Module Specification

AI in Public Service

Module Specification

AI in Public Service: Making AI work for people with confidence and integrity



This course equips you to navigate the transformative role of AI in public service, blending foundational knowledge, practical applications, and ethical considerations. You’ll gain a deep understanding of AI’s capabilities, limitations, and its potential to revolutionize governance and public service delivery.

You’ll explore how AI has evolved, its core concepts, and its current technological landscape. Learn to design workflows that balance AI efficiency with human oversight, leveraging its strengths while managing risks. Through real-world case studies, you’ll discover how AI enhances equity, efficiency, and accessibility across public sectors.

The course also addresses critical ethical and societal issues, such as bias, fairness, transparency, privacy, and workforce transformation. You’ll understand the regulatory frameworks that guide responsible AI use, enabling you to make informed decisions that balance innovation with accountability.

By combining theory, practice, and ethical guidance, this course prepares you to harness AI effectively and responsibly, empowering you to drive meaningful improvements and navigate AI’s impact on public services with confidence.



Prerequisites and Co-requisites

None

Learning Outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Demonstrate comprehensive understanding of AI principles and identify ethical considerations in AI applications.
LO2 Intellectual Skills Analyse diverse evidence to assess how data influences AI transparency and decision-making.
LO3 Intellectual Skills Critique existing and proposed AI systems using ethical frameworks, to ensure fairness across diverse populations.
LO4 Technical/Practical Skills Coordinate AI-based projects with a focus on privacy compliance and stakeholder inclusivity.
LO5 Technical/Practical Skills Use AI systems in complementary contexts, ensuring ethical compliance.
LO6 Professional/Transferable Skills Communicate the ethical use of AI to diverse audiences.

Content Structure

Week Topic
Week 1 Introductory lecture
Introduces the module, outlining its relevance to the field and connections to other topics. It provides an overview of the content structure, key references, and assessment details.
Week 2 Foundations of AI
Explore the fundamental principles of AI, its core concepts, applications, evolution and historical context.
Week 3 AI Capabilities and Limits (2024)
Attain a grounded view of AI's real-world capabilities and boundaries. Gain insights into the current state of AI technology, examining its strengths, limitations, and areas where it either falls short of, matches, or surpasses human intelligence. Explore which tasks AI can handle autonomously, which require human oversight, and which remain beyond its reach.
Week 4 Human-AI Synergy
Understand how workflows should be designed to balance AI efficiency with human oversight to mitigate risks. Explore smart escalation methods for managing routine tasks with quality assurance through human review. Discover AI's potential in ideation and brainstorming while recognising its creative limitations.
Week 5 Enhancing Public Services with AI
Explore how AI can improve governance and empower the citizens. Learn how AI can simplify access, personalise support, ensure fairness, promote equity in public services, boost efficiency, scale delivery, and drive continuous improvement.
Week 6 AI Case-studies and Opportunities
Discover how to leverage AI to enhance public sector efficiency and impact. This chapter is packed with practical examples, revealing AI's potential to drive meaningful improvements in public services. Explore case studies of successful AI applications across public administration, healthcare, education, and more, along with insights into future possibilities.
Week 7 AI Ethics, Regulation, and Societal Impacts
Explore AI's ethical challenges, including bias, fairness, transparency, and privacy, within the context of evolving regulatory frameworks. Examine how laws and standards guide responsible AI deployment in public service, balancing innovation with accountability. Understand AI's societal impacts, particularly workforce transformation, addressing job displacement, reskilling, and new opportunities to ensure equity and inclusion.

Student Workload

The methods of teaching and learning for this module are based on the School's Short-course 5 teaching system, consisting of the following activities.

Activity
Introductory lecture
Concept learning (knowledge graph)
AI formative assessment
Case Study Review
Independent reading, exploration and practice
Total:

Teaching and Learning Methods

Activity Description
Introductory lecture

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.

Concept learning (knowledge graph)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.

AI formative assessment

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.

Case Study Review

In this learning activity, students explore recent real-world case studies relevant to their course topic. The case studies will have been selected and curated by the module leader to represent up-to-date examples. They guide students through key details, contextual factors, and outcomes. This approach enhances students' understanding of current industry trends, challenges, and solutions, preparing them for real-world scenarios they may encounter in their future careers.

The learning experienced will be augmented by AI (virtual private tutor) allowing the students to critically engage with the content and discuss the case studies.

Independent reading, exploration and practice

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.


Module Approval

Stage Version Date of approval Authority Chair Revalidation
Compliance 1.0 Academic Board Dr Paresh Kathrani
Pre-Teaching 1.0 Director of Education Dr Paresh Kathrani
Note: The information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: AI in Public Service: Making AI work for people with confidence and integrity (AI16)