This is a belated post on developing a new BSc AI and Data Science (Hons) programme. This programme has successfully passed validation in early 2022 and we are now accepting applications for the 22/23 academic year.

The development of the new programme is an answer to the growing demand for machine learning engineers and scientists in the UK job market. Using AI and machine learning to increase productivity, save cost, and assist new designs is no longer a privilege for large tech companies and government organisations. In the past few years, we have worked with many small and micro-businesses that are enthusiastic about adopting AI techniques and recruiting AI talents. Although we have been teaching AI-related topics such as computer vision, deep learning and graph databases within our existing programmes for many years, it is now imperative to design a dedicated BSc programme to capture recent advancements in AI as well as the legal, ethical, and environmental challenges that may follow. I am pleased to have the chance to be part of this development as the programme lead.

We had two parallel procedures taking place: Computing market research and CAIeRO Planner. The market research was carried out by key academics who are currently teaching AI-related modules. We did a few case studies of similar programmes offered by our main competitors and current job vacancies for ML engineers, researchers, and data analysts. We noticed that a lot of AI programmes are offered as a collection of discrete data science and machine learning modules that don’t synergise with each other. While this setting may give prospective students the impression of a rich and sophisticated course, students do not get the best value while hopping between those modules. We wanted to follow the theme of responsible and human-centred AI while providing a clear path to success and a sense of accomplishment along the way. The research on the job market was especially important because we wanted to continuously champion hands-on learning and practical skills. This practice gave us a general idea of the toolset, frameworks, workflow, and R&D environment that our students will be expected to master in their future workplace.

Planning on the technical content is only half of the story. The University has a large and dedicated Learning Technology team to support any activities on the module and programme development and improvement. We had two learning technologists assigned to our programme to support detailed designs at both programme and module levels. We used an in-house planner Creating Aligned Interactive educational Resource Opportunities (CAIeRO) to guide the exercises.

We started with the “look and feel”, learning outcomes, mission statement and assessment strategy for the programme as a whole using interactive tools and sharable environments such as padlet. All members of the programme team had equal inputs to the design. The whole process was carried out through multiple online sessions over a few weeks. Because everyone came to the meeting fully prepared, the sessions were really effective and super engaging. The programme level design then became the blueprint for module-level designs to ensure coherence and consistency across all modules.

We then identified four new modules for the programme: Mathematics for Computer Science, Introduction to AI, Natural Language Processing, and Cloud Computing and Big Data. We also reworked some existing modules such as Advanced AI and Applications, and Media Technology to better accommodate the programme learning outcomes.

Developing module-level learning outcomes can be challenging, especially when we need to maintain the coherence between modules at the same level. As student-facing documents, the module specifications also need to be clear and concise. We used a toolkit called COGS which stands for Changemaker Outcomes for Graduate Success. It includes a series of guidelines that help staff write clear and robust learning outcomes that are appropriate to the academic level of study in order to clarify for students what is expected of them across the different stages of their study. I found this tool extremely useful when I developed the new modules, knowing that my colleagues would be using similar languages for the related modules.

We also took a few extra steps to make sure that the learning outcomes will be assessed using a range of tools including assignment, project, time-constrained assessment and dissertation. Most modules also offer a mix of face-to-face and a small number of online contact hours for active and blended learning. This will allow students to work on subject tasks online before they join the classes, a practice that could greatly improve student engagement.

If you are interested in more details about our programme, please don’t hesitate to contact me.