Unit information: Individual AI Project in 2027/28

Please note: Programme and unit information may change as the relevant academic field develops. We may also make changes to the structure of programmes and assessments to improve the student experience.

Unit name Individual AI Project
Unit code COMS30092
Credit points 40
Level of study H/6
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Seth Bullock
Open unit status Not open
Units you must take before you take this one (pre-requisite units)

Completion of Years 1 and 2 of the Computer Science with AI Undergraduate degree.

Units you must take alongside this one (co-requisite units)

Individual Project (Teaching Unit)

Units you may not take alongside this one

None.

School/department School of Computer Science
Faculty Faculty of Engineering

Unit Information

Why is this unit important?

The final year project acts as a focus for the accumulated skills resulting from all other units: the overarching goal is application of those skills to a specific, significant AI challenge or problem.

How does this unit fit into your programme of study

This unit is the capstone of the student’s degree, allowing them to use the skills they have gained across the programme so far.

Your learning on this unit

An overview of content

The unit offers a high degree of freedom with respect to the project’s AI topic, and allows students to spend a significant amount of time and effort on an area of AI they are specifically interested in. Ideally this might act as a bridge to a career in such a topic, but will also satisfy more general, transferable learning outcomes.

How will students, personally, be different as a result of the unit

Students will be able to demonstrate that they can successfully complete and present a significant piece of Computer Science project work. This will be different for every student, as they will have chosen their own areas to work on.

Learning Outcomes

On successful completion of this unit, students will be able to:

  1. Read and understand research-level material.
  2. Identify a well-motivated, scientifically interesting AI challenge, and decide suitable objectives.
  3. Engage in a suitable approach to solving said challenge (e.g., developing a proof, analysing an algorithm, implementing a system) and critically evaluate their solution in a suitable manner.
  4. Present results in written and verbal form.
  5. Identify any ethical issues that arise in their work, data collection or processing in order to seek expert guidance.

How you will learn

Optional workshops and tutorials with the Project Supervisor. Compulsory training on identifying ethical issues associated to the project work will be organised by the Unit Director.

How you will be assessed

Tasks which help you learn and prepare you for summative tasks (formative):

Students will have regular meetings with their academic supervisor throughout Teaching Block 2, who will give them verbal feedback on their work. This will be different for every student, as their projects can cover a wide range of topics. Students will also have a separate opportunity before submission to present their project to an audience including their second marker, in order to get feedback.

Tasks which count towards your unit mark (summative):

Written Dissertation (100%) to assess Learning Outcomes 1 through 5, submitted at the end of Teaching Block 2

The final Dissertation mark is a single mark determined by a panel for the submitted dissertation. There is an interactive presentation to authenticate the work and provide opportunities for clarification. The project’s codebase may also be considered, where applicable. In addition to achieving a pass overall, students must complete the ‘must-do’ ethics training in order to be awarded credit points for the unit. This training takes the form of an online introduction and test (e.g. via Blackboard) with immediate feedback and unlimited answer adjustments until sufficient proficiency is demonstrated.

When assessment does not go to plan

Students who fail this unit retake the assessment in a like-for-like fashion. Students will be expected to resubmit their dissertation and undertake a project presentation.

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMS30092).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the University Workload statement relating to this unit for more information.

Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates within the Regulations and Code of Practice for Taught Programmes.