| Mode(s) of Study | Code | CATS Credits | ECTSCredits | Framework | HECoSCode |
|---|---|---|---|---|---|
| Full-time blended Part-time blended |
LL71 | 30 | 15 | FHEQ - L7 | artificial intelligence |
Prerequisites and Co-requisites
Learning Outcomes
| Code | AttributesDeveloped | Outcomes |
|---|---|---|
| LO1 | Knowledge and Understanding | Demonstrate comprehensive knowledge of advanced LLM customisation techniques and integration in multi-step interaction workflows. |
| LO2 | Intellectual Skills | Analyse ethical implications and data privacy challenges in human-LLM collaboration and workflow integration. |
| LO3 | Intellectual Skills | Critically evaluate methodologies for managing conversation states and task decomposition in complex multi-turn LLM interactions. |
| LO4 | Technical/Practical Skills | Develop custom LLM agents utilising frameworks like LangChain to autonomously manage tasks with human oversight inclusions. |
| LO5 | Technical/Practical Skills | Implement secure database integrations with LLMs, ensuring data security and privacy through advanced anonymisation techniques. |
Student Workload
The methods of teaching and learning for this module are based on the School's Technical 30 teaching system, consisting of the following activities.
| Activity | TotalHours |
|---|---|
| Introductory lecture | 1.50 |
| Concept learning (knowledge graph) | 36.00 |
| AI formative assessment | 18.00 |
| Workshop/Lab Sessions | 27.00 |
| Independent reading, exploration and practice | 153.50 |
| Summative assessment | 64.00 |
| Total: | 300.00 |
Teaching and Learning Methods
| Activity | Description |
|---|---|
| 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. |
| 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. |
| 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. |
| Workshop/Lab Sessions | 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. |
| 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. |
| 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. |
Assessment Patterns
| Weighting | Format | Outcomes assessed |
|---|---|---|
| 40 |
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 reasoned 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 LO2 I LO3 K LO1 |
| 60 |
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 LO2 I LO3 K LO1 T LO4 T LO5 |
References/Indicative Reading List
| Importance | ISBN | Description |
|---|---|---|
| Core Textbook | 9781040134306 | Atkinson-Abutridy, J. Large Language Models: Concepts, Techniques and Applications. CRC Press, 2024 |
| Core Textbook | 9781501520600 | Campesato, Oswald. Large Language Models: An Introduction. Mercury Learning and Information, 2024 |
| Supplementary Reading | 9789355519658 | Khandare, SS. Mastering Large Language Models. BPB Publications, 2024. |
| Supplementary Reading | 9781040052174 | Thakurm K. Barker, H. Khan Pathan, A. Artificial Intelligence and Large Language Models: An Introduction to the Technological Future. Chapman and Hall/CRC, 2024 |
| Supplementary Reading | 9783031656460 | Kamath, Uday, Kevin Keenan, Garrett Somers, and Sarah Sorenson. Large Language Models: A Deep Dive. Springer, 2024 |
| Supplementary Reading | 9789027259158 | Nolan, Brian and Periñán-Pascual, Carlos. Language Processing and Grammars: The role of functionally oriented computational models. John Benjamins Publishing Company, 2014 |
| Supplementary Reading | 9781803247335 | Rothman, Denis. Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3. Packt Publishing, 2022 |
| Supplementary Reading | 9798868800177 | Amaratunga, T. Understanding Large Language Models: Learning Their Underlying Concepts and Technologies. Apress, 2023 |
| Supplementary Reading | 9780138199197 | Ozdemir, Sinan. Quick start guide to large language models: strategies and best practices for using ChatGPT and other LLMs. Addison-Wesley Professional, 2023 |
| Supplementary Reading | 9781800200883 | Babcock, Joseph and Bali, Raghav. Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, GPT models and more: Harness the power of generative models to create images, text, and music. Packt Publishing, 2021 |
Related Programmes
| Programme | Term | Type | |
|---|---|---|---|
| 1 | MSc AI for Business Transformation | 2 | Optional |
| 2 | MSc Software Technical Leadership | 2 | Optional |
| 3 | MSc AI and Machine Learning | 2 | Core |
Module Approval
| Stage | Version | ApprovalDate | Authority | Chair | Revalidation |
|---|---|---|---|---|---|
| Compliance | dd MMMM yyyy | Academic Board | Dr Paresh Kathrani | ||
| Pre-Teaching | dd MMMM yyyy | Director of Education | Dr Paresh Kathrani |