Artificial Intelligence Engineering MSc

AI in Education

This module aims to develop an advanced, critical, and applied understanding of how Artificial Intelligence can be designed, implemented, and evaluated within educational contexts.

The Educational Technology market is worth over £10 billion in the UK and over £190 billion worldwide. A significant trend in this sector is the increasing use of embedded Artificial Intelligence across teaching, learning, assessment, and student support systems. Graduates in this sector, including Computer Science, Education, Business and Marketing disciplines, are increasingly expected not only to design and develop AI systems, but also to understand their pedagogical value, ethical implications, legal constraints, and societal impact, particularly in education.

The module enables you to explore how AI such as generative models, adaptive systems, automated assessment, feedback engines, and learning analytics can enhance educational practice. You will critically examine the benefits, risks, and limitations of AI‑enabled education, with a strong emphasis on responsible, inclusive, and lawful deployment in the UK and global contexts.

The module draws upon and extends students' prior knowledge of computing, data, and AI, while remaining accessible to students from other disciplines, such as Education, Business and Marketing, who wish to engage critically with AI‑enhanced learning. Through applied problem-solving, case studies, and design activities, students will develop the skills required to design AI‑supported educational opportunities aligned with real‑world institutional, regulatory, and professional constraints.

Responsible AI: Ethics, Law and Governance

The module aims to explore and judge the nature and scope of AI interventions, the legal, social, moral and ethical implications of these technologies and their impact on societies and government institutions.

With the ever increasing adoption of AI systems for decision making, customer service, modelling and many other applications, the impact these systems have on our everyday lives must be examined and critically assessed in terms of their fairness, their compliance with legal frameworks and how they affect the diverse populations around the world.

Statistical Applications of Industrial Big Data

​​Nowadays large amounts of data are collected from many different sources; such data can be used for enhanced benefits and impact to society by evaluating the quality and relevance, integrating with existing information and digital resources, extracting patterns and creating new knowledge for decision support in engineering, healthcare and wellbeing, and society sustainable development.

​However, large amounts of data create continuous challenges for relevant and effective usage in industry. The module is intended to Engineering, Management, Data Analytics, Computer Science and similar subject graduates to gain hands-on development of advanced knowledge and skills in the application of statistical methods in support of robust big data-based decision-making.

​This research-informed module enables students to develop both specialist knowledge and enhanced problem-solving skills in statistical data analysis required to apply data science principles, and to provide data-driven, innovative engineering solutions for decision support and data-enhanced applications.

​Students will explore how statistical applications of industrial big data resources can support knowledge discovery for decision making in domains such as industry 4.0/5.0, product design and development, product quality management and product safety. The module will also address legal, social ethical and professional aspects of such application domains. Students will also benefit from a hybrid approach of learning and assessment, benefitting from critical research, planning and working as team members, and solution development and demonstration as individual professionals.

​The example topics are aligned with the programme of studies topics through relevant data resources and references. The module assessment will provide opportunities to develop interest, knowledge and skills in problem solving applying statistics to industrial big data challenges and projects within the new wave of data-enhanced engineering developments.​

Cloud AI

​​Are you excited to use massive amounts of data to develop Artificial Intelligence (AI) models on the cloud? Eager to learn from many different enterprise's use cases of cloud AI? Want to help the largest global enterprises derive business value through the adoption of AI?

​In this module, you will learn the basics of cloud computing, including the advantages and disadvantages of cloud computing. We will reveal what cloud services such as Amazon Web Services are and the Cloud Adoption Frameworks. This course assumes you have a non-IT background, as it will not teach you how to build applications in the cloud. This course will give you a general conceptual understanding of cloud AI frameworks and services.

Advanced Machine Learning

The application of Artificial Intelligence to real world problems particularly facing big data challenges often requires the use of complex machine learning algorithms chosen from a broad and deep range of possible knowledge and tools. Understanding the context within which predictive modelling, data clustering and other techniques operate, and how to appropriately select and use them for a defined purpose, requires an understanding of both theoretical concepts and practical aspects of Computer Science and Artificial Intelligence.

Artificial intelligence is part of everyday life across the world, providing cutting edge digital technology solutions, opportunities and challenges in every economic, scientific, social, health and environment sector amongst others, including business, education, care, manufacturing, finances, travel, biodiversity and wellbeing, while supporting also scientific discoveries and progress.

In this module, you will have the opportunity to grow and apply the knowledge and skills assimilated and imparted during all years of your programme of studies and advance your problem-solving skills through the solution of a problem normally involving research, analysis, design and practical development of a machine learning product or process.

The module aims to explore and define current theoretical knowledge including data, information, knowledge and intelligence concepts, also explore in depth understanding of analysis, algorithms and techniques used to enable sustainable machine learning technologies and models, and to develop practical skills in applying them in specific contexts. Consequently students will have the necessary domain knowledge and applied skills to understand the problem, define project requirements and select/use a range of appropriate techniques and processes to produce a suitable solution.

For our Computer Science programmes the British Computer Society (BCS - The Chartered Institute for IT) - is the key Professional, Statutory, and Regulatory Body (PSRB). The accredited programs ensure meeting the standards required for graduates to enter the profession and gain recognition as IT professionals. The accreditation, that involves a rigorous review process, including curriculum evaluation, teaching quality, and the relevance of the program to industry needs and professional career building, is relevantly addressed by the enclosed module Learning Outcomes, Teaching and Learning Methods, Assessment Strategy and delivery.

Business Systems Security

To provide students with a thorough comprehensive understanding of the underlying technologies of secure systems and a critical awareness of the associated risks and privacy concerns in their use within commercial and business contexts.

MSc Group Project

​​​​Conducting projects is an integral part of academic and/or professional career. Some projects fail due technical reasons, but most failures are directly related to the poor project management and not applying the principles of good project management. In this module, the students will learn project management processes that project teams must understand and the standards to which their projects are being held to and apply these principles in a practical project. The module gives students experience of carrying out a computer science research project that solves a particular research problem relevant to the programme of study (e.g. Artificial Intelligence, Big Data Systems, Internet of Things, etc.). The students will either provide a computational solution using scientific tools and methods and performing computational analysis, or they provide a software engineering solution by developing a fully functional software system using state-of-the-art software development techniques. The students will also analyse legal, social, ethical and professional issues related to their projects. By working in teams, the students are expected to demonstrate the need for a professional approach to all aspects of research and software development. As part of the project, teams will deliver reports, code, and demonstrate a working software system or a computational solution. In addition, they will present certain elements of their work in written, graphical and verbal forms through the production of materials, e.g., reports, demo, and oral presentation. The students will be guided by a supervisor from the relevant research disciplines.​

Dissertation

​​The Dissertation for all MSc programmes is driven by the student's aspirations and preferences and draws on and enhances the professional skills and knowledge gained by the student during the taught component of the course. The student must select relevant tools and techniques acquired throughout their specific program of study (AI, Cyber Security, or Big Data) and design and develop a solution to a practical problem or challenge by using either (1) a real-world data set or (2) a generated hypothetical data set. Completing their dissertation, the student will be able to demonstrate evidence of achieving the program learning outcomes and graduate attributes and be able to o demonstrate these to future employers, including consideration of legal, social, ethical, and professional issues relevant to their topic.​

AI in Education

This module aims to develop an advanced, critical, and applied understanding of how Artificial Intelligence can be designed, implemented, and evaluated within educational contexts.

The Educational Technology market is worth over £10 billion in the UK and over £190 billion worldwide. A significant trend in this sector is the increasing use of embedded Artificial Intelligence across teaching, learning, assessment, and student support systems. Graduates in this sector, including Computer Science, Education, Business and Marketing disciplines, are increasingly expected not only to design and develop AI systems, but also to understand their pedagogical value, ethical implications, legal constraints, and societal impact, particularly in education.

The module enables you to explore how AI such as generative models, adaptive systems, automated assessment, feedback engines, and learning analytics can enhance educational practice. You will critically examine the benefits, risks, and limitations of AI‑enabled education, with a strong emphasis on responsible, inclusive, and lawful deployment in the UK and global contexts.

The module draws upon and extends students' prior knowledge of computing, data, and AI, while remaining accessible to students from other disciplines, such as Education, Business and Marketing, who wish to engage critically with AI‑enhanced learning. Through applied problem-solving, case studies, and design activities, students will develop the skills required to design AI‑supported educational opportunities aligned with real‑world institutional, regulatory, and professional constraints.

Responsible AI: Ethics, Law and Governance

The module aims to explore and judge the nature and scope of AI interventions, the legal, social, moral and ethical implications of these technologies and their impact on societies and government institutions.

With the ever increasing adoption of AI systems for decision making, customer service, modelling and many other applications, the impact these systems have on our everyday lives must be examined and critically assessed in terms of their fairness, their compliance with legal frameworks and how they affect the diverse populations around the world.

Statistical Applications of Industrial Big Data

​​Nowadays large amounts of data are collected from many different sources; such data can be used for enhanced benefits and impact to society by evaluating the quality and relevance, integrating with existing information and digital resources, extracting patterns and creating new knowledge for decision support in engineering, healthcare and wellbeing, and society sustainable development.

​However, large amounts of data create continuous challenges for relevant and effective usage in industry. The module is intended to Engineering, Management, Data Analytics, Computer Science and similar subject graduates to gain hands-on development of advanced knowledge and skills in the application of statistical methods in support of robust big data-based decision-making.

​This research-informed module enables students to develop both specialist knowledge and enhanced problem-solving skills in statistical data analysis required to apply data science principles, and to provide data-driven, innovative engineering solutions for decision support and data-enhanced applications.

​Students will explore how statistical applications of industrial big data resources can support knowledge discovery for decision making in domains such as industry 4.0/5.0, product design and development, product quality management and product safety. The module will also address legal, social ethical and professional aspects of such application domains. Students will also benefit from a hybrid approach of learning and assessment, benefitting from critical research, planning and working as team members, and solution development and demonstration as individual professionals.

​The example topics are aligned with the programme of studies topics through relevant data resources and references. The module assessment will provide opportunities to develop interest, knowledge and skills in problem solving applying statistics to industrial big data challenges and projects within the new wave of data-enhanced engineering developments.​

Cloud AI

​​Are you excited to use massive amounts of data to develop Artificial Intelligence (AI) models on the cloud? Eager to learn from many different enterprise's use cases of cloud AI? Want to help the largest global enterprises derive business value through the adoption of AI?

​In this module, you will learn the basics of cloud computing, including the advantages and disadvantages of cloud computing. We will reveal what cloud services such as Amazon Web Services are and the Cloud Adoption Frameworks. This course assumes you have a non-IT background, as it will not teach you how to build applications in the cloud. This course will give you a general conceptual understanding of cloud AI frameworks and services.

Advanced Machine Learning

The application of Artificial Intelligence to real world problems particularly facing big data challenges often requires the use of complex machine learning algorithms chosen from a broad and deep range of possible knowledge and tools. Understanding the context within which predictive modelling, data clustering and other techniques operate, and how to appropriately select and use them for a defined purpose, requires an understanding of both theoretical concepts and practical aspects of Computer Science and Artificial Intelligence.

Artificial intelligence is part of everyday life across the world, providing cutting edge digital technology solutions, opportunities and challenges in every economic, scientific, social, health and environment sector amongst others, including business, education, care, manufacturing, finances, travel, biodiversity and wellbeing, while supporting also scientific discoveries and progress.

In this module, you will have the opportunity to grow and apply the knowledge and skills assimilated and imparted during all years of your programme of studies and advance your problem-solving skills through the solution of a problem normally involving research, analysis, design and practical development of a machine learning product or process.

The module aims to explore and define current theoretical knowledge including data, information, knowledge and intelligence concepts, also explore in depth understanding of analysis, algorithms and techniques used to enable sustainable machine learning technologies and models, and to develop practical skills in applying them in specific contexts. Consequently students will have the necessary domain knowledge and applied skills to understand the problem, define project requirements and select/use a range of appropriate techniques and processes to produce a suitable solution.

For our Computer Science programmes the British Computer Society (BCS - The Chartered Institute for IT) - is the key Professional, Statutory, and Regulatory Body (PSRB). The accredited programs ensure meeting the standards required for graduates to enter the profession and gain recognition as IT professionals. The accreditation, that involves a rigorous review process, including curriculum evaluation, teaching quality, and the relevance of the program to industry needs and professional career building, is relevantly addressed by the enclosed module Learning Outcomes, Teaching and Learning Methods, Assessment Strategy and delivery.

Business Systems Security

To provide students with a thorough comprehensive understanding of the underlying technologies of secure systems and a critical awareness of the associated risks and privacy concerns in their use within commercial and business contexts.

MSc Group Project

​​​​Conducting projects is an integral part of academic and/or professional career. Some projects fail due technical reasons, but most failures are directly related to the poor project management and not applying the principles of good project management. In this module, the students will learn project management processes that project teams must understand and the standards to which their projects are being held to and apply these principles in a practical project. The module gives students experience of carrying out a computer science research project that solves a particular research problem relevant to the programme of study (e.g. Artificial Intelligence, Big Data Systems, Internet of Things, etc.). The students will either provide a computational solution using scientific tools and methods and performing computational analysis, or they provide a software engineering solution by developing a fully functional software system using state-of-the-art software development techniques. The students will also analyse legal, social, ethical and professional issues related to their projects. By working in teams, the students are expected to demonstrate the need for a professional approach to all aspects of research and software development. As part of the project, teams will deliver reports, code, and demonstrate a working software system or a computational solution. In addition, they will present certain elements of their work in written, graphical and verbal forms through the production of materials, e.g., reports, demo, and oral presentation. The students will be guided by a supervisor from the relevant research disciplines.​

Dissertation

​​The Dissertation for all MSc programmes is driven by the student's aspirations and preferences and draws on and enhances the professional skills and knowledge gained by the student during the taught component of the course. The student must select relevant tools and techniques acquired throughout their specific program of study (AI, Cyber Security, or Big Data) and design and develop a solution to a practical problem or challenge by using either (1) a real-world data set or (2) a generated hypothetical data set. Completing their dissertation, the student will be able to demonstrate evidence of achieving the program learning outcomes and graduate attributes and be able to o demonstrate these to future employers, including consideration of legal, social, ethical, and professional issues relevant to their topic.​