MSc AI and Machine Learning

Standard LSI teaching system for 30-credit modules for subjects requiring hands-on technical skills.

Standard LSI teaching system for 15-credit modules for professional subjects focusing on understanding key concepts and processes, and developing management or analytical skills.

A practical project module, suitable for the final project of a specialist master's programme, with one-to-one supervisory meetings every 2 weeks for 45 minutes per session on average. This involves learning the concepts in the glossary of research methods and best-practices.

Standard LSI teaching system for 15-credit modules for subjects requiring hands-on technical skills.

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.

In this learning activity, students explore recent real-world case studies relevant to their course topic. The case studies will have been selected and curated by the module leader to represent up-to-date examples. They guide students through key details, contextual factors, and outcomes. This approach enhances students' understanding of current industry trends, challenges, and solutions, preparing them for real-world scenarios they may encounter in their future careers.

The learning experienced will be augmented by AI (virtual private tutor) allowing the students to critically engage with the content and discuss the case studies.

AI Roleplay is an innovative educational approach that leverages artificial intelligence to create immersive, interactive learning experiences for university students. In this activity, students are presented with a professional challenge or scenario relevant to their course. They then engage in a simulated interaction with one or more AI-powered characters, each programmed to embody specific roles, personalities, and expertise.

These AI characters respond dynamically to the student's inputs, creating a realistic and adaptive roleplay environment. Students can practice their communication skills, decision-making, problem-solving, and other professional competencies in a safe, low-stakes setting. After the session, the AI system provides detailed feedback on the student's performance, highlighting strengths and areas for improvement. This personalised guidance helps students refine their skills and gain confidence in handling real-world professional situations.

Those studying in the blended learning mode will attend these 9 weekly classes (in person or remotely) during weeks 2 to 10. These sessions will complement the theory already studied during the preceding week (in our flipped-learning model), with discussions, analysis, practice or experience . They will be interactive and participatory, rather than one-way lectures. There will also be an opportunity for Q&A in every session. Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning. They may include hands-on project work, group discussions or debates, roleplay, simulation, case studies, presentations, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students.

Live lectures are used to facilitate discussions and provide students with an opportunity to ask questions and engage with module material in real-time. Instructors often use live lectures to clarify complex ideas, provide examples, and encourage critical thinking. Live lectures can also be recorded and made available for students to review later, allowing them to revisit important concepts or catch up on missed material.

Each Workshop/Lab session will be followed by an assignment. Assignments are used to reinforce learning and encourage independent thinking and problem-solving. They help the students identify the gaps in their understanding of the subject and provide them with an opportunity to apply what they have learned in a practical setting.

Assignments can be individual or group-based (teams of 2 to 4). They can take many forms, including essays, presentations, or projects. When they are group-based, teams will be randomly picked by AGS, in order to promote broader teamwork practice. Assignment files will be uploaded to AGS by the students ahead of the next weekly session. Feedback will be provided on each submitted assignment.

These are typically student-led presentations showcasing their research on specific module topics. After a period of independent exploration, students craft a structured presentation to share their findings with peers and instructors. Following the delivery, an interactive Q&A segment tests their understanding and adaptability to spontaneous queries. Feedback from the module leader and peers evaluates the research's depth, presentation efficacy, and Q&A responses.

Their purpose is to deepen subject knowledge but also hone presentation and critical thinking skills, preparing students for future academic and professional engagements.

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.

Part of the credit hours on a module are also made up of self-guided individual research. These hours enable students to look at what components they are going to study on a module and ascertain for themselves what they will believe will additionally benefit their leaning. This may be prior to attending a lecture or workshop, following their use of a concept learning (knowledge graph) where they identify that additional reading may deepen their understanding of a concept, or after a seminar has taken place. Students will also use self-guided individual research to prepare for summative assessments. In the main, as this is self-guided, students will decide for themselves what additional research they will do. This will require them to identify what concepts or knowledge, skills, and competencies they want to deepen, what resources will assist them, such as books, videos, or online sources, how they will use these, and what the outcomes should be. Students may decide to work with their peers in undertaking this individual research – and they can ask their tutors for guidance and help. Students may also have to use some of their self-guided individual research to prepare for lectures, workshops, or assignments, or for work their tutors have set them.

Summative assessments are used to evaluate student learning at the end of a module. These assessments can take many forms, including exams, papers, or presentations. Instructors can use summative assessments to measure whether students have achieved the learning outcomes for the module and provide them with a sense of their overall progress. Summative assessments can also be used to evaluate the effectiveness of the teaching methods used in the module.

During these meetings, the student presents their recent progress, including any research findings, data analysis, or draft sections of their work. The supervisor provides feedback, addressing both strengths and areas needing improvement. These sessions often involve discussing challenges faced by the student, strategising solutions, and setting goals or deadlines for the next phase of work. The supervisor may also offer insights on relevant literature, methodologies, or academic writing techniques.