PMROBWAI - MSc Robotics with Artificial Intelligence

Students will develop advanced knowledge and practical expertise at the interface between robotics and artificial intelligence; two technologies that are reshaping industry, healthcare, transport, and society. The course equips students to design and build intelligent robotic systems that can perceive, reason, and act autonomously, combining rigorous theoretical understanding with hands-on, project-based experience. Students will learn to integrate sensing, control, machine learning, and computer vision, and to apply these skills across sectors ranging from manufacturing and logistics to assistive health technologies and autonomous vehicles.

The programme is distinctive in its practice-based, research-informed pedagogy and its alignment with London Metropolitan University’s long tradition of applied engineering education. Students will learn through experimentation, prototyping, and reflective practice in specialist laboratories, supported by access to cutting-edge facilities in the Centre for Communication Technology and the Cyber Security Research Centre. The course embodies the School’s strategy for learning, teaching and assessment by promoting active, inclusive, and digitally-enhanced learning. Students will engage in authentic assessments that mirror industry practice, including design reports, prototype demonstrations, technical blogs, and oral vivas. At the same time, developing both technical mastery and professional competencies mapped to the UK-SPEC and the Institution of Engineering and Technology (IET) learning outcomes.

Students will experience a blended learning environment that integrates face-to-face teaching with e-learning via the University’s virtual learning environment (WebLearn). Lecture recordings, digital laboratories, simulation tools, and collaborative discussion spaces will ensure continuous engagement and flexibility for full-time and part-time learners. Each module site provides structured weekly activities, formative feedback opportunities, and clear pathways for independent and group learning. The inclusive curriculum design ensures that all students, regardless of background or protected characteristic, are able to participate fully, with diverse examples, case studies and reading materials embedded across the programme. Education for Social Justice Framework (ESJF 2.0) is embedded across the MSc Robotics and Artificial Intelligence through an accessible, empowering and reflective curriculum. The course is designed to remove barriers to participation through inclusive and flexible learning approaches, supported by Universal Design for Learning principles and varied assessment formats. Students are empowered through authentic, project-based activities that encourage critical engagement with the societal impact of robotics and AI, valuing diverse perspectives and supporting student agency. Reflective practice is integrated throughout the programme, enabling students to examine professional responsibilities, ethical decision-making, and their own development as inclusive engineers contributing positively to society. Current modules enable students to deepen their expertise in advanced mathematical modelling, statistical forecasting, and emerging computational paradigms, such as quantum computing, thereby enabling critical engagement with future technologies shaping robotics, artificial intelligence, and intelligent systems.

The MSc Robotics and AI responds directly to market demand and national skills priorities. As the industry adopts automation and intelligent systems at scale, there is a growing need for engineers who combine systems thinking with data-driven insight. Students will graduate with highly transferable skills, problem-solving, innovation, ethical awareness, and leadership. All of which enhance employability in robotics, AI development, automation engineering, software and systems design, research, and entrepreneurship. Career opportunities span manufacturing, transport, healthcare, defence, fintech, creative technologies, and academia.

Through this course, students will become adaptable professionals and innovators capable of contributing to the UK’s digital and industrial strategy, shaping the next generation of intelligent robotic systems for a sustainable and inclusive future.

The MSc Robotics and Artificial Intelligence is designed to fulfil London Metropolitan University’s mission to provide a transformative, research-informed education that empowers students from diverse backgrounds to become confident, values-driven and employable graduates. The course aims to equip students with advanced knowledge, technical expertise, and professional competencies at the intersection of robotics, artificial intelligence and intelligent systems engineering.

Aligned with the University’s Learning, Teaching and Assessment Strategy and the QAA Subject Benchmark for Master’s Degrees in Engineering, the programme meets the educational requirements for registration as a Chartered Engineer (CEng) with the Engineering Council UK.

The aims of the course are to:
• Develop advanced and specialist understanding of the principles, technologies and methodologies underpinning modern robotics and artificial intelligence systems, robotic systems, including sensors, actuators, control, machine learning and computer vision.
• Enable students to integrate theory and practice through the design, simulation, and implementation of intelligent robotic systems that can perceive, reason, and act autonomously in complex environments.
• Foster creativity and innovation by encouraging students to design novel solutions, products and processes that respond to real-world challenges, taking account of sustainability, ethics, and societal impact.
• Cultivate critical awareness and independent judgement, enabling students to evaluate current research, emerging technologies and industry practices in robotics and AI.
• Develop collaborative and leadership skills, preparing students to work effectively in multidisciplinary and multicultural teams and to manage complex engineering projects within commercial and industrial constraints.
• Promote ethical, professional and regulatory awareness, including consideration of health and safety, risk, environmental and information security issues relevant to robotics and AI practice.
• Advance students’ research capability through engagement with research-active staff, centres and projects, culminating in an independent MSc dissertation that demonstrates originality and analytical rigour.
• Strengthen communication skills, enabling students to convey complex technical ideas clearly and effectively to specialist and non-specialist audiences in oral, written and visual formats.
• Encourage lifelong learning and continuing professional development, preparing graduates to adapt to rapid technological change and to pursue further study or professional recognition such as Chartered Engineer status.
• Support employability and enterprise by integrating authentic assessments, transferable skills, and opportunities for innovation, entrepreneurship and industry engagement throughout the course.

On successful completion of the MSc Robotics and Artificial Intelligence, students will be able to:

LO1
Students will demonstrate a comprehensive and systematic understanding of the scientific, mathematical and computational principles underpinning robotics and AI, including sensing, control, perception, learning and decision-making, applying advanced mathematics and computational methods to complex engineering problems (AHEP4 M1–M2). This outcome aligns with Engineering Council area A and is assessed at FHEQ Level 7 through Analyse / Evaluate. Indicative assessment includes examinations, analytical design reports, laboratory work and seminars.

LO2
Students will critically analyse and evaluate complex robotic and AI systems, applying modelling, simulation and analytical techniques to predict performance, assess limitations and propose improvements under conditions of uncertainty and within commercial, environmental, sustainability, security and ethical constraints (AHEP4 M3–M4, M10). This aligns with Engineering Council areas A, B and E and is assessed at FHEQ Level 7 through Evaluate / Synthesise. Indicative assessment includes coursework, simulation projects and case studies.

LO3
Students will integrate hardware, software and algorithmic components to design, implement and test intelligent robotic systems capable of autonomous behaviour, delivering innovative solutions to complex engineering problems and making informed engineering decisions in the presence of uncertainty and incomplete data (AHEP4 M5–M6, M12). This aligns with Engineering Council areas B and C and is assessed at FHEQ Level 7 through Create / Evaluate. Indicative assessment includes major laboratory projects, practical workshops and the MSc Project.

LO4
Students will apply advanced research, analytical and problem-solving skills to generate innovative solutions, designs or methodologies for complex and emerging challenges, and critically appraise current and emerging technologies within the robotics and AI domain (AHEP4 M7–M8). This aligns with Engineering Council areas A and B and is assessed at FHEQ Level 7 through Analyse / Create. Indicative assessment includes the dissertation, research proposals and technical reviews.

LO5
Students will demonstrate effective project management, leadership and teamwork in multidisciplinary contexts, managing resources risk and commercial considerations responsibly, and communicating complex engineering findings clearly to specialist and non-specialist audiences (AHEP4 M15–M17). This aligns with Engineering Council areas C, D and E and is assessed at FHEQ Level 7 through Apply / Evaluate. Indicative assessment includes group projects, presentations and reflective logs.

LO6
Students will evaluate and apply relevant professional, legal and ethical frameworks, including health and safety, sustainability, equality, diversity and inclusion, cybersecurity and data protection, when developing responsible solutions to complex robotics and AI engineering problems (AHEP4 M9–M10). This aligns with Engineering Council area E and is assessed at FHEQ Level 7 through Evaluate / Apply. Indicative assessment includes essays, policy reviews and project risk assessments.

LO7
Students will exhibit autonomy, creativity and originality in independent learning and professional development, identifying personal goals and reflecting critically on progress to sustain lifelong learning, professional judgement and continuing professional development (AHEP4 M18). This aligns with Engineering Council areas C and E and is assessed at FHEQ Level 7 through Evaluate / Create. Indicative assessment includes reflective portfolios, learning journals and supervision meetings.

LO8
Students will demonstrate confidence, resilience, ambition and creativity, acting as inclusive, collaborative and socially responsible professionals, recognising the wider societal, ethical and security implications of engineering decisions in robotics and AI practice (AHEP4 M10–M11, M16–M17). This aligns with Engineering Council areas D and E and is assessed at FHEQ Level 7 through Apply / Internalise / Create. Indicative assessment includes professional development portfolios, viva examinations and peer assessment.

1. QAA Subject Benchmark Statement for Engineering
2. Engineering Council UK - The Accreditation of Higher Education Programmes

Assessment across the MSc Robotics and Artificial Intelligence is designed to measure achievement of the course learning outcomes and to prepare students for professional practice. The strategy emphasises authentic, inclusive and developmental assessment aligned with the University’s Education for Social Justice Framework. Students will be assessed through a range of methods that mirror professional and research practice, including laboratory logbooks, design portfolios, case study analyses, presentations, group projects, examinations and the final MSc dissertation and viva.

In line with ESJF 2.0, assessment is designed to be inclusive and accessible, offering students multiple ways to demonstrate achievement of learning outcomes through practical portfolios, reflective components, presentations and applied project work. This approach supports diverse learners, promotes engagement for all, and encourages critical reflection on professional practice within robotics and AI.

Each module specifies its assessment pattern and criteria in the module guide, available via WebLearn. Students will have access to detailed marking rubrics and exemplars to promote transparency and a shared understanding of academic standards. Assessments are introduced progressively, developing from structured analytical tasks in early modules to independent research and innovation in the final project. At least one summative authentic assessment in every module links directly to employability skills such as problem-solving, teamwork, communication and project management.

Formative assessment is embedded throughout the course to help students reflect on their progress and prepare for summative tasks. Feedback is timely, constructive and developmental, delivered through written comments, in-class discussion, and individual tutorials. Students will be encouraged to act on feedback to enhance their subsequent work and to reflect on their learning through their Living CV and Personal Development Planning (PDP) activities.

Group-based assessments promote collaborative working, negotiation, and leadership skills valued by employers. Individual accountability is maintained through reflective statements and peer evaluation. The 60-credit MSc Project represents the capstone of the course, enabling students to demonstrate autonomy, originality and professional competence through research, implementation, and critical evaluation, culminating in a dissertation and viva examination.

Assessment and feedback practices are underpinned by educational scholarship, subject benchmarks and professional standards (Engineering Council UK-SPEC). All assessments are internally moderated, externally examined and subject to School-wide calibration to ensure fairness, reliability and consistency. The overall approach enables students to evidence the knowledge, skills and behaviours expected of professional engineers and researchers in the field of Robotics and Artificial Intelligence.

Exceptionally talented students may be invited to join and contribute to research and enterprise activities within the School’s research centres. In addition, students possessing extensive industrial experience and/or relevant professional qualifications may be given the opportunity to teach within the School. Some interested students may be offered to serve as student coaches in the School’s undergraduate courses.

Part-time students may select one or two modules per semester in consultation with the course leader. Two possible examples are as follows:

Example 1:
Year 1 (Semester 1/ Autumn): Robotic Systems, Artificial Intelligence
Year 1 (Semester 2 / Spring): Sensors, Actuators and Control, Computer Vision
Year 2 (Semester 1/ Autumn): Option (e.g. Operation Technology and Management)
Year 2 (Semester 2 / Spring): Option (e.g. Statistical Modelling and Forecasting or Quantum Computing and Applications)
Year 2 (Summer): MSc Project

Example 2:
Year 1 (Semester 1/ Autumn): Robotic Systems,
Year 1 (Semester 2 / Spring): Sensors, Actuators and Control
Year 2 (Semester 1/ Autumn): Artificial Intelligence and an option (e.g. Operation Technology and Management)
Year 2 (Semester 2 / Spring): Computer Vision and an option (e.g. Statistical Modelling and Forecasting or Quantum Computing and Applications)
Year 2 (Summer): MSc Project

The Institution of Engineering and Technology (IET) accreditation is awarded according to the following additional course regulation:

For students starting from academic year 2022-23, a maximum of 20 credits can be compensated in the entire programme, excluding the major individual project module. The minimum module mark for which compensation is allowed is no more than ten percentage points below the nominal module pass mark.

To obtain the degree award of MSc Robotics with Artificial Intelligence, the students need to complete successfully all 6 taught modules and the MSc project module (a minimum of 180 credits with a minimum mark of 50%).

Completing all six taught modules (a minimum of 120 taught credits excluding the Project with a minimum mark of 50%), included in the course curriculum, entitles the students to the PGDip Robotics with Artificial Intelligence award.

Completing two out of three CT modules (CT7158 Robotics Systems, CT7159 Sensors, Actuators and Control, CT7160 Computer Vision) and CS7050 Artificial Intelligence (a minimum of 60 taught credits excluding the Project with a minimum mark of 50%) from the curriculum entitles the students to the PGCert Robotics with Artificial Intelligence award.

The MSc award is only categorised into the following specific grades with due consideration to borderline cases as per University regulations:

MSc with Distinction: This award is achieved by a student gaining an overall average mark on the programme of study of 70% and above.

MSc with Merit: This award is achieved by a student gaining an overall average mark on the programme of study between 60% and 69.99%.

MSc: This award is achieved by a student gaining an overall mark in the programme of study between 50% and 59.99%.

Reflective learning and Personal Development Planning (PDP) are integral to the MSc Robotics and Artificial Intelligence, supporting students in becoming self-aware, autonomous and employable professionals. Students will be encouraged to reflect on their progress, identify strengths and areas for improvement, and set goals for their continuing professional development from induction through to the MSc Project.

From the outset, students will be expected to take responsibility for their own learning and to engage actively in formative feedback, peer discussion and tutorial reflection. Lectures, workshops and laboratory sessions are designed not only to convey knowledge but also to stimulate reflection on how theory translates into real engineering practice. Structured formative feedback on laboratory reports, design exercises and project proposals enables students to evaluate their performance and apply feedback to subsequent work.

Reflective learning is further embedded through seminars and team design activities in which students present and evaluate their own research and that of others. The 60-credit individual project serves as a capstone experience where students critically assess their professional development, document learning outcomes, and demonstrate independence and creativity.

Throughout the course, employability and PDP are supported by the University’s Gateway to Success and Living CV initiatives, which help students articulate and evidence their evolving skills and competencies. Graduates will leave with a clear record of achievement, the confidence to manage their career development, and the reflective capacity required for lifelong learning and professional registration as Chartered Engineers.

Students will graduate from the MSc Robotics and Artificial Intelligence programme with the specialist technical skills and professional competencies required to thrive in a rapidly evolving sector. The course equips students to design, build and deploy intelligent robotic systems that combine mechanical, electrical and computational elements, and to apply artificial intelligence methods such as machine learning, computer vision, and sensor fusion to solve real-world challenges.

Graduates will be well prepared for employment across a wide range of industries including advanced manufacturing, aerospace, defence, healthcare, automotive, energy, agriculture, and logistics, as well as emerging fields such as human–robot interaction and ethical AI. Typical graduate roles include Robotics Engineer, Machine Learning Engineer, Automation Specialist, Computer Vision Engineer, Research Scientist, and Systems Integration Consultant. The course also provides a strong foundation for doctoral study and research careers, building analytical and investigative capabilities aligned with UKRI and EPSRC priority areas.

Employability is embedded throughout the course and developed progressively at each level. In the early stages, students will engage in authentic assessments such as lab-based investigations, design reports and technical blogs that mirror real industry deliverables. Later modules introduce more complex, open-ended projects, preparing students for professional practice through problem-based learning, teamwork, and project management. The individual MSc Project enables students to demonstrate professional competence through the design, implementation and evaluation of an advanced robotic or AI system. Reflective practice, critical evaluation and communication skills are integrated across assessments to develop confident, work-ready graduates.

The course team works closely with employers and professional bodies to ensure the curriculum remains current and relevant. Industry professionals contribute through guest lectures, live briefs, seminars, and networking events coordinated with the Graduate Success Team and the School’s Industry Advisory Board. Students will also benefit from engagement with the University’s two active research centres – the Centre for Communication Technology and the Cyber Security Research Centre – both of which maintain collaborative links with industry through Knowledge Transfer Partnerships (KTPs), consultancies, and research projects. These relationships provide valuable opportunities for students to gain real-world insight and contribute to applied research.

Enterprise and innovation are integral to the course philosophy. Students are encouraged to consider entrepreneurial pathways, including freelancing, consultancy, and technology start-ups. Through the University’s Accelerator and Student Enterprise initiatives, students will access workshops, mentoring and business start-up guidance relevant to the robotics and AI sector. Modules embed enterprise skills such as ideation, commercial awareness, and innovation management, supporting students who wish to turn project outcomes into viable products or services.

Students will also participate in the University’s Gateway to Success programme, which helps them articulate and evidence their skills, experience, and professional growth. The Living CV initiative allows students to continuously reflect on and update their competencies throughout the course, supporting applications for employment, placements, and further study. The Graduate Success Team provides individual career coaching, CV clinics, mock interviews and employer networking sessions to ensure every student can plan and manage their career progression effectively.

Overall, the MSc Robotics and AI programme provides a holistic framework for professional development that combines technical depth, research capability, employability, enterprise and lifelong learning, producing graduates who are adaptable, creative and ready to lead in the next generation of intelligent systems industries.

The high demand for Robotics and Artificial Intelligence specialists in business and industry promises wider employability, a broader career path and higher starting salaries.

Careers in robotics include:

• Robotics Control System Engineer
• Electronics Engineer (Robotics)
• Robotic Stems Electronics Engineer
• Software Engineer/Developer – Robotics
• Electro-Mechanical Robotics Engineer
• Robotics Control System Engineer
• Robotics/Computer Vision Engineer
• Engineer in Robotics, Control & Automation Engineer – Robotics
• Graduate Robotics Engineer
• Computer Vision Engineer in Robotics
• Robotic Systems Engineer
• Embedded Software Engineer – Robotics, etc.

Careers in computer systems engineering include:

• Computer/IT Systems and Support Engineer
• Systems Engineering
• IT System Support Engineer
• Software Support Engineer
• Application Support Consultant
• System Support Engineer
• Graduate System Support Engineer

You'll be required to have:

• a minimum of a lower second-class (2.2) honours degree (or equivalent) in electrical and electronic engineering, robotics, mechatronics, computer science or systems engineering, computing, computer networking, physics or a closely related discipline.

Graduates from other disciplines who have extensive relevant work experience will be considered on an individual basis, and may be asked to attend an interview.

Programming skills with at least one of the popular languages (e.g. Java, Python, C/C++) and computational tools such as Matlab/Maple as well as some familiarity with electronic devices would be highly desirable.