Module manager: Prof. Samantha Pugh
Email: S.L.Pugh@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
This module is approved as a discovery module
This module introduces students to the fundamental concepts of Artificial Intelligence (AI), its ethical and legal implications, and its applications across scientific disciplines. Students will gain a broad understanding of how different AI systems work at a conceptual level, explore contemporary ethical and legal challenges surrounding AI use, and investigate real-world applications in science. The module emphasizes collaborative learning through a guided case study, culminating in a creative group presentation and a critical reflection on the responsible use of AI tools.
The objectives of this module are to gain:
An overview of the different types of AI and how they work at a conceptual level.
Awareness of the ethical issues relating to the use of AI.
An introductory knowledge of the legal aspects in the use of AI.
Knowledge of specific applications of AI to scientific fields, with the opportunity to investigate one area in depth.
Experience of working together on a case study and presenting findings in a creative way, using AI as appropriate.
On successful completion of the module students will be able to:
1) Answer questions on the different types of AI and how they work at a conceptual level.
2) Work effectively and responsibly with AI applications.
3) Answer questions on the ethical and legal use of AI.
4) Provide an example of an application of AI to science.
Skills Learning Outcomes
a) Work effectively in a group to deliver a creative output relating to AI.
b) Critically reflect on their use of AI for a particular purpose.
c) Develop digital and data literacy.
d) Undertake effective teamwork and communication.
e) Demonstrate creative problem-solving.
What is AI?
History and definitions of AI
Narrow AI vs General AI
Rule-based systems vs data-driven approaches
How AI Works:
Data, training, and models
Supervised and unsupervised learning
Introductory examples
Neural networks (conceptual overview only)
Generative AI and large language models
Strengths and limitations of AI systems
Ethics of AI
Bias, fairness, and transparency
Sustainability and environmental impacts
Data privacy and consent
Human oversight and accountability
Legal Frameworks for AI
Data protection
Intellectual property and AI-generated outputs
Overview of AI regulation
AI in Scientific Research
AI in physics, chemistry, biology, and environmental science
Automation, prediction, and discovery
Guest lecture or case examples
Case Study and Creative Communication with AI
Introduction of group case studies
Selecting a scientific application area
Guided research and planning sessions
Visualisation, storytelling, and presentation tools
Using AI to support (not replace) human creativity
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 10 | 1 | 10 |
| Seminar | 5 | 1 | 5 |
| Independent online learning hours | 10 | ||
| Private study hours | 75 | ||
| Total Contact hours | 15 | ||
| Total hours (100hr per 10 credits) | 100 | ||
75 hours of Private Study Time.
A mock exam will be provided.
Students will be given verbal formative feedback as they develop their case study ideas.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team