42174 Artificial Intelligence Studio

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Subject handbook information prior to 2025 is available in the Archives.

Subject level:

Postgraduate

Description

This subject requires the student to plan and execute a substantial research-based project, using their technical and communication skills to learn, design, implement, analyse and evaluate developments in AI. It brings together the complete skill set learned by students in artificial intelligence. Students are required to carry out a specified piece of independent research in a setting that fosters the development of artificial intelligence skills. These skills include the capacity to express a research question: (i) showing how it relates to existing works; (ii) identifying the tools/methods needed to investigate the question; (iii) researching a systematic way; (iv) analysing the results obtained and presenting the outcomes in a clear, coherent, and logically structured report. In short, projects are set out to solve real-world problems in academic research or industry and may include Background and motivation, Data generation/collection, Data processing/cleaning, AI algorithm/analysis, Interpretation and communication of results.

Throughout the project, students are supported by an academic mentor and assessment through written reports, and an oral presentation is in line with expectations from academia and industry.

Subject learning objectives (SLOs)

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

• Socially Responsible: FEIT graduates identify, engage, and influence stakeholders, and apply expert judgment establishing and managing constraints, conflicts and uncertainties within a hazards and risk framework to define system requirements and interactivity. (B.1)
• Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
• Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
• Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
• Reflective: FEIT graduates critically self-review their own and others' performance with a high level of responsibility to improve and practice competently for the benefit of professional practice and society. (F.1)

Teaching and learning strategies

This subject is a studio-based learning experience in which students assume the persona of a ‘reflective practitioner’ and go through an iterative learning process with feedback in the context of solving an interesting and significant real-world problem using AI technology for good. This Studio uses Agile methodology in managing the AI project.

This subject is delivered in a blended mode of self-paced learning/research and weekly face-2-face workshops. Students are expected to conduct substantial self-learning/research outside classes to bridge the knowledge and skill gaps required to produce a complete working solution for the chosen problem. They need to develop a problem statement based on their research and develop a complete satisfactory working solution in a small team using Agile technology. The students form a team with different roles and run a few sprints to propose, plan, design, develop and test the solution for their agreed business problem. The students are expected to follow the standard software development life cycle (SDLC) with focus on machine learning and MLOps.

During workshops, students will either work on the allocated project tasks, discuss with peers, or present and get feedback from facilitators. These workshops are a valuable source of verbal feedback and give students the opportunity to clarify their understanding and to discuss their research to gain deeper considerations to include in their projects.

Throughout the semester, students develop a portfolio for their learning journey in this subject through collaborative teamwork assisted by their project mentors/sponsors and give an oral defense at the end. The portfolio consists of a reflective journal and project artefacts. They need to use a self-chosen portfolio managing tool. In each week, students need to add an entry/page/section to their journal to describe what they have done and how their efforts contribute to development of the subject learning objectives using the incremental development of project artifacts as the evidence.

Students are assessed based on their portfolios and performance in oral defense.

Content (topics)

In this subject, students will apply MLOp pipelines, Agile Project Management (resource allocation, working in teams, planning, running meetings), and use their AI knowledge and skills to solve a real-world problem for good.

Assessment

Assessment task 1: Building your Portfolio through initializing the project

Assessment task 2: Building your Portfolio through developing proof of concept (POC)

Assessment task 3: Build your Portfolio through developing an interim solution

Assessment task 4: Build your Portfolio through developing the complete solution

Assessment task 5: Completed Portfolio and Presentation

Minimum requirements

To achieve a pass grade, students must meet the pass performance level stipulated in the subject’s specification standards.