Artificial Intelligence Capstone Project Course Descriptor

Course Summary

This course is an artificial intelligence (AI) capstone project, conceived and executed by the learner in an external organisation. The project will demonstrate a professional level of technical and analytical skill, aligned to achieving organisational goals and enabling effective institutional change. The learner will use the scientific method to formulate the AI research/business question, design the experiment(s) and test hypotheses. The project may be:

• An idea or opportunity to use AI or new developments in the AI/machine learning field in the business.
• A specific business problem to be addressed using AI.
• A recurring issue in AI.

The project will culminate with a written dissertation and a viva voce exam.

Course Aims

• Give learners the opportunity to carry out an independent research project in artificial intelligence aligned to a business problem.
• Train learners to write up their findings and ideas accurately, clearly, coherently and to a high-professional standard.
• Train learners to present their own arguments logically and competently, to engage specialist and non-specialist stakeholders.

Learning Outcomes

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

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

The contact hours on this course are formed predominantly of supervisory meetings, typically 6 x 1 hour. 

Learners are expected to carry out independent research into the topic. 

Readings should include a mix of books, journal articles, policy papers and other relevant documents, depending on the topic and the approach taken in the dissertation. 

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

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

The course learning and teaching hours will be structured as follows:

• Off-the-job learning (12 days x 7 hours) = 84 hours (e.g. 1 day per week for 12 weeks)
• On-the-job learning (24 days x 7 hours) = 168 hours (e.g. 2 days per week for 12  weeks)
• Private study = 48 hours (e.g. 4 hours per week for 12 weeks)
• Total 300 hours

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Regular tri-partite reviews between the learner (apprentice), their apprenticeship advisor (provider) and workplace line manager (employer) formally monitor and evaluate the learner’s progress.

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.

Books

Preece, R., 1994. Starting Research: An Introduction to Academic Research and Dissertation Writing. London, New York: Pinter Publishers

Stephan F. M., and Smith, I., 2019. A Practical Guide to Dissertation and Thesis Writing. Newcastle upon Tyne, England: Cambridge Scholars Publishing 

Dubber, M., Pasquale, F., and Das, S., 2020. The Oxford Handbook of Ethics of AI. New York, New Jersey: Oxford University Press

Journals

Learners are encouraged to read material from relevant journals on postgraduate dissertations and artificial intelligence as directed by their Course Leader.

Electronic Resources

Learners are encouraged to consult relevant websites on postgraduate dissertations and artificial intelligence.

Indicative Topics

Learners will study the following topics: 

• Artificial intelligence solutions
• Business context
• Scientific method