UDARIRFY - BEng (Hons) Artificial Intelligence and Robotics (including foundation year)

This course is designed to address engineering skills-shortage in intelligent hardware/software systems and applied robotics as an enabler to ongoing AI revolution across a range of sectors such as autonomous vehicles, aviation, construction, telecommunication, software systems, military and consumer electronics.

The courses provide experience in the design and development of robotics systems, from the initial hardware design and development, to writing and implementing advanced AI software to enable robots to accomplish tasks in the real world. The course is structured with a strong emphasis on practical work to build your skills and confidence in the usage and application of contemporary Robotics and AI technologies. Students will learn the elements needed to build robots, from simple electronic circuitry, software engineering and control, to high-level robot intelligence and will learn how to develop robotic systems and bring the learning to life.

This exciting course:

• Gives students a good grounding in robotics and the application of artificial intelligence.
• It enables students to design, build and deploy various types of intelligent robotic systems.
• Offers students excellent employment prospects through hands-on hardware and software experience closely linked to industrial practice.
• Has a range of stimulating optional modules for students to choose as they progress through their studies.

The course with balanced theory and hands-on practice prepares motivated and academically minded graduates for entering postgraduate studies such as MSc and/or MPhil/PhD. The final year project titles are continuously updated in consultation with our industrial partners to reflect recent technological advances and increase employability opportunities for our graduates.
The teaching and assessment related activities for this course are delivered over 30 weeks of formal scheduled contact time. Modules are mainly delivered through a combination of lectures (1 hour/ week), tutorials/lab-based workshop (2 hours/ week) sessions, and blended learning. Teaching materials such as lecture notes and other support learning materials are accessible via the university VLE network (e.g. WebLearn, Blackboard) and School’s network facilities (e.g. web server).
Appropriate blended learning approaches and technologies, such as, the University’s VLE, and specialized engineering labs are used to facilitate and support student learning to: (i) deliver course, (ii) content, (iii) encourage active learning, (iv) provide formative and summative assessments, and prompt feedback, & (v) enhance student engagement and learning experience.

The course is supported by several specialized laboratories. When studying any of our specialized modules you will spend a considerable part of the module in these laboratories, providing an opportunity to practice what you learn in your lectures and seminar sessions and (using an industry-standard simulation package) investigate, design, implement, test and document a variety of industry relevant examples of hardware, software and robotic systems. These sessions are performed individually or as part of a group.

You will have opportunities to enhance the skills that employers in the industry are looking for and gain real experience through placements on client-driven projects – working with business and industry through our work-related learning module. The course will also help you develop interpersonal, teamwork and engineering skills alongside commercial, ethical and environmental awareness.

The BEng course in AI and Robotics exemplifies the University’s strategy for learning, teaching, and assessment by providing a student-centered, practical-oriented, and varied learning experience that prepares students for the challenges and opportunities in the field of AI and robotics.

Course Aims

• To prepare students for a career in robotics or an allied discipline.
• To develop students with a thorough understanding of the technologies, techniques and theories underpinning effective design, realization and development of intelligent autonomous ‘robotic’ systems, and the practical skills used in their creation.
• To produce graduates with a sound understanding of the tools and techniques used to support the design and development process behind systems with embedded intelligence.
• To produce practitioners with the ability and experience to tackle the cradle-to-grave process of robotics development, from requirements capture to testing and delivery.
• To produce graduates with a clear sense of user focused design and who possess a range of tools and techniques to uncover and define user requirements.
• To produce graduates with the capacity to proactively solve problems.
• To produce graduates with strong communication skills, who can explain their concepts to a diverse audience using a range of media.
• To prepare students for progression to further study and/or research into robotics or related disciplines.
• To develop students' independent study skills and prepare them for lifelong learning experiences.

UL0. Demonstrate confidence, resilience, ambition and creativity and will act as inclusive, collaborative and socially responsible practitioners/professionals in their discipline.

LO1 Apply knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems. Some of the knowledge will be at the forefront of the particular subject of study.

LO2 Analyze complex problems to reach substantiated conclusions using first principles of mathematics, statistics, natural science and engineering principles.

LO3 Select and apply appropriate computational and analytical techniques to model complex problems, recognizing the limitations of the techniques employed.

LO4 Select and evaluate technical literature and other sources of information to address complex problems.

LO5 Design solutions for complex problems that meet a combination of societal, user, business and customer need as appropriate. This will involve consideration of applicable health and safety, diversity, inclusion, cultural, societal, environmental and commercial matters, codes of practice and industry standards.

LO6 Apply an integrated or systems approach to the solution of complex problems.

LO7 Evaluate the environmental and societal impact of solutions to complex problems and minimise adverse impacts.

LO8 Identify and analyze ethical concerns and make reasoned ethical choices informed by professional codes of conduct.

LO9 Use a risk management process to identify, evaluate and mitigate risks (the effects of uncertainty) associated with a particular project or activity.

LO10 Adopt a holistic and proportionate approach to the mitigation of security risks.

LO11 Adopt an inclusive approach to engineering practice and recognize the responsibilities, benefits and importance of supporting equality, diversity and inclusion.

LO12 Use practical laboratory and workshop skills to investigate complex problems.

LO13 Select and apply appropriate materials, equipment, engineering technologies and processes, recognizing their limitations.

LO14 Discuss the role of quality management systems and continuous improvement in the context of complex problems.

LO15 Apply knowledge of engineering management principles, commercial context, project and change management, and relevant legal matters including intellectual property rights.

LO16 Function effectively as an individual, and as a member or leader of a team.

LO17 Communicate effectively on complex engineering matters with technical and non-technical audiences.

LO18 Plan and record self-learning and development as the foundation for lifelong learning/continuing professional development (CPD).

The course design refers to Quality Assurance Agency (QAA)’s Subject Benchmark Statements in Engineering set out in the UK Quality Code for Higher Education.

https://www.qaa.ac.uk/docs/qaa/subject-benchmark-statements/sbs-engineering-15.pdf?sfvrsn=f99df781_10

A range of assessment methods (class tests, theory and practical examinations, coursework through logbook/ case-study and laboratory report/ poster / artefact, viva) is employed throughout the course. The method of assessment and marking criteria for each module at each level is clearly described in the individual ‘Module Guide’ which is made available to the students at the start of the semester (via WebLearn - VLE). Every module (core as well as options) has a VLE presence providing students with comprehensive learning/teaching material including Workshops exercises. Module leaders use this site regularly to communicate with their students including providing general feedback, guidelines on how to write technical report/ effective presentations and keeping logbooks. One of the core modules at Level 4 has a mandatory formative/summative assessment element "Learning reflection essay" to initiate and induct students to reflective learning to develop effective and SMART study plans for all modules.

Students are provided with opportunities to develop an understanding of and the necessary skills to demonstrate good academic practice. Particularly, students will be encouraged to complete weekly tutorials/tests and laboratory exercises as well as periodic formative progress tests to enhance their learning. During laboratory sessions students receive ongoing support and feedback on their work to promote engagement and provide the basis for tackling the summative assessments.

The volume, timing and nature of assessment enable students to demonstrate the extent to which they have achieved the intended learning outcomes. Formative and summative feedback are be provided using a variety of methods and approaches, such as online, one to one and in groups on the submitted work, at various points throughout the teaching period and in line with University's policy on assessment and feedback.

Students will be referred to the University’s guidance on the use of Artificial Intelligence (AI) to become acquainted with it.

The course includes a 15-credit module on Work Related Learning (FC5W51) at Level 5. Accredited work-related learning is an integral part of the course. This can take the form of a placement (sometimes referred to as an internship) or a work-based project. The Careers and Employability team of the University provides advice on all stages of the selection process including developing CV, completing application forms, preparing for interview or online assessment. The School of Computing and Digital Media's World of Work (WOW) Agency (a.k.a. Employment Outcomes Team - EOT), working closely with the module /course leader, offers opportunities to enhance employability skills, gain real experience through placements into real client-driven projects - working with business and industry. In addition to the work-related core module FC5W51 Work Related Learning II, students can apply for a sandwich year at the end of Level 5 during the course.

Part-time Structure:

Level 3 to 5 all modules are core. However, at level 6 students will need to do two option modules from five. The default options are CT6053 and CT6064.

Degree award:
BEng (Hons) degree is awarded according to the following additional course regulations to meet accreditation requirements of IET. Failing to achieve this will result in a lesser award, such as the BSc (Hons) degree, according to the University's academic regulations. Although the student will be enrolled on the BEng (Hons) programme, the final award will be determined at the end of the course.

• The proportion of failed modules deemed to be completed will be less than or equal to 20 credits in each year;
• The minimum acceptable progression marks will be greater than or equal to 30 %; and
• Degree classifications will include all modules in the final 2 years (total of 240 credits at levels 5 and 6) using the standard university weightings;
• Final year project must be passed (not just complete)
• Re-assessment and re-takes will be capped at 40%

Direct entry:
Direct entry applicants entering final year (Level 6) must have Level 5 (or equivalent) in relevant area. Such applicants’ degree award may be BSc (Hons) rather than BEng (Hons) deepening on whether their previsions studies were accredited by IET/BCS.

Direct entry applicants who do not meet this entry requirement will ONLY be accepted to the second year (Level 5) of the course if the applicant has achieved 240 credits from levels 4 and 5 (or equivalent) in appropriate modules covering the required learning outcomes.

All direct entry students are required to attend an interview with the course leader or his/her nominee prior to being made an offer.

Certificate of Higher Education: 120 credits at Level 4 as per the course structure in section 22.

Diploma of Higher Education: 240 credits with minimum of 120 at Level 5 as per the course structure in section 22.

BSc: 300 credits with max. 120 at credits at Level 4, 120 at Level 5 and min 60 credits at Level 6 as per the course structure in section 22.

BSc (Hons): 360 credits with max. 120 credits at Level 4, min. 90 credits at Level 6 as per the course structure in section 23 BUT “Course Specific Regulations” are not satisfied.

BEng (Hons): 360 credits with max. 120 credits at Level 4, min. 90 credits at Level 6 as per the course structure in section 23 AND “Course Specific Regulations” are satisfied.

All modules are based on lectures (1 hour) followed immediately by small-group tutorials, Laboratory / Workshops, individual and group case studies (2 hours). These ‘after-lecture’ activities are an important part of a student’s learning process. It is during these activities that students have an opportunity to reflect on their learning. For each activity, students are expected to keep a logbook for their workshops giving a full account of the problems, methods of solutions, results and conclusions.

Students are expected to start their ‘Personal Development Plan (PDP) in Level-4 and to complete this during Level 6 project where students are assigned to a supervisor with whom they communicate on a weekly basis throughout the year.

Output standards set out by Engineering Council’s UK Standard for Professional Engineering Competence (UK-SPEC) (4th ed) ensuring threshold academic standard.

https://www.engc.org.uk/media/3410/ahep-fourth-edition.pdf

AI and Robotics are interdisciplinary fields that draw from computer science, mathematics, and engineering. This makes them relevant to a wide range of applications and industries. Job search on uk.Indeed.com on 14th October 2023, shows the following vacancies: 2,679 robotics, 1,816 in AI engineers, 18,327 in control engineers, 682 in IoT, 1,345 in sensors engineers, 17,555 in networking, and 5,731 in AI. Job prospects in these fields are likely to continue growing as technology evolves and becomes increasingly integrated into various aspects of our lives.

Industries spanning healthcare, finance, manufacturing, and technology now rely on highly skilled engineers to create and deploy AI systems, autonomous robots, and cybernetic solutions. This has created an increasing demand for graduates equipped with expertise in Artificial Intelligence (AI) and Robotics.

This course provides the multi-disciplinary knowledge and skills students will need to meet the demand for experts in robotics and AI. Graduates of the programme will be well equipped for careers in AI and Robotics in a range of industries and SMEs, from advanced manufacturing to oil and gas exploration, nuclear energy to railways and automotive, healthcare to defense. Upon graduation, students will be able to unlock promising career opportunities in diverse industries, including robotics, automation, manufacturing, aerospace, systems analyst, and artificial intelligence.

This course is also excellent preparation for further studies or research by pursuing MSc or PhD.

Upon graduation, you will be well-prepared to pursue rewarding and successful careers in diverse fields, including robotics, automation, manufacturing, aerospace, systems analysis, security, and artificial intelligence. Furthermore, you will be equipped to pursue further academic study at an advanced level, such as pursuing MSc or PhD research. In fact, this course will be a gateway to our MSc in Robotics with AI.

In addition to the University's standard entry requirements, you should have:

• a minimum of 32 UCAS points from an A Level or equivalent Level 3 qualification ( e.g. BTEC National, Subsidiary or Extended Diploma).
• English Language and Mathematics at grade C/4 or above (or equivalent).