Unit information: Practice-Oriented AI Summer 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 Practice-Oriented AI Summer Project
Unit code COMSM0150
Credit points 60
Level of study M/7
Teaching block(s) Academic Year (weeks 1 - 52)
Unit director Dr. Ray
Open unit status Not open
Units you must take before you take this one (pre-requisite units)

None

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

None

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 Summer Project is the first deliverable of a PrO-AI student’s PhD. The main purpose of this initial phase is to compile the literature review and analyse the feasibility, social impact and any ethical issues. It will deliver a small proof-of-principle implementation and also a report for formal assessment, which includes the outline plan of the subsequent PhD research.

How does this unit fit into your programme of study?

The unit runs over the Summer months (June-July-August). Prior to this the students will have prepared a synopsis for the Summer Project, outlining background, objectives, a timeline and deliverables. During the Summer months the student works full-time on the Summer project. Two days are set aside for all Foundation Year students to present their interim and final Summer Project work to the entire CDT.

Your learning on this unit

An overview of content

Summer Projects are self-contained research projects relevant to Practice-Oriented AI. The research concerns a specific problem in an application area in science and research for which an AI-based solution is designed, implemented and evaluated.

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

After completing the Summer Project the student will be in a much better position to outline the broad topic of their PhD in relation to the state of the art, and articulate the next steps in their PhD study.

Learning Outcomes

Upon successful completion of this unit students will be able to:

  1. Work independently on a practice-oriented AI project for which they have defined the objectives and rationale.
  2. Apply knowledge about an area to a specific problem, which may be engineering, analytical, academic or applied in nature.
  3. Effectively communicate their conclusions in terms of their motivation, methodology, results and relation existing work.

How you will learn

Training will be delivered though regular supervision meetings involving discussion of progress, technical advice, and guidance. These will be supplemented by a combination of synchronous and asynchronous cohort-based sessions to provide the key organisational and practical skills required for the project.

How you will be assessed

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

As part of the Research Orientation module the student prepares a synopsis for the Summer Project, outlining background, objectives, a timeline and deliverables. This will receive feedback from the proposed supervisor and one other academic.

At the halfway point of the Summer Project all students give short progress presentations to the cohort, supervisors and mentors. This is another opportunity for formative feedback.

Tasks which count towards your unit mark (summative):

The student will submit a project report of about 15,000 words (30 pages). This will be assessed on a pass/fail basis by the supervisor and another academic in terms of scholarly content, academic writing, and adherence to the project synopsis. Narrative feedback is provided, indicating strong points as well as areas for improvement.

When assessment does not go to plan

In case a fail mark is attained the narrative feedback will indicate what further work is needed before the student demonstrates to be ready for subsequent PhD research.

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. COMSM0150).

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.