Module code: EEEM075

Module Overview

Recently, Artificial Intelligence (AI) has been playing a key role in the research and development of scientific and technological breakthroughs in many disciplines to solve real world problems, providing new foundations and stepping stones to foster more advances and solutions. In this context, AI has a great potential to play a transformative role in helping to achieve the United Nations Sustainable Development Goals (UNSDG), by providing new insights, enabling more efficient use of resources, and supporting a better understanding of complex systems that underpin the dynamics of people’s lives and the planet’s environment. Therefore, the purpose of this module is to present the key concepts with practical applications related to the development of more sustainable AI techniques (e.g. model, data and energy efficiency, bias and unfairness identification and mitigation, trustworthy AI, physics-informed neural networks), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing).

Module provider

SOL - Computer Science and Elec Eng

Module Leader


Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 93

Tutorial Hours: 4

Guided Learning: 42

Captured Content: 11

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

  • Introduction to AI and Sustainability.

  • AI core concepts recap.

  • AI for time series processing.

  • Trustworthy AI.

  • Model and data efficiency.

  • Physics-Informed Neural Networks.

  • Advanced topics in AI and Sustainability.

  • Practical applications in AI and Sustainability.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Coursework 100

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to:
The assessment strategy is designed to provide students with the opportunity to demonstrate:

  • Ability to understand and implement the key concepts and core fundamental knowledge in sustainability of AI and AI for sustainability.

  • Ability to understand the needs required to build appropriate solutions for a range of sustainability challenges and problems using AI techniques, either in the sustainability of AI or AI for sustainability.

  • Ability to develop, evaluate, experiment, demonstrate and apply appropriate AI techniques to tackle real-world sustainability challenges, generating suitable and defensible results.

Thus, the summative assessment for this module consists of:

  • Coursework: a report along with the corresponding code developed to solve a real-world problem either in the sustainability of AI or AI for sustainability (100% weighting). This addresses all learning outcomes.

Formative assessment:
For the module, students will receive formative assessment/feedback in the following ways.

  • By means of unassessed tutorial problems (with answers/model solutions).

  • During tutorial Q&A sessions.

  • Via the marking of the assignment, both electronic file submissions, codes and written report.

Students will engage in weekly captured content and guided materials which will be complemented by weekly practical tasks through self-guided lab exercises. The corresponding solutions will provide feedback on understanding and practicing. Captured content, guided learning activities, tutorials, independent learning, and feedback will then support the coursework. Individual feedback on the coursework will be given to the student.

Module aims

  • This module aims to provide an understanding of what AI and Sustainability means ¿ i.e. the aspects of the sustainability of AI, and AI for sustainability, time series processing with AI, trustworthy AI, model and data efficiency techniques, physics-informed neural networks, along with some advanced topics in AI that play a key role in the sustainable development, such as foundation models and diffusion models applied to sustainability challenges.

Learning outcomes

Attributes Developed
001 Develop an understanding of the concepts, complementarities and differences between the sustainability of AI, and AI for sustainability. KCT
002 Apply advanced techniques that aim to develop more sustainable AI solutions employing time series processing, data-driven and physic-informed approaches. KPT
003 Develop state-of-the-art AI solutions that can support in achieving the UNSDGs. PT
004 Apply advanced techniques that aim to develop sustainable development employing efficient and trustworthy AI approaches. KPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:
This module aims to present an introductory course on the core concepts, background theory and key fundamental aspects of the sustainability of AI and AI for sustainability – i.e. AI and Sustainability – through captured content and guided-learning activities designed to deliver modern theoretical content, along with self-guided lab activities to present to the student practical applications of the concepts covered. Live tutorials will provide an opportunity to clarify the students understanding of key concepts and answer their questions. The self-guided labs will be designed in Python, aiming at providing further technical depth of the content delivered to the students, with practical examples of real-world situations where students would need to apply these concepts.
The learning and teaching methods include:

  • Captured content.

  • Self-guided labs.

  • Tutorial Q&A sessions.

  • Designed real-world problems to be used as case studies during learning activities.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: EEEM075

Other information

We are committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Digital capabilities: The advanced topics in AI and Sustainability taught in this module will provide students skills in coding and other computer science techniques that are fundamental in developing and deploying more sustainable AI solutions and solving sustainability challenges using AI techniques. 
Employability: This module provides transferable and underpinning skills in advanced programming, AI, sustainability, software and data processing that are important in solving many real-world problems in AI and sustainability. This includes skills in developing AI solutions that are environmentally and socially sustainable, and that aim to solve sustainability challenges that contribute to achieving UNSDGs. Those skills are highly appreciated by employers in many disciplines of knowledge, which are even more important for those companies working towards achieving Industry 5.0 and Society 5.0 standards. 
Global and cultural capabilities: AI and sustainability are global challenges that are of great interest to industries, companies, governments and societies worldwide. This module provides a unique opportunity to delve into the intersection of these two areas and develop hard and soft skills that can be used not only regionally but also globally, contributing to raise awareness on the importance of developing more sustainable AI solutions, that can also be applied to solve sustainability problems.
Resourcefulness and Resilience: This module allows students to develop skills in methods they have learned in through the different activities, so that they can be ready to reason about and solve new unseen real-world problems in AI and sustainability.
Sustainability: This module is especially linked to Sustainability as it is focused on bringing the core knowledge and developing the skills necessary to develop more sustainable AI solutions, and how to develop them to solve sustainability challenges to achieve UNSDGs.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
People-Centred Artificial Intelligence (Online) MSc 2 Compulsory A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.