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Capstone Project (PRAI): Artificial Intelligence (PGT) - COM00197M

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  • Department: Computer Science
  • Credit value: 60 credits
  • Credit level: M
  • Academic year of delivery: 2024-25
    • See module specification for other years: 2023-24
  • Notes: This is an independent study module

Module summary

A core Capstone Project module (CPM) that consolidates and applies the knowledge and skills acquired in the MSc in Artificial Intelligence programme. The CPM allows students to choose a specialist topic within the field of AI and complete a research or engineering project with under expert supervision.

Module will run

Occurrence Teaching period
A Semester 2 2024-25 to Summer Semester 2024-25

Module aims

The aims of this module are to:

  • allow students to contribute to the advancement of knowledge in the field of Artifical Intelligence.

  • provide a culmination of taught material, in a substantial CPM.

  • support synthesis and application of material from the taught degree course.

  • demonstrate an appreciation of engineering and/or scientific methods and techniques, through coverage, as appropriate, of requirements, specification, design, implementation and evaluation (an engineering "lifecycle") for an implementation-oriented project, or a scientific lifecycle (hypothesis generation, experimental design, implementation and evaluation etc.) for a more experimentally-oriented project, or other appropriate systematic and rigorous approach depending on the problem.

Module learning outcomes

  • Acquire specialisation in a particular part of the subject area, including enhanced or new technical skills that build on taught theory. Examined in the overall project report.
  • Conduct an investigation in an area that involves some element of novelty or originality, and critically evaluate the results of the work. Examined in the development and evaluation sections of the project report.
  • Carry out research, critically engaging with research literature, and identify a clearly articulated line of enquiry. Examined in the literature review and evaluation sections of the project report.
  • Contribute to an established area of research or development, demonstrating understanding of how established techniques of research and enquiry are used to create and interpret knowledge. Examined in the overall project report.
  • Recognise alternative approaches, selecting and justifying the approach taken at each point in the report, identifying parts of the project area that are feasible within the time constraints of the project. Analyse limitations of work undertaken, and identify potential directions for future study.
  • Prepare a written report on the work done, according to the defined criteria (supplied separately), aiming for a standard that would be acceptable for wider publication.
  • Account for and reflect on appropriate legal, ethical, social, professional and commercial issues involved in the project. Document this in the project report.
  • Express questions for investigation in the field of Artificial Intelligence in a clear and concise way, providing information about the key motivations for investigating those questions.

Indicative assessment

Task % of module mark
Essay/coursework 100

Special assessment rules

Non-compensatable

Additional assessment information

Please note, as per University of York assessment regulations (see the Rules for progression and award), it is only possible to resit PGT Capstone Project Modules (CPMs) under certain conditions. In the case of a marginal fail of the CPM (marks that fall within 40-49), reassessment is permitted. Students are given the opportunity to make amendments to enable them to reach a pass threshold, within a specified time frame. The mark for the resubmitted CPM will be capped at the pass mark (50). There will only be one such reassessment.

Indicative reassessment

Task % of module mark
Essay/coursework 100

Module feedback

Feedback on project draft (when submitted to supervisor in a timely manner).
Written feedback after written project report submitted.

Indicative reading

  1. Dawson, C. W. (2015) Projects in Computing and Information Systems. 3rd edn. Addison-Wesley

  2. Gowers, E. (2015) Plain Words. Penguin

  3. Kopka, H. and Daly, P.W. (1999) A guide to LaTeX: Document Preparation for Beginners and Advanced Users, 3rd edn. Addison-Wesley

  4. Zobel, J. (2015) Writing for Computer Science, 3rd edn. Springer


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.