contributing to the advancement of computing solutions while adhering to ethical standards and industry best practices. The course consists of no less than 6 weeks of supervised practical training at an approved organization for a minimum of 40 hours per week. The course is expected to be completed during the summer session. Prerequisite: 1501387 Practical Training I in AI
1501398Junior Project in AI3 credits (3-0:3)
Prerequisite: 1501215, 1501246 (Data Structures)
This course represents the first phase of the AI senior project. Students form teams, conduct literature reviews, define project requirements, design system architecture, and develop an initial AI-based prototype. Emphasis is placed on documentation, ethical reporting, teamwork, and effective presentation. Prerequisite: 1501215 Data Structures; 1501246 Object-Oriented Design
1501431Edge AI3 credits (3-0:3)
Prerequisite: 1501331 Machine Learning
This course introduces the fundamentals of Edge Artificial Intelligence and deployment of AI models close to data sources. Topics include edge-cloud architectures, resource constraints, model trade-offs, security, privacy, safety, and system-level design considerations for real-world edge applications. Prerequisite: Object-Oriented Design with Java
1501433Intro. to Comp. Vision & Image Proc.3 credits
Prerequisite: 1501215/1501214 (Data Structures/Prog with Data Structures)
1501435Natural Language Processing3 credits (3-0:3)
Prerequisite: 1501331 Machine Learning
This course introduces the fundamentals of Natural Language Processing (NLP) and focuses on how computers analyze, process, and represent human language. Topics include text preprocessing, tokenization, normalization, feature extraction, text representation (bag-of-words, n-grams, TF-IDF), basic text classification and sentiment analysis, model evaluation metrics, introductory neural NLP concepts, and ethical and social considerations in language technologies. Prerequisite: 1501331 Machine Learning
1501440Deep Learning Applications3 credits
Prerequisite: 1501331 Machine Learning
1501459Information Security3 credits (3-0:3)
Prerequisite: 1501215 Data Structures
This course introduces concepts, methodologies, and techniques in information security. Topics include security threats and attacks, malicious software, web and network vulnerabilities, access control mechanisms, cryptography and encryption algorithms, digital signatures and certificates, public key infrastructure, and legal, ethical, and professional aspects of information security. Prerequisite: 1501215 Data Structures or 1501214 Programming with Data Structures
1501498Senior Project in AI3 credits (3-0:3)
Prerequisite: 1501398 Junior Project in AI
This course builds on the Junior Project in AI and focuses on the design, integration, implementation, and evaluation of a substantial AI-based software system. Students work in teams to address a real-world problem, produce technical documentation, present and defend their solutions, and demonstrate professional, collaborative, and ethical practices in project development. Prerequisite: 1501398 Junior Project in AI

The page describes a course called Introduction to Deep Learning (1501446) rather than Deep Learning Applications (1501440), and gives no description for Introduction to Computer Vision and Image Processing (1501433). Where a course description gives a different prerequisite from this list, both are shown (the description’s is its last line).

Program requirements: elective courses (15 credit hours)

Five courses (Program Req. Elective 1 to 5 in the study plan).

1501335Agentic AI3 credits (3-0:3)
Prerequisite: 1501220 Python Programming
This course introduces the principles and design of autonomous and agentic artificial intelligence systems. Students explore intelligent agents, decision-making frameworks, goal-directed behavior, planning, reasoning, and interaction strategies in dynamic environments. Emphasis is placed on designing, implementing, and evaluating agent-based systems for real-world applications. Prerequisite: 1501330 Intro to AI
1501337Information Retrieval3 credits (3-0:3)
Prerequisite: 1501330 Intro. to Artificial Intelligence
This course introduces the fundamental concepts and techniques of information retrieval systems. Topics include text representation, indexing, ranking models, search evaluation, relevance feedback, web search technologies, and modern retrieval methods. Students gain practical experience in building and evaluating search and retrieval systems. Prerequisite: 1501330 Intro to AI
1501341Web Programming3 credits (3-0:3)
Prerequisite: 1501116 Programming 1
This course covers the fundamentals of web programming including client-side and server-side technologies. Topics include HTML, CSS, JavaScript, HTTP protocols, and dynamic web development. Students design and implement interactive and database-driven web applications. Prerequisite: 1501116 Programming I
1501432Introduction to Computational Intelligence3 credits (3-0:3)
Prerequisite: 1501215 Data Structures
This course introduces soft computing techniques including evolutionary algorithms, fuzzy systems, neural networks, and swarm intelligence. Students design and implement heuristic optimization methods and apply them to real-world artificial intelligence and optimization problems. Prerequisite: 1501215 Data Structures
1501434Reinforcement Learning3 credits (3-0:3)
Prerequisite: 1501330 Intro. to Artificial Intelligence
This course introduces reinforcement learning principles and algorithms for sequential decision-making. Topics include Markov Decision Processes, dynamic programming, Monte Carlo methods, temporal-difference learning, and Q-learning. Students implement and evaluate reinforcement learning solutions. Prerequisite: 1501330 Intro to AI
1501436Knowledge Representation & Reasoning3 credits (3-0:3)
Prerequisite: 1501279 Discrete Structures
This course introduces symbolic knowledge representation and reasoning techniques including propositional and first-order logic, ontologies, semantic web technologies, and knowledge graphs. Students design and query knowledge bases for intelligent systems applications. Prerequisite: 1501279 Discrete Structures
1501437Robotics & Autonomous Systems3 credits (3-0:3)
Prerequisite: 1501215/1501214 (Data Structures/Prog with Data Structures)
This course introduces robotics fundamentals including kinematics, dynamics, sensing, perception, and control. Students study modeling and analysis of robotic systems and explore applications in autonomous systems. Prerequisite: 1501215 Data Structures
1501438AI for Immersive Metaverse3 credits (3-0:3)
Prerequisite: 1501215/1501214 (Data Structures/Prog with Data Structures)
This course explores artificial intelligence concepts and implementations for immersive virtual environments. Topics include finite state machines, behavior trees, pathfinding, agent communication, and reinforcement learning within metaverse applications. Prerequisite: 1501215 Data Structures
1501443Human Computer Interaction3 credits (3-0:3)
Prerequisite: 1501220 + 1501315 (Python Programming + Practical Data Science)
This course introduces principles of human-computer interaction, usability engineering, and interface design. Students design, prototype, and evaluate user-centered interfaces for interactive systems. Prerequisite: 1501245 Multimedia Programming
1501444Game Design & Development3 credits (3-0:3)
Prerequisite: 1501215/1501214 (Data Structures/Prog with Data Structures)
This course covers principles of game design and development including gameplay mechanics, narrative design, game balancing, and AI integration. Students develop functional games using modern game engines. Prerequisite: 1501215 Data Structures
1501454Quantum Computing3 credits (3-0:3)
Prerequisite: 1501371 Design and Analysis of Algorithms
This course introduces the foundations of quantum computing including quantum circuits, algorithms, and programming frameworks. Students explore applications in cryptography, optimization, and machine learning. Prerequisite: None
1501458Mobile Application & Design3 credits (3-0:3)
Prerequisite: 1501215/1501214 (Data Structures/Prog with Data Structures)
This course introduces mobile application architecture and development using modern SDKs. Students design and implement mobile applications with user interfaces and event-driven programming. Prerequisite: 1501215 Data Structures
1501465Development of Web Applications3 credits (3-0:3)
Prerequisite: 1501341 + 1501263 (Web Programming + Intro to Data Base Systems)
This course focuses on building web-based database applications using three-tier architecture. Students develop dynamic web applications integrating databases, server-side scripting, and security mechanisms. Prerequisite: 1501341 Web Programming
1501497Topics in AI I3 credits (3-0:3)
Prerequisite: 1501331 Machine Learning
This course explores advanced and emerging topics in artificial intelligence, emphasizing recent research developments, experimentation, and critical evaluation of AI systems. Prerequisite: 1501330 Intro to AI
1501499Topics in AI II3 credits (3-0:3)
Prerequisite: 1501331 Machine Learning
This course continues the exploration of advanced AI topics with deeper focus on research trends, applications, evaluation, and independent study of emerging artificial intelligence technologies. Prerequisite: 1501330 Intro to AI

The page describes Topics in AI I under code 1501498, the code of Senior Project in AI; this list numbers it 1501497.

Support requirements: compulsory courses (15 credit hours)

Five courses.

0202227Critical Reading and Writing3 credits
1440131Calculus I3 credits
1440132Calculus II3 credits
1440211Linear Algebra I3 credits
1440281Intro Probability & Statistics3 credits
Prerequisite: 1440131 Calculus 1

Support requirements: elective courses (3 credit hours)

One course.

1420101General Chemistry I3 credits
1430110Physics I for Sciences3 credits
1450101General Biology I3 credits

Study plan: Year 1, Fall semester

15 credit hours

0201102Arabic Language3 credits
0202112English for Academic Purposes3 credits
University Req. Elective 13 credits
1440131Calculus I3 credits
University Req. Elective 23 credits

Study plan: Year 1, Spring semester

16 credit hours

0204102UAE Society3 credits
University Req. Elective 33 credits
1501116Programming I4 credits
1440132Calculus II3 credits
Support Req. Elective3 credits

Study plan: Year 2, Fall semester

15 credit hours

0104100Islamic Culture3 credits
1501211Programming II3 credits
1440281Intro Probability & Statistics3 credits
1501279Discrete Structures3 credits
1501220Python Programming3 credits

Study plan: Year 2, Spring semester

15 credit hours

0202227Critical Reading and Writing3 credits
1501215Data Structures3 credits
1501315Practical Data Science3 credits
1501100AI and Digital Technologies3 credits
1440211Linear Algebra I3 credits

Study plan: Year 3, Fall semester

17 credit hours

1501330Introduction to Artificial Intelligence3 credits
1501263Introduction to Database Management Systems3 credits
1501371Design & Analysis of Algorithms3 credits
1501366Software Engineering3 credits
1501352Operating Systems3 credits
1501329AI Ethics2 credits

Study plan: Year 3, Spring semester

16 credit hours

Program Req. Elective 13 credits
1501331Introduction to Machine Learning3 credits
Program Req. Elective 23 credits
1501364Big Data Analytics3 credits
1501398Junior Project in AI3 credits
1501387Practical Training I in AI1 credit

Study plan: Year 3, Summer semester

2 credit hours

1501388Practical Training II in AI2 credits

Study plan: Year 4, Fall semester

15 credit hours

1501433Intro. to Comp. Vision & Image Proc.3 credits
1501440Deep Learning Applications3 credits
1501459Information Security3 credits
1501498Senior Project in AI3 credits
Program Req. Elective 33 credits

Study plan: Year 4, Spring semester

12 credit hours (programme total: 123)

1501435Natural Language Processing3 credits
1501431Edge AI3 credits
Program Req. Elective 43 credits
Program Req. Elective 53 credits

Other courses described on the programme page

The programme page also describes this course, which its course lists and the study plan do not include.

1501446Introduction to Deep Learning3 credits (3-0:3)
This course provides a comprehensive introduction to foundational and advanced deep learning techniques. Topics include gradient descent and optimization (including Adam), neural networks and backpropagation, CNNs, RNNs, LSTMs, Autoencoders, GANs, Diffusion Models, Graph Neural Networks, Transformers, and selected applications such as NLP, recommender systems, and reinforcement learning. Emphasis is placed on implementation and practical AI applications. Prerequisite: 1501331 Introduction to Machine Learning

What You'll Learn

• Apply knowledge of computing, mathematics, and artificial intelligence principles to analyze and solve real-world problems. • Design intelligent systems and applications using appropriate AI methods, algorithms, and tools. • Analyze data and generate meaningful insights using machine learning and data-driven decision-making techniques. • Implement AI-based solutions responsibly by applying professional, ethical, and social responsibility principles. • Communicate technical concepts, analyses, and solutions effectively in written and oral forms to specialist and non-specialist audiences. • Collaborate productively as members of multidisciplinary and multicultural teams to develop AI-driven solutions. • Engage in lifelong learning and inquiry to remain current with emerging AI trends, technologies, and applications.

Entry Requirements

Academic: Admission requirements for this programme (Undergraduate Admissions page): • High school average: 70% for Advanced & General tracks. • Subject grades in the high school certificate: 70% in Mathematics and one science subject (Biology, Chemistry, or Physics) for General Track students. The high school grades mentioned are those of the UAE educational curriculum (Advanced Track) or their equivalents as per the framework approved by the Ministry of Education. Students who did not achieve the required subjects grades in high school will be accepted conditionally and will have to register and pass alternative qualifying courses at the university, which is equivalent to zero credit hours and is not included in the calculation of the cumulative average. The University of Sharjah accepts students who are academically qualified and behaviorally distinguished, regardless of their nationality, color, religion, or disability. All applicants must satisfy the following basic admission requirements: • Completion of secondary education or an equivalent level with the required average attained no earlier than three years prior to joining the University. Applicants to the College of Medicine and Dental Medicine need to have finished their secondary education no more than one year previously. • The applicant should not have been expelled from the UoS or any other institution for academic or disciplinary reasons. • The applicant should be medically, physically, and mentally fit, and must provide evidence of immunization against major blood-borne viral infections in accordance with the requirements for their major. • Applicants should indicate their order of preference for majors on the application form. • Admitted students are allocated to the University's colleges within the limits of the approved number of seats for each college, based on the capacity of each program. This is done according to their preferences and their grades. Admission to the University of Sharjah is competitive, with priority given to citizens of the UAE and students with high secondary school grades. • Applicants should complete and submit the application form and required documents to the Admissions Department by the stated deadlines, and pay the application fee of AED 360. • Meeting the secondary education grade requirements, fulfilling other admission requirements, submitting the application, paying the application fee, and receiving an ID number does not guarantee admission to the University. Students are required to pay a reservation fee after meeting specific criteria on a competitive basis, in accordance with University policy. Student reservation fees are nonrefundable if the student withdraws or does not enroll in the University, and are applied toward tuition fees if the student enrolls in their program. • Applicants to the Colleges of Medicine and Dental Medicine pay a 1500 AED fee for their applications to be considered as part of the admissions competitive process. This fee is non-refundable if the applicant is rejected or withdraws from the university. • Applicants who are still enrolled in secondary school will receive conditional admission until their final results are submitted. Meeting the required conditions and the minimum grade based on competitive standards is mandatory. • Students accepted conditionally will be given a specific period of time according to the related undertaking, and they must fulfill the conditional admission requirements to continue studying at the university. • Professional diploma Certifications are not acceptable for admission into undergraduate programs. Admission Regulations and Academic Qualification Requirements: • The grade averages stated above represent the minimum requirements for admission to the University. Please note that students meeting the above average requirements are not guaranteed admission but are subject to the University's approved competitive admission regulations and standards. Conditional Admission and Remedial Courses: • Failure to Meet the Minimum Required Grades in Qualifying Secondary School Subjects: Students who do not satisfy the minimum grade requirements in the qualifying secondary school subjects may be granted conditional admission. Such students will be required to complete equivalent remedial courses during their first semester of study. These courses carry zero credit hours and are not included in the calculation of the cumulative GPA. Successful completion of the remedial courses is required in accordance with University regulations. • Opportunity to Repeat Remedial Courses: Students who fail a remedial course on their first attempt will be granted one additional opportunity to repeat and pass the course. • Continuation in the Academic Program: Students who fail to successfully complete the required remedial course(s) after exhausting the permitted repeat opportunity will not be allowed to continue in their current academic program and must transfer to another program that does not require the relevant qualifying subjects. • Exception - Colleges of Medicine and Dentistry: Applicants to the Colleges of Medicine and Dentistry must fully satisfy all qualifying subject grade requirements for direct and final admission. Conditional admission and remedial courses are not permitted under any circumstances.

English: Required English test for this programme: 5.5 in IELTS Academic, 61 in TOEFL (IBT), 500 in TOEFL (ITP) or its equivalent. Upon admission to UoS and prior to course registration, all students admitted to UoS (new, transfer and bridging) must demonstrate a level of English proficiency consistent with the requirement of their college. The certificate must be submitted to the Admission department through their Admission Services Portal to be reviewed and approved by the English Language Center at the UoS. Accepted tests: • IELTS (Academic): accepted from any country; accepted from IDP and British Council; UKVI version accepted; computer and paper based accepted; Home Edition not accepted. • TOEFL iBT: accepted; Home Edition not accepted. • TOEFL ITP: accepted only if taken at the University of Sharjah (UOS), AMIDEAST Dubai (Head Office) or AMIDEAST Abu Dhabi (Head Office). • PTE Academic: accepted; Home Edition not accepted. Important notes: • English proficiency test Certificates older than two years are not accepted. • Students who achieve a score of 5.5 in the IELTS test are exempted from remedial skill courses. • Students who couldn't obtain the required score in any of the above listed English Proficiency tests will be enrolled in an "Intensive English Program" course, in which their English level will be determined by the result of their English proficiency test. All students are placed in the Intensive English program with a recognized Language qualification. • The UoS reserves the right to require students to attend an interview in the Languages Institute. Students may be required to take a further in-house test to ensure their scores are consistent with their English Language proficiency.

Careers

Graduates of the Bachelor of Science in Artificial Intelligence are well positioned to pursue diverse career pathways in AI-driven and data-intensive environments. The program prepares students with both technical depth and practical experience, enabling them to contribute effectively to digital transformation initiatives across multiple sectors. Typical career opportunities include: • AI Engineer: Design and develop intelligent systems and AI-powered applications for real-world problems. • Machine Learning Engineer: Build, train, optimize, and deploy machine learning and deep learning models. • Data Analyst: Analyze structured and unstructured data to generate actionable insights for decision-making. • Data Scientist (Entry Level): Apply statistical and machine learning techniques to solve complex analytical problems. • Intelligent Systems Developer: Develop rule-based and learning-based systems for automation and decision support. • Computer Vision Developer: Implement image processing and visual recognition solutions. • Natural Language Processing Engineer: Develop AI systems for text analysis, chatbots, and language technologies. • AI Applications Specialist: Customize and integrate AI tools within enterprise systems. • Business Intelligence Analyst: Support strategic decisions through analytics and predictive modeling. • AI Research Assistant: Contribute to applied research projects in universities or research centers. Graduates may find employment in: • Government entities and smart city initiatives • Technology and software development companies • Healthcare and biomedical organizations • Financial institutions and fintech companies • Cybersecurity and digital forensics firms • Logistics, transportation, and automation industries • Startups and innovation-driven enterprises The program also provides a strong academic foundation for graduates who wish to pursue postgraduate studies in Artificial Intelligence, Data Science, Computer Science, Robotics, or related disciplines.