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Academic Handbook Postgraduate Programmes

Artificial Intelligence Dissertation Project Course Descriptor

Course Code LDSCI7237 Discipline Data Science
Credit Points 60 Teaching Period Any
FHEQ Level 7 Date Approved June 2023
Core Attributes None
Pre-requisites None
Co-requisites None

Course Summary

This course provides students with the opportunity to complete an MSc dissertation project addressing a substantial, real-world problem. The project can cover a wide spectrum of topics taught in the programme: from ethical and philosophical issues surrounding AI and data science, to applied machine learning, to developing a software artefact (e.g., a software library). All projects, however, must have an element of data, AI, software, and a “human face”, building upon the variety of material being taught during the programme. There is a particular focus on sustainable development in terms of resource efficiency as well as societal, economic, and environmental impact.

The project may also be interdisciplinary in nature, for example, solving a problem in digital humanities or computational social sciences using data analytics and machine learning. Such interdisciplinary projects get assigned two supervisors: (i) an expert from the humanities discipline who will guide students to solve a non-trivial problem; and (ii) an expert in computer or data science who will guide students to develop a non-trivial solution.

After an initial group seminar with the course leader, students meet with their assigned supervisor(s) to finalise the subject of their project and discuss and refine its requirements. Once the dissertation has been submitted, students defend it in a 30-minute presentation and demonstration.

Course Aims

The aims of the course are:

  • Extend students’ ability to devise original solutions for a particular problem of their choice.
  • Extend students’ ability to organise and manage a project from start to finish.
  • Extend students’ ability to present clearly their ideas, choices and evaluation methodology to their peers.
  • Prepare students for a wide range of careers and roles in society.

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

K1d Identify, analyse, and interpret requirements to solve a problem rigorously (e.g., formulate a thesis statement, identify steps to prove it, and substantiate your findings with data. ).
K2d Demonstrate detailed critical engagement with methods, tools and technologies required to solve a problem (e.g., philosophical devices or software libraries).
K3d Demonstrate a sophisticated understanding of (qualitative or quantitative) data analysis principles, tools and techniques.
K4d Critical review of related work, identifying key developments in a particular area, opportunities for integration, limitations and avenues for further development and innovation.

Subject Specific Skills

S1d Ability to engage in a peer review process that involves critical review of ideas, arguments, software and related documentation. , coupled with positive actionsadvice for improvement and innovation.
S3d Develop original arguments based on solid background work and coupled with positive actions for improvement and innovation.
S2d Ability to recognise the individual components required to solve a problem or answer a question and combine them into a coherent argument or solution.
S4d Familiarity with codes of ethics and codes of practice that underpin the development of high quality, high integrity research projects.

Transferable and Professional Skills

T1d Project leadership skills, from understanding a problem to proposing a solution based on sound insights, to encouraging others to share that vision.
T2d Consistently display an excellent level of technical proficiency in written English and command of scholarly terminology, so as to be able to deal with complex issues in a sophisticated and systematic way.
T3d Research and analytical skills with a range of up-to-date, well-proven tools and resources.
T4d Communicate effectively the intellectual merit and broader impacts of the project to specialist and non-specialist audiences.

Teaching and Learning

Teaching and learning strategies for this course will include: 

  • Group seminars
  • Independent (though guided) study and research
  • Individual supervision, which supports both writing and oral communication skills
  • Individual written feedback
  • Online discussion forum

Course information and supplementary materials are available on the University’s Virtual Learning Environment (VLE).

Students are required to attend and participate in all the formal and timetabled sessions for this course. Students are also expected to manage their directed learning and independent study in support of the course.

Employability Skills

The individual dissertation project cultivates the following employability skills: 

  • Research skills: gather requirements, ideas and material and combine them to propose a novel solution; conduct research and explore relevant existing works; solve problems through logical reasoning and rigorous testing.
  • Leadership skills: work independently, creatively and to deadlines; and engage in collaborative and constructive discussions with peers.
  • Communication skills: communicate findings in a clear, structured manner both orally, via presentation and demonstration, and in writing, via technical documentation.



Students will be formatively assessed during the course as they produce successive drafts of their dissertation and, if applicable, software artefact(s). These do not count towards the end of year results, but will provide students with developmental feedback, both written and oral. 


Assessment will be in three forms:

AE: Assessment Activity Weighting (%) Online submission Duration Length
1 Project Proposal 20% Yes N/A Up to 5,000 words
2 Dissertation 60% N/A N/A Up to 10,000 words
3 Oral Assessment and Presentation 20% N/A 30 minutes N/A

The project’s dissertation, any accompanying artefacts and the presentation will be assessed in accordance with the assessment aims set out in the Programme Specification.


Students will receive formal feedback in a variety of ways: written (in comments on draft material, including via email correspondence); oral (within one-to-one supervision tutorials and on an ad hoc basis).

Indicative Reading

Reading is to be decided upon between student and supervisor, depending on the topic of the chosen dissertation project.

Indicative Topics

The topics covered by students will vary across projects, but typically a student will encounter (one or more times) the following topics during the project:

  • Problem statement definition
  • Independent development of a solution or hypothesis
  • Defending proposed solution or hypothesis with supporting evidence
  • Writing up
  • Submission
  • Presentation, or demonstration, or both
Title: LDSCI7237 Artificial Intelligence Dissertation Project Course Descriptor

Approved by: Academic Board

Location: Academic Handbook/Programme specifications and Handbooks/ Postgraduate Programme Specifications/

Version number Date approved Date published Owner Proposed next review date Modification (As per AQF4) & category number
1.0 January 2021 March 2021 Alexandros Koliousis January 2026  
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