AI and Machine Learning (MSc)

Programme Specification

London school of Innovation

Artificial Intelligence is revolutionising industries and pushing the boundaries of innovation. From healthcare breakthroughs to financial transformations, machine learning is solving the toughest challenges of our time.

This programme empowers you to seize the full potential of AI. Through cutting-edge insights and practical tools, you will learn to create and scale intelligent systems that can redefine entire sectors and drive real impact. Whether you're a seasoned professional or ready to step into this game-changing field, it offers an inspiring journey into the heart of AI.

Master key concepts like neural networks and machine learning, and position yourself to lead in a world shaped by AI, ready to drive change with confidence and creativity.

Qualification Level Framework Credits Hours HECos Code
Postgraduate FHEQ - L7 CATS 180, ECTS 90 1800 artificial intelligence (100359)

Award Information

Award Title AI and Machine Learning (MSc)
Type of Qualification Master's Degree
Awarding Body London School of Innovation (subject to New DAPs)
Teaching Institute London School of Innovation
Exit Award(s) PgDip (120 credits), PgCert (60 credits)

Programme Details

Language Of Programme Applicable FHEQ Descriptor Applicable Subject Benchmark Statement
English FHEQ Level 7 descriptor QAA Computing Subject Benchmark Statement

Entry Criteria

Requirement Type Details
Academic Qualifications An undergraduate degree or equivalent in any of the following:
Management/Business studies
STEM (science, technology, engineering or math)
Required Work Experience At least 4 years' commercial experience in a technical or managerial position involving intellectually challenging day-to-day tasks.
English Language IELTS Level [Min. 6 ]

Programme Aims and Learning Outcomes

Skill Development In the age of automation, having a broad skill set is key to thriving. This course doesn't just cover theory; it equips you with practical skills you can use right away. You will learn to design machine learning models, handle complex data, and create AI systems that make autonomous decisions. The curriculum ensures you build both technical know-how and strategic insight. You will sharpen your skills in Python, TensorFlow, and cloud computing, while also critically examining AI’s ethical challenges. Collaboration is another core part of the programme, pushing you to team up with peers from varied backgrounds to tackle real-world issues. By the end, you will have the hands-on expertise to face any AI challenge head-on.
Real World Application AI's impact lies in its real-world applications. This course shifts AI and machine learning from theory to practice. You will delve into how AI is reshaping industries like healthcare, finance, manufacturing, and transport. With hands-on experience, you will apply AI models to real business problems, learning the realities of implementation. Through live case studies and simulations, you will grasp both the 'how' and the 'why' of AI. Whether improving supply chains, forecasting markets, or diagnosing illnesses, you will learn to deploy AI for real results. By the end, you won’t just understand AI—you will be ready to deliver impactful solutions across sectors.
Career Prospects Artificial intelligence is not just the future, it is the present. AI experts are in high demand, with businesses across the globe seeking leaders who understand the potential of this technology. Completing this course positions you at the forefront of this dynamic field. Whether you are aiming to advance within your current organisation or seeking to pivot into a new sector, the opportunities are vast and growing. From becoming a machine learning engineer or AI consultant to leading digital transformation projects, this course opens the door to a variety of career paths. You will be equipped to take on senior roles in tech companies, multinational corporations, and innovative startups. For those with an entrepreneurial spirit, the knowledge and skills you gain here can help you launch your own AI-driven ventures. This programme is not just about learning AI, it is about shaping the future of your career.
Personal Growth Learning AI is not just about technical expertise, it is about expanding how you think. This programme will challenge and inspire you, pushing you to solve complex problems with creativity and confidence. You will sharpen your critical thinking, learn how to make data-driven decisions and develop the resilience needed to lead in a fast-paced, tech-driven world. Beyond the technical skills, this course will help you grow as a leader. You will learn how to communicate complex ideas to both technical and non-technical audiences, positioning yourself as a trusted expert in any room. The diverse perspectives you encounter during the programme will also enhance your ability to work in cross-functional teams and navigate the complexities of modern organisations. Completing this course will not just make you more skilled, it will make you more adaptable, confident, and ready for whatever comes next.
FHEQ Level 7 (Threshold Academic Standard)
Quality Descriptor Programme outcome(s)
Domain knowledge
Systematic understanding of knowledge in their field
Exhibit a systematic understanding of the knowledge underpinning AI and machine learning, and the distinction between the two and other related fields such as automation, and key concepts, including neural networks, deep learning, reinforcement learning.
Problems and new ideas in the field
Critical awareness of current problems or new ideas in their field
Demonstrate a critical awareness of contemporary developments in the fields of AI and machine learning, including generative AI, the benefits and limitations of such systems, and issues related to their application in different contexts, such as the professional, legal, and ethical.
Techniques
Comprehensive understanding of applicable techniques in their field
Have a clear and comprehensive understanding of the appropriate techniques that can be applied to identify problems and provide solutions in the fields of applied AI and machine learning, including qualitative and quantitative research, data collection and processing, algorithm design, and model evaluation.
Originality
Show some originality in applying knowledge
Synthesise knowledge and new insights on applied AI and machine learning in a novel way that shows a comprehension of how knowledge in the fields are advanced, including by designing and executing practical research projects that show how intelligent systems and machine learning can deliver change.
Knowledge discernment
Practical understanding of how to create and interpret knowledge in their field using established techniques of research and enquiry
Evaluate the benefits and limitations of different practical methods in the fields of applied AI and machine learning in the creation and interpretation of new insights, including bias, transparency, and low quality data and what should be done with regards to these for the discernment of knowledge.
Research critique
Conceptual understanding so they can criticise and evaluate the current research papers in their field, and the current methodologies and techniques.
Critique current problems and new insights within applied AI and machine learning, including in current literature, and the methodologies and paradigms used, to propose new theories and possible solutions, such as on data privacy and infrastructure.
BCS Level 7 (Subject Benchmark Statement)
Quality Descriptor Programme outcome(s)
Intellectual skills
Analyse, apply and critically evaluate concepts, principles and practices.
Examine, use, and appraise knowledge within the fields of AI and machine learning to challenge existing understanding and practice.
Problem-solving
Well-developed skills in critical thinking, research design, judgement and problem-solving, leading to the ability to create effective computational artefacts, given complex or open constraints, with a high degree of autonomy.
Demonstrate a high degree of independence and perceptiveness in evaluating and identifying problems related to AI and machine learning, including data scarcity, to design suitable frameworks that balance and makes use of diverse and appropriate methods to generate suitable computational artefacts.
Practical computing skills
Apply computing techniques, as appropriate to the area of study, within complex or unpredictable scenarios, in a systematic manner, making appropriate decisions given incomplete or missing data.
Select computing techniques that are relevant and effective in overcoming both existing and foreseeable challenges in the production of solutions to problems, including evaluating effective methods of data collection, such as APIs and public data sets, and addressing challenges, such as cost and time.
Autonomy and self-direction
Demonstrate some self-direction in learning and attainment, tackling and solving problems, and approaching and implementing tasks and activities proactively and effectively.
Plan what needs to be done in order to problem solve with a high level of autonomy, foreseeing and proposing effective solutions that are likely to arise in various contexts, such as the professional, legal, and ethical, so that objectives can be delivered.
Professional practice
Identify appropriate practices in complex and unpredictable professional environments in the work that they undertake, and perform work within a professional, legal and ethical framework – including data management and use, security, equality, diversity and inclusion (EDI) and sustainability.
Demonstrate a commitment to keep up-to-date with the latest issues and best practice with the fields of applied AI and machine learning, such as regulation, so that reflection can be used to effectively identify and apply solutions to problems that may prevent objectives from being delivered.
Professional communication
Communicate their work to specialist and non-specialist audiences.
Communicate complex applied AI and machine learning concepts effectively to diverse audiences, including those from outside the field, using clear language tailored to the contextual understanding of listeners.

Programme Modules

To qualify for the Master's Degree (MSc) you must achieve 180 CATS credits from the following.

Optional

Title Code Credits Level Teaching System Duration
Deep Learning (DL71) DL71 30 FHEQ - L7 Technical 30 4 Months Spec
Interaction Design for User-Centred Systems (VS71) VS71 15 FHEQ - L7 Technical 15 4 Months Spec
Advanced Computer Vision (CV72) CV72 30 FHEQ - L7 Technical 30 4 Months Spec
Advanced Leadership for Innovation (DA71) DA71 15 FHEQ - L7 Professional 15 4 Months Spec

Core

Title Code Credits Level Teaching System Duration
Applied Machine Learning 1 (ML71) ML71 30 FHEQ - L7 Technical 30 4 Months Spec
AI in Business: Strategies and Implementation (MA71) MA71 15 FHEQ - L7 Professional 15 4 Months Spec
Master's Final Project (FP10) FP10 60 FHEQ - L7 Postgraduate Final Project 60 4 Months Spec
LLM Engineering and Integration (LL71) LL71 30 FHEQ - L7 Technical 30 4 Months Spec

Mode(s) of Study

Students can choose either of the following. Entry points can be at the beginning of any semester in the School's academic calendar (February, June or October) where an entry cohort is provisioned. For each semester of each year, the School's website will set out whether an entry cohort for this programme is scheduled.

Please view the programme page on our website for the latest information.

Title Duration Location Asynchronous learning Synchronous learning
1 Full-time Blended Learning
Ideal for students who can fully commit to weekly classes (in-person or remotely) and willing to immerse in full-time education.
12 months
Students can begin in any of our standard semesters, on the first of February, June or October, and complete the programme in 3 consecutive semesters, studying 60 credits per semester.
On-campus or online. All modules delivered at LSI will allow remote attendance in order to promote flexibility, access, and participation. Our advanced, AI-enhanced online learning platform elevates student engagement. It features the Interactive Knowledge Graph (IKG) for efficient, engaging knowledge attainment, alongside AI-guided activities like quizzes, discussions, Q&A, and immediate feedback on practical tasks, supplementing synchronous classes. Rather than conventional lectures, our academic staff and subject-matter experts focus on interactive methods in live classes, facilitating problem-solving, role-play, case studies, discussions, and teamwork. Students attend these weekly sessions to engage in structured social learning. Our hybrid approach blends the convenience of digital resources with the motivation of human interaction.
2 Part-time Blended Learning
Ideal for students busy with work/life commitments, but who can commit to weekly classes (in-person or remotely).
24 months
Students can begin in any of our standard semesters, on the first of February, June or October, and complete the programme within 2 years. Per semester, they typically study 30 credits. Each taken module should begin and end within the same semester, except the final project, which can be stretched across two.
On-campus or online. All modules delivered at LSI will allow remote attendance in order to promote flexibility, access, and participation. Our advanced, AI-enhanced online learning platform elevates student engagement. It features the Interactive Knowledge Graph (IKG) for efficient, engaging knowledge attainment, alongside AI-guided activities like quizzes, discussions, Q&A, and immediate feedback on practical tasks, supplementing synchronous classes. Rather than conventional lectures, our academic staff and subject-matter experts focus on interactive methods in live classes, facilitating problem-solving, role-play, case studies, discussions, and teamwork. Students attend these weekly sessions to engage in structured social learning. Our hybrid approach blends the convenience of digital resources with the motivation of human interaction.

Credit Structure

The following are examples only. For more information, please read the school's registration regulations .

Full-time Blended Learning Example 1 (total of 180 credits)
Taught modulesFinal project
Year 1 Semester 160
Year 1 Semester 260
Year 1 Semester 360
Total12060
Full-time Blended Learning Example 2 (total of 180 credits)
Taught modulesFinal project
Year 1 Semester 160
Year 1 Semester 23030
Year 1 Semester 33030
Total12060
Part-time Blended Learning Example (total of 180 credits)
Taught modulesFinal project
Year 1 Semester 130
Year 1 Semester 230
Year 1 Semester 330
Year 2 Semester 130
Year 2 Semester 230
Year 2 Semester 330
Total12060

Teaching Systems

Name Workload Assessment Modules
Technical 30

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

36h πŸ•‘ Concept learning (knowledge graph)
18h πŸ•‘ AI formative assessment
1.5h πŸ•‘ Introductory lecture
27h πŸ•‘ Workshop/Lab Sessions
64h πŸ•‘ Summative assessment
153.5h πŸ•‘ Independent reading, exploration and practice
Total: 300 hours
60% I T P K Technical Analysis and Solution Assessment
40% I K P Invigilated Exam
DL71, ML71, CV72, LL71
Professional 15

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

18h πŸ•‘ Concept learning (knowledge graph)
9h πŸ•‘ AI formative assessment
61.5h πŸ•‘ Independent reading, exploration and practice
9h πŸ•‘ Case Study Review
13.5h πŸ•‘ Workshop/Lab Sessions
24h πŸ•‘ Summative assessment
1.5h πŸ•‘ Introductory lecture
13.5h πŸ•‘ AI Roleplay
Total: 150 hours
50% I T K P Simulation and Role Playing Assessment
50% I K P Invigilated Exam
MA71, DA71
Postgraduate Final Project 60

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.

276h πŸ•‘ Independent reading, exploration and practice
4.5h πŸ•‘ One-to-one project supervision meeting
1.5h πŸ•‘ Introductory lecture
18h πŸ•‘ Concept learning (knowledge graph)
300h πŸ•‘ Individual Research
Total: 600 hours
50% I K P Research Module Assessment: Final Report
15% K I P Research Module Assessment: Presentation
35% T I K P Research Module Assessment: Artefact
FP10
Technical 15

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

18h πŸ•‘ Concept learning (knowledge graph)
9h πŸ•‘ AI formative assessment
1.5h πŸ•‘ Introductory lecture
13.5h πŸ•‘ Workshop/Lab Sessions
18h πŸ•‘ Individual or group assignments
30h πŸ•‘ Summative assessment
51h πŸ•‘ Independent reading, exploration and practice
9h πŸ•‘ Case Study Review
Total: 150 hours
40% I K P Invigilated Exam
60% I T P K Technical Analysis and Solution Assessment
VS71

Teaching and Learning Methods

Each module will specify its teaching system, including weighted teaching and learning activities, which will be drawn from the following pool as appropriate.

Name Description
1 Introductory lecture

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.

2 Concept learning (knowledge graph)

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.

3 AI formative assessment

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.

4 Case Study Review

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.

5 AI Roleplay

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.

6 Workshop/Lab Sessions

The 9 weekly sessions following the introduction (weeks 2 to 10) will be dedicated to teaching the contents of the module during interactive workshops. These sessions will complement the theory with practice, experience or analysis. Their purpose is to advance the student's cognition from 'knowledge' to 'understand' and 'apply'.

Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning, and may include hands-on project work, group discussions or debates, roleplay, simulation, case study or other presentation, 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. There will be an opportunity also for Q&A in every session.

7 Live lecture

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.

8 Individual or group assignments

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.

9 Seminars

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.

10 Independent reading, exploration and practice

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.

11 Individual Research

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.

12 Summative assessment

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.

13 One-to-one project supervision meeting

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.

Marking Criteria

The following grid sets out the School’s marking criteria for FHEQ - L7.

Outcome Expectation Distinction (70 - 100%) Merit (60 - 69%) Pass (50 - 59%) Fail (0 - 49%)
Knowledge and Understanding Systematic and critical understanding of relevant knowledge, concepts, new insights, and developments in the discipline, including within current literature, and also incorporating interrelationships with other relevant disciplines. Outstanding systematic and critical understanding of relevant knowledge, concepts, new insights, and developments in the discipline, including within current literature, and also incorporating interrelationships with other relevant disciplines. Very good systematic and critical understanding of relevant knowledge, concepts, new insights, and developments in the discipline, including within current literature, and also incorporating interrelationships with other relevant disciplines. Satisfactory systematic and critical understanding of relevant knowledge, concepts, new insights, and developments in the discipline, including within current literature, and also incorporating interrelationships with other relevant disciplines. Little to no systematic and critical understanding of relevant knowledge, concepts, new insights, and developments in the discipline, including within current literature, and also incorporating interrelationships with other relevant disciplines.
Intellectual Skills Ability to analyse, apply, and critically evaluate knowledge, techniques, and practices, in unpredictably complex contexts and to existing discourses and methodologies with intellectual skill and some originality. Exceptional analysis, application, and critical evaluation of knowledge, techniques, and practices in unpredictably complex contexts and to existing discourses and methodologies, with a high-level of intellectual skill and some originality. Sound analysis, application, and critical evaluation of knowledge, techniques, and practices in unpredictably complex contexts and to existing discourses and methodologies, with very good intellectual skill and some originality. Acceptable analysis, application, and critical evaluation of knowledge, techniques, and practices in unpredictably complex contexts and to existing discourses and methodologies, with satisfactory intellectual skill and limited originality. Little to no analysis, application, and critical evaluation of knowledge, techniques, and practices in unpredictably complex contexts and to existing discourses and methodologies, with a very narrow level of intellectual skill and no originality.
Technical/Practical Skills Comprehensive and critical understanding and organisation of specialist techniques and advanced methodologies in the discipline, including those related to critical thinking, specialist projects, research design, problem-solving, and techniques, and a practical understanding of how they should be selected and used to interpret incomplete knowledge and create effective artefacts. Outstanding critical understanding and organisation of specialist techniques and advanced methodologies in the discipline, including high-level critical thinking, specialist projects, research design, problem-solving, and techniques, and a thorough practical understanding of how they should be selected and used to interpret incomplete knowledge and create effective artefacts. Very good critical understanding and organisation of specialist techniques and advanced methodologies in the discipline, including sound critical thinking, specialist projects, research design, problem-solving, and techniques, and a very good practical understanding of how they should be selected and used to interpret incomplete knowledge and create effective artefacts. Acceptable critical understanding and organisation of specialist techniques and advanced methodologies in the discipline, including satisfactory critical thinking, specialist projects, research design, problem-solving, and techniques, and acceptable understanding of how they should be selected and used to interpret imcomplete knowledge and create effective artefacts. Limited or no critical understanding and organisation of specialist techniques and advanced methodologies in the discipline, including little or no critical thinking, , specialist projects, research design, problem-solving, and techniques, and a limited to no practical understanding of how they should be selected and used to interpret incomplete knowledge and create effective artefacts.
Professional/Transferable Skills Ability to show awareness, autonomy and self-direction in development and learning, tackling and solving complex problems, approaching and implementing tasks in diverse and unpredictable contexts, including professional, legal and ethical, critically evaluating own and others capabilities, and with an ability to communicate work to specialist and non-specialist audiences. Exceptional ability to show awareness, autonomy and self-direction in development and learning, taking a thorough proactive approach to tackling and solving complex problems, approaching and implementing tasks in diverse and unpredictable contexts at a very high level, including professional, legal and ethical, exceptional critical evaluation of own and others work, and with a thorough ability to communicate work to specialist and non-specialist audiences Very good ability to show awareness, autonomy and self-direction in development and learning, taking an effective and proactive approach in tackling and solving complex problems, approaching and implementing tasks in diverse and unpredictable contexts at a very good level, including professional, legal and ethical, very good critical evaluation of own and others work, and with a very good ability to communicate work to specialist and non-specialist audiences. Satisfactory ability to show awareness, autonomy and self-direction in development and learning, taking a good approach in tackling and solving complex problems, approaching and implementing tasks in diverse and unpredictable contexts at an acceptable level, including professional, legal and ethical, satisfactory critical evaluation of own and others work, and with a good ability to communicate work to specialist and non-specialist audiences. Little to no ability to show awareness, autonomy and self-direction in development and learning, taking a limited or no proactive approach in tackling and solving complex problems, approaching and implementing tasks in diverse and unpredictable contexts at a very limited level, including professional, legal and ethical, little to no critical evaluation of own and others work, and with little to no ability to communicate work to specialist and non-specialist audiences.

Assessment Formats

Each module will specify its weighted summative assessment formats which will be drawn, as appropriate, from the following pool.

Name Outcomes Modules
1 Technical Analysis and Solution Assessment
This assessment requires students to develop a solution to a complex problem within a simulated domain, followed by a detailed analysis and reflection on their design and its theoretical underpinnings. The aim is to assess students' abilities to design practical solutions, critically analyse their work, and articulate their understanding of the technical and theoretical aspects of the module.
I T P K DL71, ML71, VS71, CV72, LL71
2 Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity. The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills. The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasonedΒ judgements with respect to the problem. To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.
I K P DL71, ML71, MA71, VS71, CV72, LL71, DA71
3 Simulation and Role Playing Assessment
This assessment requires students to engage in AI-assisted simulations or role-playing scenarios that mirror real-world professional situations. It evaluates their practical knowledge, decision-making, and adaptability. Students are given a detailed brief outlining a dynamic, evolving problem involving various issues like business, legal, professional, and ethical considerations. They must interpret the situation, consult relevant sources, and present a solution based on their knowledge from the module. At the start of the module, students attend a workshop on effective participation in simulations aligned with the learning outcomes. Throughout the term, they practice through formative simulations, receiving feedback from AI, peers, staff, and their module leader.
I T K P MA71, DA71
4 Research Module Assessment: Final Report
Students will be required to submit a final report. The purpose of the final report is to assess how students conducted independent research, applied critical thinking, and demonstrated a systematic understanding of their subject of study within computer science in producing their artefact. The final report also allows students to showcase their originality in applying knowledge and techniques in producing the artefact, as well as their proficiency in utilizing established research methods and tools. It provides an opportunity for students to communicate their research findings, interpretations, and conclusions effectively, both to specialist and non-specialist audiences. Students will have a workshop in the module on how to prepare, structure, and submit a final report, and your module leader will be able to provide you with further support whilst you work on it. You will be shown examples of successful and unsuccessful final reports. You will also have the opportunity to present your work during your programme modules and receive peer and tutor feedback. Throughout the programme, students will regularly receive formative assessment tasks and feedback opportunities to gain actionable feedback (from self, peers and staff) on their own work to indicate how to improve future work and learn how to give constructive feedback to other people.
I K P FP10
5 Research Module Assessment: Presentation
Students must deliver a presentation on their artefact. The purpose of the presentation is to assess their ability to communicate their research findings, methodologies, and implications effectively to a diverse audience in a concise, professional, and engaging manner. The presentation stems from the research problem statement set out in the Final Project proposal, which require students to come up with a practical solution in the form of an artefact that uses the implementation lifecycle. It is envisaged that the Final Project will require students to apply the tools and architectures they have learnt in their programme modules to diagnose problems, undertake requirements analyses, and produce an artefact. This presentation will require them to expand on how they strategized and overcame practical, professional, ethical and other issues and constraints they may have come across. Students will have a workshop in the research module on how to make an effective presentation, and their module leader will be able to provide them with further support whilst they work on their project. They will have the opportunity to present their work in their programme modules and receive peer and tutor feedback. Throughout such programme modules, students will also regularly receive formative assessment tasks and feedback opportunities to gain actionable feedback (from self, peers and staff) on their own work to indicate how to improve future work and learn how to give constructive feedback to other people.
K I P FP10
6 Research Module Assessment: Artefact
For the research project, students must submit an artefact that meets the problem statement that they articulate in their final report. You will have a workshop on the module on how to ideate and design practical solutions for problems using an implementation lifecycle and how to succeed with your project. Throughout the programme, in particular, their programme modules, students will regularly receive formative assessment tasks and feedback opportunities to gain actionable feedback (from self, peers and staff) on their own work to indicate how to improve future work and learn how to give constructive feedback to other people.
T I K P FP10

Programme Contacts

Role Description Name Email
Programme Director Oversees the overall direction and integrity of the programme.

Approval

Core > Programme spec > Msc